Original: Adam Can Converge With Proper β 1 and β
Revised: Adam Can Converge With Proper β 1 and β

Original: Abstract
Revised: Abstract

Original: Ever since Reddi et al.
Revised: Ever since Reddi et al.

Original: (2018) pointed out the divergence issue of Adam, many new variantshave been designed to obtain convergence.
Revised: (2018) pointed out the divergence issue of Adam, many new variantshave been designed to obtain convergence.

Original: However, vanilla Adam remains exceptionallypopular and it works well after proper tuning.
Revised: However, vanilla Adam remains exceptionallypopular and it works well after proper tuning.

Original: Why is the divergence not observed?
Revised: Why is the divergence not observed?

Original: Wepoint out there is a gap between theory and practice: Reddi et al.
Revised: Wepoint out there is a gap between theory and practice: Reddi et al.

Original: (2018) pick the samplesize n after picking the hyperparameters of Adam, i.e., ( β 1 , β 2 ) ; while practical applicationsoften fix n first and then tune ( β 1 , β 2 ) .
Revised: (2018) pick the samplesize n after picking the hyperparameters of Adam, i.e., ( β 1 , β 2 ) ; while practical applicationsoften fix n first and then tune ( β 1 , β 2 ) .

Original: Since Adam often works well in the latter setting, weconjecture it can still converge.
Revised: Since Adam often works well in the latter setting, weconjecture it can still converge.

Original: In this work, we confirm this conjecture under fixed n .
Revised: In this work, we confirm this conjecture under fixed n .

Original: Weprove that, when the 2nd-order momentum parameter β 2 is large and 1st-order momentumparameter β 1 < √ β 2 < 1 , Adam converges to the neighborhood of stationary points.
Revised: Weprove that, when the 2nd-order momentum parameter β 2 is large and 1st-order momentumparameter β 1 < √ β 2 < 1 , Adam converges to the neighborhood of critical points.

Original: The sizeof the neighborhood is propositional to the variance of stochastic gradients.
Revised: The sizeof the neighborhood is propositional to the variance of stochastic gradients.

Original: Under an extracondition (strong growth condition), Adam converges to stationary points.
Revised: Under an extracondition (strong growth condition), Adam converges to critical points.

Original: As β 2 increases,our convergence result can cover any β 1 ∈ [0 , 1) including the default setting β 1 = 0 .
Revised: As β 2 increases, ourconvergence result can cover any β 1 ∈ [0 , 1) including the default setting β 1 = 0 .

Original: Toour best knowledge, we are the first to prove that Adam with arbitrarily large β 1 can convergewithout any form of bounded gradient assumption.
Revised: To ourbest knowledge, we are the first to prove that Adam with arbitrarily large β 1 can convergewithout any form of bounded gradient assumption.

Original: This result shows that vanilla Adam,without any modification, is still theoretically justified to converge.
Revised: This result shows that vanilla Adam,without any modification, is still theoretically justified to converge.

Original: When β 2 is small, wefurther point out a large region of ( β 1 , β 2 ) combinations where Adam can diverge to infinity.
Revised: When β 2 is small, wefurther point out a large region of ( β 1 , β 2 ) combinations where Adam can diverge to infinity.

Original: Our divergence result considers the same setting (fixing n ahead) as our convergence result,indicating that there is a phase transition from divergence to convergence when increasing β 2 .
Revised: Our divergence result considers the same setting (fixing n ahead) as our convergence result,indicating that there is a phase transition from divergence to convergence when increasing β 2 .

Original: These results may provide guidance towards better hyperparameter tuning of Adam.
Revised: These results may provide guidance towards better hyperparameter tuning of Adam.

Original:  Introduction
Revised:  Introduction

Original: Modern machine learning tasks often aim to solve the following finite-sum problem.
Revised: Modern machine learning tasks often aim to solve the following finite-sum problem.

Original: [Equation]
Revised: [Equation]

Original: where n is the number of samples or mini-batches and x denotes the trainable parameters.
Revised: where n is the number of samples or mini-batches and x denotes the trainable parameters.

Original: In deeplearning, Adam (Kingma & Ba, 2014) is one of the most popular algorithms for solving (1).
Revised: In deeplearning, Adam (Kingma & Ba, 2014) is one of the most popular algorithms for solving (1).

Original: Ithas been applied to various machine learning domains such as natural language processing (NLP)(Vaswani et al., 2017; Brown et al., 2020;
Revised: Ithas been applied to various machine learning domains such as natural language processing (NLP)(Vaswani et al., 2017; Brown et al., 2020;

Original: Devlin et al., 2018), generative adversarial networks (GANs)(Radford et al., 2015; Isola et al., 2017; Zhu et al., 2017) and computer vision (CV) (Dosovitskiyet al., 2021).
Revised: Devlin et al., 2018), generative adversarial networks (GANs)(Radford et al., 2015; Isola et al., 2017; Zhu et al., 2017) and computer vision (CV) (Dosovitskiyet al., 2021).

Original: Despite its prevalence, Reddi et al.
Revised: Despite its prevalence, Reddi et al.

Original: (2018) point out that Adam can diverge with a widerange of hyperparameters.
Revised: (2018) point out that Adam can diverge with a widerange of hyperparameters.

Original: A main result in (Reddi et al., 2018) states that 1 :
Revised: A main result in (Reddi et al., 2018) states that 1 :

Original: For any β 1 , β 2 s.t. 

Revised: For any β 1 , β 2 s.t.

Original: ≤ β 1 < √ β 2 < 1 , there exists a problem such that Adam diverges.
Revised: ≤ β 1 < √ β 2 < 1 , there exists a problem such that Adam diverges.

Original: Here, β 1 and β 2 are the hyperparameter to control Adam’s 1st-order and 2nd-order momentum.
Revised: Here, β 1 and β 2 are the hyperparameter to control Adam’s 1st-order and 2nd-order momentum.

Original: More description of Adam can be seen in Algorithm 1 (presented later in Section 2.1).
Revised: More description of Adam can be seen in Algorithm 1 (presented later in Section 2.1).

Original: Ever since(Reddi et al., 2018) pointed out the divergence issue, many new variants have been designed to obtainconvergence.
Revised: Ever since(Reddi et al., 2018) pointed out the divergence issue, many new variants have been designed.

Original: For instance, (Zou et al., 2019) changed β 2 to iteration-dependent β 2 t ; AMSGrad
Revised: Forinstance, AMSGrad (Reddi et al., 2018) enforced the adaptor v t (defined later in Algorithm 1) to

Original: Appendix D.2.
Revised: Appendix D.2.

Original: (Reddi et al., 2018) enforced the adaptor v t (defined later in Algorithm 1) to be non-decreasing;

Original:  AdaBound (Luo et al., 2019) imposed constraint v t ∈ [ C l , C u ] to ensure the boundedness on effectivestepsize.
Revised: be non-decreasing; AdaBound (Luo et al., 2019) imposed constraint v t ∈ [ C l , C u ] to ensure theboundedness on effective stepsize.

Original: These variants often reach good performance in practice.
Revised: Although these variants can provably obtain convergence, theyoften introduce new issues.

Revised: For instance, AMSGrad is often reported to slow down the trainingprocess (Zhou et al., 2018b); AdaBound introduces two extra hyperparameters C l and C u .

Original: On the other hand, counter-intuitively, vanilla Adam remains exceptionally popular (see evidenceat (Scholar)).
Revised: On the other hand, counter-intuitively, vanilla Adam remains exceptionally popular (see evidenceat (Scholar)).

Original: Without any modification on its update rules, Adam works well after proper tuning.
Revised: Without any modification on its update rules, Adam works well after proper tuning.

Original: Even more mysteriously, we find that the commonly reported hyperparameters actually satisfy thedivergence condition stated earlier.
Revised: Even more mysteriously, we find that the commonly reported hyperparameters actually satisfythe divergence condition stated earlier.

Original: For instance, Kingma & Ba (2014) claimed that ( β 1 , β 2 ) =(0 .
Revised: For instance, Kingma & Ba (2014) claimed that ( β 1 , β 2 ) =(0 .

Original: 9 , 0 . 999) is a “good choice for the tested machine learning problems" and it is indeed the defaultsetting in PyTorch.
Revised: 9 , 0 . 999) is a “good choice for the tested machine learning problems" and it is indeed the defaultsetting in PyTorch.

Original: In super-large models GPT-3 and Megatron (Brown et al., 2020; Smith et al.,2022), ( β 1 , β 2 ) is chosen to be (0 . 9 , 0 . 95) .
Revised: In super-large models GPT-3 and Megatron (Brown et al., 2020; Smith et al.,2022), ( β 1 , β 2 ) is chosen to be (0 . 9 , 0 . 95) .

Original: GAN researchers (e.g. Radford et al.
Revised: GAN researchers (e.g. Radford et al.

Original: ; Isolaet al.
Revised: ; Isolaet al.

Original: (2017)) use ( β 1 , β 2 ) =
Revised: (2017)) use ( β 1 , β 2 ) =

Original: All these hyperparameters live in the divergence regionβ 1 < √ β 2 .
Revised: All these hyperparameters live in the divergence regionβ 1 < √ β 2 .

Original: Surprisingly, instead of observing the divergence issue, these hyperparameters achievegood performances
Revised: Surprisingly, instead of observing the divergence issue, these hyperparameters achievegood performances

Original: and they actually show the sign of convergence.
Revised: and they actually show the sign of convergence.

Original: Why does Adam work well despite its theoretical divergence issue?
Revised: Why does Adam work well despite its theoretical divergence issue?

Original: Is there any mismatch betweendeep learning problems and the divergent example?
Revised: Is there any mismatch betweendeep learning problems and the divergent example?

Original: We take a closer look into the divergenceexample and find out the mismatch does exist.
Revised: We take a closer look into the divergenceexample and find out the mismatch does exist.

Original: In particular, we notice an important (but oftenignored) characteristic of the divergence example: (Reddi et al., 2018) picks ( β 1 , β 2 ) before pickingthe sample size n .
Revised: In particular, we notice an important (but oftenignored) characteristic of the divergence example: (Reddi et al., 2018) picks ( β 1 , β 2 ) before pickingthe sample size n .

Original: Put in another way, to construct the divergence example, they change n fordifferent ( β 1 , β 2 ) .
Revised: Put in another way, to construct the divergence example, they change n fordifferent ( β 1 , β 2 ) .

Original: For instance, for ( β 1 , β 2 ) =
Revised: For instance, for ( β 1 , β 2 ) =

Original: (0 , 0 . 99) , we use one n to construct the divergentexample; for ( β 1 , β 2 )
Revised: (0 , 0 . 99) , we use one n to construct the divergentexample; for ( β 1 , β 2 )

Original: = (0 , 0 . 9999) , we use another n to construct another divergent example.
Revised: = (0 , 0 . 9999) , we use another n to construct another divergent example.

Original: This understanding reveals a gap between the divergence result and practice: In practical applicationsof Adam listed above, practitioners tune the hyperparameters ( β 1 , β 2 ) after the sample size n is given,while the divergence example considers the opposite order.
Revised: This understanding reveals a gap between the divergence result and practice: In practical applicationsof Adam listed above, practitioners tune the hyperparameters ( β 1 , β 2 ) after the sample size n is given,while the divergence example considers the opposite order.

Original: Considering the good performance of
Revised: Considering the good performance of

Original: Adam under fixed n , we conjecture that “the order of picking n or ( β 1 , β 2 ) " might be the major gapbetween theory and practice.
Revised: Adam under fixed n , we conjecture that “the order of picking n or ( β 1 , β 2 ) " might be the major gapbetween theory and practice.

Original: So could Adam possibly converge when n is fixed?
Revised: So could Adam possibly converge when n is fixed?

Original: Unfortunately,the behavior of vanilla Adam is far less studied than its variants (perhaps due to the criticism ofdivergence).
Revised: Unfortunately,the behavior of vanilla Adam is far less studied than its variants (perhaps due to the criticism ofdivergence).

Original: To explore this question, we run experiments for different choices of ( β 1 , β 2 ) on afew tasks.
Revised: To explore this question, we run experiments for different choices of ( β 1 , β 2 ) on afew tasks.

Original: First, we run Adam for a convex function (2) with fixed n (see the definition in Section3.2).
Revised: First, we run Adam for a convex function (2) with fixed n (see the definition in Section3.2).

Original: Second, we run Adam for the classification problem on data MNIST and CIFAR-10 with fixedbatchsize.
Revised: Second, we run Adam for the classification problem on data MNIST and CIFAR-10 with fixedbatchsize.

Original: We observe some interesting phenomena in Figure 1 (a), (b) and (c).
Revised: We observe some interesting phenomena in Figure 1 (a), (b) and (c).

Original: First, when β 2 is large, the optimization error is small for almost all values of β 1 .
Revised: First, when β 2 is large, the optimization error is small for almost all values of β 1 .

Original: Second, whenβ 1 , β 2 are both small, there is a red region with relatively large error.
Revised: Second, whenβ 1 , β 2 are both small, there is a red region with relatively large error.

Original: On MNIST, CIFAR-10, theerror in the red region is around 0.4 times higher than that in the blue region.
Revised: On MNIST, CIFAR-10, theerror in the red region is around 0.4 times higher than that in the blue region.

Original: The situation is a lotworse on function (2): the error in the red region is 70 times higher than that in the blue region.
Revised: The situation is a lotworse on function (2): the error in the red region is 70 times higher than that in the blue region.

Original: [Figure]
Revised: [Figure]

Revised: [Table]

Revised: Function

Original:  While Adam’s performances seem unstable in the red region, we find that Adam always performswell in the top blue region in Figure 1.
Revised: [Figure] [Table] [Figure] While Adam’s performances seem unstable in the red region, we find that Adam always performswell in the top blue region in Figure 1.

Original: This seems to suggest that Adam can converge without anyalgorithmic modification, as long as β 1 and β 2 are chosen properly.
Revised: This seems to suggest that Adam can converge without anyalgorithmic modification, as long as β 1 and β 2 are chosen properly.

Original: We ask the following question:
Revised: We ask the following question:

Original: When n is fixed, can vanilla Adam provably converge without any algorithmic modification?
Revised: When n is fixed, can vanilla Adam provably converge without any algorithmic modification?

Original: In this work, we theoretically explore this question.
Revised: In this work, we theoretically explore this question.

Original: Our contributions are summarized below andalso visualized in Figure 1 (d).
Revised: Our contributions are summarized below andalso visualized in Figure 1 (d).

Original: We prove the following results when n is fixed.
Revised: We prove the following results when n is fixed.

Original: • We prove that when β 2 is large enough and β 1 < √ β 2 , Adam converges to the neighborhoodof stationary points.
Revised: • We prove that when β 2 is large enough and β 1 < √ β 2 , Adam converges to the neighborhoodof critical points.

Original: The size of the neighborhood is propositional to the variance of stochasticgradients.
Revised: The size of the neighborhood is propositional to the variance of stochasticgradients.

Original: With an extra condition (so-called strong growth condition), we prove that Adam canconverge to stationary points.
Revised: With an extra condition (so-called strong growth condition), we prove that Adam canconverge to critical points.

Original: As β 2 increases, these results can cover any momentum parameterβ 1 ∈ [0 , 1) including the default setting β 1 = 0 .
Revised: As β 2 increases, these results can cover any momentum parameterβ 1 ∈ [0 , 1) including the default setting β 1 = 0 .

Original: In particular, our analysis does not requirebounded gradient assumption.•
Revised: In particular, our analysis does not requirebounded gradient assumption.•

Original: We study the divergence issue of Adam.
Revised: We study the divergence issue of Adam.

Original: We prove that: for any fixed n , there exists a functionsuch that, Adam diverges to infinity when ( β 1 , β 2 ) is picked in the red region in Figure 1 (d).The size of the red region increases with n .
Revised: We prove that: for any fixed n , there exists a functionsuch that, Adam diverges to infinity when ( β 1 , β 2 ) is picked in the red region in Figure 1 (d).The size of the red region increases with n .

Original: The shape of the region follows the solution to ouranalytic conditions.•
Revised: The shape of the region follows the solution to ouranalytic conditions.•

Original: We emphasize a few characteristics of our results.
Revised: We emphasize a few characteristics of our results.

Original: (1) phase transition .
Revised: (1) phase transition .

Original: The divergence resultconsiders the same setting (fixing n and then pick ( β 1 , β 2 ) ) as our convergence result, indicatingthat there is a phase transition from divergence to convergence.
Revised: The divergence resultconsiders the same setting (fixing n and then pick ( β 1 , β 2 ) ) as our convergence result, indicatingthat there is a phase transition from divergence to convergence.

Original: (2) problem-dependent bounds .Our
Revised: (2) problem-dependent bounds .Our

Original: convergence and divergence regions of ( β 1 , β 2 ) are problem-dependent, which is drasticallydifferent from (Reddi et al., 2018) which established the problem-independent worst-case choiceof ( β 1 , β 2 ) .
Revised: convergence and divergence regions of ( β 1 , β 2 ) are problem-dependent, which is drasticallydifferent from (Reddi et al., 2018) which established the problem-independent worst-case choiceof ( β 1 , β 2 ) .

Original: (3) non-asymptotic characterization .
Revised: (3) non-asymptotic characterization .

Original: the “divergence region” of ( β 1 , β 2 ) expandsas n increases and converges to the whole region [0 , 1) 2 as n goes to infinity, which recovers(actually stronger than) the problem-independent divergence result of (Reddi et al., 2018) thatrequires β 1 < √ β 2 .
Revised: the “divergence region” of ( β 1 , β 2 ) expandsas n increases and converges to the whole region [0 , 1) 2 as n goes to infinity, which recovers(actually stronger than) the problem-independent divergence result of (Reddi et al., 2018) thatrequires β 1 < √ β 2 .

Original: In this sense, we can view the divergence result of (Reddi et al., 2018) as anasymptotic characterization of the divergence region (as n → ∞ ) and our divergence result as annon-asymptotic characterization (for any fixed n ).
Revised: In this sense, we can view the divergence result of (Reddi et al., 2018) as anasymptotic characterization of the divergence region (as n → ∞ ) and our divergence result as anon-asymptotic characterization (for any fixed n ).

Original: We provide more discussion in Section 4.We believe
Revised: We provide more discussion in Section 4.We believe

Original: our results
Revised: our results

Original: can boost new understandings for Adam.
Revised: can boost new understandings for Adam.

Original: While Reddi
Revised: While Reddi

Original: et al.
Revised: et al.

Original: (2018) reveal that“Adam can diverge", our results show the other side of the coin: When n is fixed, Adam can still workwithout any algorithmic modification.
Revised: (2018) reveal that“Adam can diverge", our results show the other side of the coin: When n is fixed, Adam can still workwithout any algorithmic modification.

Original: Since “fixing n " is closer to practical scenarios, our resultssuggests that Adam is still a theoretically justified algorithm and practitioners can use it confidently.
Revised: Since “fixing n " is closer to practical scenarios, our resultssuggests that Adam is still a theoretically justified algorithm and practitioners can use it confidently.

Original: We further emphasize two features in this work.
Revised: We further emphasize two features in this work.

Original: First, our convergence results can cover anyβ 1 ∈ [0 , 1) , which can involve massive momentum.
Revised: First, our convergence results can cover anyβ 1 ∈ [0 , 1) , which can involve massive momentum.

Original: It turns out that large-momentum Adam is noteasy to analyze.
Revised: It turns out that large-momentum Adam is noteasy to analyze.

Original: Even with stronger assumptions like bounded gradient ( ∥∇ f ( x ) ∥ < C , ∀ x ), itsconvergence is not well understood (see related works in Section 2.2).
Revised: Even with stronger assumptions like bounded gradient ( ∥∇ f ( x ) ∥ < C , ∀ x ), itsconvergence is not well understood (see related works in Section 2.2).

Original: To our best knowledge, this isthe first result that proves Adam with any large β 1 can converge without any form of bounded gradientassumption.
Revised: To our best knowledge, this isthe first result that proves Adam with any large β 1 can converge without any form of bounded gradientassumption.

Original: The proof contains a new method to handle unbounded momentum in the stochasticnon-linear dynamics system.
Revised: The proof contains a new method to handle unbounded momentum in the stochasticnon-linear dynamics system.

Original: We will highlight our technical novelties in Section 5.
Revised: We will highlight our technical novelties in Section 5.

Original: Second, when n is fixed, we point out the behavior of Adam significantly changes under different( β 1 , β 2 ) :
Revised: Second, when n is fixed, we point out the behavior of Adam significantly changes under different( β 1 , β 2 ) :

Original: It converges in certain region but it may diverge in others.
Revised: It converges in certain region but it may diverge in others.

Original: Pointing out these behaviorsin the ( β 1 , β 2 ) diagram may provide better guidance for tuning β 1 and β 2 .
Revised: Pointing out these behaviorsin the ( β 1 , β 2 ) diagram may provide better guidance for tuning β 1 and β 2 .

Original: Removing boundednesscondition is important to characterize such qualitative behaviors.
Revised: Removing boundednesscondition is important to characterize such qualitative behaviors.

Original: With assumed boundedness ongradient or iterates, Adam cannot diverge to infinity, yet we have theoretical and numerical evidenceshowing that it can.
Revised: With assumed boundedness ongradient or iterates, Adam cannot diverge to infinity, yet we have theoretical and numerical evidenceshowing that it can.

Original:  Preliminaries
Revised:  Preliminaries

Original: 2.1 Problem Settings
Revised: 2.1 Problem Settings

Original: We consider finite-sum problem (1).
Revised: We consider finite-sum problem (1).

Original: We use x to denote the optimization variable.
Revised: We use x to denote the optimization variable.

Original: We denote ∇ f j asthe gradient of f j and let ◦ be the component-wise product.
Revised: We denote ∇ f j asthe gradient of f j and let ◦ be the component-wise product.

Original: The division and square-root operatorare component-wise as well.
Revised: The division and square-root operatorare component-wise as well.

Original: We present randomly shuffled Adam in Algorithm 1.
Revised: We present randomly shuffled Adam in Algorithm 1.

Original: In Algorithm 1, m denotes the 1st-order momentum and v denotes the 2nd-order momentum.
Revised: In Algorithm 1, m denotes the 1st-order momentum and v denotes the 2nd-order momentum.

Original: theyare weighted averaged by hyperparameter β 1 , β 2 , respectively.
Revised: theyare weighted averaged by hyperparameter β 1 , β 2 , respectively.

Original: Larger β 1 , β 2 will adopt more historyinformation.
Revised: Larger β 1 , β 2 will adopt more historyinformation.

Original: We denote x k,i , m k,i , v k,i ∈
Revised: We denote x k,i , m k,i , v k,i ∈

Original: R d as the value of x, m, v at the k -th outer loop (epoch)and i -th inner loop (batch), respectively.
Revised: R d as the value of x, m, v at the k -th outer loop (epoch)and i -th inner loop (batch), respectively.

Original: We choose η k = η 1 √ nk as the stepsize.
Revised: We choose η k = η 1 √ nk as the stepsize.

Original: In practice, ϵ isadopted for numerical stability and it is often chosen to be 10 − 8 .
Revised: In practice, ϵ isadopted for numerical stability and it is often chosen to be 10 − 8 .

Original: In our theory, we allow ϵ to be anarbitrary non-negative constant including 0 .
Revised: In our theory, we allow ϵ to be anarbitrary non-negative constant including 0 .

Original: In the original version of Adam in (Kingma & Ba, 2014), it has an additional “bias correction” step.
Revised: In the original version of Adam in (Kingma & Ba, 2014), it has an additional “bias correction” step.

Original: This “bias correction” step can be implemented by changing the stepsize η k into ˆ η k = √ 1 − β k1 − β k 1 η kand using zero initialization.
Revised: This “bias correction” step can be implemented by changing the stepsize η k into ˆ η k = √ 1 − β k1 − β k 1 η kand using zero initialization.

Original: In Algorithm 1, the “bias correction” step is replaced by a specialinitialization, which also corrects the bias.
Revised: In Algorithm 1, the “bias correction” step is replaced by a specialinitialization, which also corrects the bias.

Original: Note that ˆ η k ∈ [ √ 1 − β 2 η k , 1 1 − β 1 η k ] is well-bounded
Revised: Note that ˆ η k ∈ [ √ 1 − β 2 η k , 1 1 − β 1 η k ] is well-bounded

Original: Algorithm
Revised: Algorithm

Original: Initialize x 1 , 0 = x 0 , m 1 , − 1 = ∇ f ( x 0 ) and v 1 , − 1 = max i ∇ f i ( x 0 ) ◦ ∇ f i ( x 0 ) .
Revised: Initialize x 1 , 0 = x 0 , m 1 , − 1 = ∇ f ( x 0 ) and v 1 , − 1 = max i ∇ f i ( x 0 ) ◦ ∇ f i ( x 0 ) .

Original: for k = 1 → ∞ do
Revised: for k = 1 → ∞ do

Original: Sample { τ k, 0 , τ k, 1 , · · · , τ k,n − 1 } as a random permutation of { 0 , 1 , 2 , · · · , n − 1 } for i = 

Revised: Sample { τ k, 0 , τ k, 1 , · · · , τ k,n − 1 } as a random permutation of { 0 , 1 , 2 , · · · , n − 1 } for i =

Original: → n − 1 do m k,i =
Revised: → n − 1 do m k,i =

Original: β 1 m k,i − 1 + (1 − β 1 ) ∇ f τ k,i ( x k,i ) v k,i =
Revised: β 1 m k,i − 1 + (1 − β 1 ) ∇ f τ k,i ( x k,i ) v k,i =

Original: β 2 v k,i − 1 + (1 − β 2 ) ∇ f τ k,i ( x k,i ) ◦ ∇ f τ k,i ( x k,i ) x
Revised: β 2 v k,i − 1 + (1 − β 2 ) ∇ f τ k,i ( x k,i ) ◦ ∇ f τ k,i ( x k,i ) x

Original: k,i +1 =
Revised: k,i +1 =

Original: x k,i − η k √ v k,i + ϵ ◦ m k,i end for x k +1 , 

Revised: x k,i − η k √ v k,i + ϵ ◦ m k,i end for x k +1 ,

Original: = x k,n ; v k +1 , − 1 = v k,n − 1 ; m k +1 , − 1 = m k,n − 1 end for near η k , so η k and ˆ η k brings the same convergence rate.
Revised: = x k,n ; v k +1 , − 1 = v k,n − 1 ; m k +1 , − 1 = m k,n − 1 end for near η k , so η k and ˆ η k brings the same convergence rate.

Original: In addition, as the effect of initializationbecomes negligible when the training progresses, Adam with zero & our initialization will have thesame asymptotic behavior.
Revised: In addition, as the effect of initializationbecomes negligible when the training progresses, Adam with zero & our initialization will have thesame asymptotic behavior.

Original: We follow the form of Algorithm 1, which makes results cleaner.
Revised: We follow the form of Algorithm 1, which makes results cleaner.

Original: In ouranalysis, we make the assumptions below.
Revised: In ouranalysis, we make the assumptions below.

Original: Assumption 2.1.
Revised: Assumption 2.1.

Original: we consider x ∈ R d and f i ( x ) satisfies gradient Lipschitz continuous with constant
Revised: we consider x ∈ R d and f i ( x ) satisfies gradient Lipschitz continuous with constant

Original: L .
Revised: L .

Original: We assume f ( x ) is lower bounded by a finite constant f ∗ .
Revised: We assume f ( x ) is lower bounded by a finite constant f ∗ .

Original: Assumption 2.2.
Revised: Assumption 2.2.

Original: f i ( x ) and f ( x ) satisfy: (cid:80) n
Revised: f i ( x ) and f ( x ) satisfy: (cid:80) n

Original: [Equation]
Revised: [Equation]

Original: Assumption 2.2 is quite general.
Revised: Assumption 2.2 is quite general.

Original: When D 1 = 1 /n , it becomes the “constant variance” with constant
Revised: When D 1 = 1 /n , it becomes the “constant variance” with constant

Original: D 0 /n .
Revised: D 0 /n .

Original: “constant variance" condition is commonly used in both SGD and Adam analysis (e.g.(Ghadimi et al., 2016;
Revised: “constant variance" condition is commonly used in both SGD and Adam analysis (e.g.(Ghadimi et al., 2016;

Original: Zaheer et al., 2018; Huang et al., 2021)).
Revised: Zaheer et al., 2018; Huang et al., 2021)).

Original: Assumption 2.2 allows more flexiblechoices of D 1 ̸ = n and thus it is weaker than “constant variance”.
Revised: Assumption 2.2 allows more flexiblechoices of D 1 ̸ = n and thus it is weaker than “constant variance”.

Original: When D 0 > 0 , the problem instance is sometimes called “non-realizable" (Shi et al., 2020).
Revised: When D 0 > 0 , the problem instance is sometimes called “non-realizable" (Shi et al., 2020).

Original: In thiscase, adaptive gradient methods are not guaranteed to reach the exact stationary point.
Revised: In thiscase, adaptive gradient methods are not guaranteed to reach the exact critical point.

Original: Instead, theyonly converge to a bounded region (near stationary points)
Revised: Instead, theyonly converge to a bounded region (near critical points)

Original: (Zaheer et al., 2018; Shi et al., 2020).
Revised: (Zaheer et al., 2018; Shi et al., 2020).

Original: Thisphenomenon indeed occurs for Adam in experiments, even with diminishing stepsize (see Appendix
Revised: Thisphenomenon indeed occurs for Adam in experiments, even with diminishing stepsize (see Appendix

Original: B.1).
Revised: B.1).

Original: The behavior of SGD is similar: constant stepsize SGD converges to a bounded region with itssize propositional to the noise level D 

Revised: The behavior of SGD is similar: constant stepsize SGD converges to a bounded region with itssize propositional to the noise level D

Original: (Yan et al., 2018; Yu et al., 2019; Liu et al., 2020b).
Revised: (Yan et al., 2018; Yu et al., 2019; Liu et al., 2020b).

Original: When D 0 = 0 , Assumption 2.2 is often called “strong growth condition" (SGC) (Vaswani et al.,2019).
Revised: When D 0 = 0 , Assumption 2.2 is often called “strong growth condition" (SGC) (Vaswani et al.,2019).

Original: When ∥∇ f ( x ) ∥ = 0 , under SGC we have ∥∇ f j ( x ) ∥ = 0 for all j .
Revised: When ∥∇ f ( x ) ∥ = 0 , under SGC we have ∥∇ f j ( x ) ∥ = 0 for all j .

Original: SGC is increasinglypopular recently e.g.(Schmidt & Roux, 2013; Vaswani et al., 2019).
Revised: SGC is increasinglypopular recently e.g.(Schmidt & Roux, 2013; Vaswani et al., 2019).

Original: This condition is reasonablein the overparameterized regime where neural networks are capable of interpolating all data points:
Revised: This condition is reasonablein the overparameterized regime where neural networks are capable of interpolating all data points:

Original: When every data point reaches zero training loss, the gradient of each point also converges to zero(Vaswani et al., 2019).
Revised: When every data point reaches zero training loss, the gradient of each point also converges to zero(Vaswani et al., 2019).

Original: We will show that Adam can converge to stationary points if SGC holds.
Revised: We will show that Adam can converge to critical points if SGC holds.

Original: When n, f ∗ , L, D 0 , D 1 are fixed a priori, we use F n,f ∗ L,D 0 ,D 1 ( R d ) to denote the (sub) function classcontaining f ( x ) satisfying Assumption 2.1 and 2.2 with constant n, f ∗ , etc..
Revised: When n, f ∗ , L, D 0 , D 1 are fixed a priori, we use F n,f ∗ L,D 0 ,D 1 ( R d ) to denote the (sub) function classcontaining f ( x ) satisfying Assumption 2.1 and 2.2 with constant n, f ∗ , etc..

Original: We introduce thisnotation to clearly present the divergence result in Proposition 3.3.
Revised: We introduce thisnotation to clearly present the divergence result in Proposition 3.3.

Original: Without this pre-defined functionclass, the claim of divergence might be confusing.
Revised: Without this pre-defined functionclass, the claim of divergence might be confusing.

Original: 2.2 Related Works
Revised: 2.2 Related Works

Original: Ever since Reddi et al.
Revised: Ever since Reddi et al.

Original: (2018) pointed out the divergence issue, there are many attempts on designingnew variants of Adam.
Revised: (2018) pointed out the divergence issue, there are many attempts on designingnew variants of Adam.

Original: As introduced in Section 1, these methods can address the divergence ofthe original Adam and they often achieve satisfactory performance in practice.
Revised: As introduced in Section 1, these methods can address the divergence of theoriginal Adam. But they might brings extra concerns (e.g. extra hyperparameters).

Original: Since we focus onunderstanding Adam without modification , we introduce more variants later in Appendix D.1.
Revised: Since we focus onunderstanding Adam without modification , we introduce more variants later in Appendix D.1.

Original: Compared with proposing new variants, the convergence of vanilla Adam is far less studied thanits variants (perhaps due to the criticism of divergence).
Revised: Compared with proposing new variants, the convergence of vanilla Adam is far less studied thanits variants (perhaps due to the criticism of divergence).

Original: We summarize a few works here.
Revised: We summarize a few works here.

Original: Zhouet al.
Revised: Zhouet al.

Original: (2018b) analyze the counter-example in (Reddi et al., 2018) and find certain hyperparametercan work.
Revised: (2018b) analyze the counter-example in (Reddi et al., 2018) and find certain hyperparametercan work.

Original: However, their analysis is restricted to the counter-example.
Revised: However, their analysis is restricted to the counter-example.

Original: Zaheer et al.
Revised: Zaheer et al.

Original: (2018) revealsrelation between mini-batch sizes and (non-)convergence of Adam.
Revised: (2018) revealsrelation between mini-batch sizes and (non-)convergence of Adam.

Original: However, this work requireβ 1 = 0 and Adam is reduced to RMSProp (Hinton et al., 2012).
Revised: However, this work requireβ 1 = 0 and Adam is reduced to RMSProp (Hinton et al., 2012).

Original: De et al.
Revised: De et al.

Original: (2018) analyze RMSPropand non-zero- β 1 Adam, but they assume the sign of all stochastic gradients to keep the same.
Revised: (2018) analyze RMSPropand non-zero- β 1 Adam, but they assume the sign of all stochastic gradients to keep the same.

Original: Itseems unclear how to check this condition a priori.
Revised: Itseems unclear how to check this condition a priori.

Original: Additionally, they require β 1 to be inverselyrelated to the upper bound of gradient, which forces β 1 to be small (as a side note, this result only applies to full-batch Adam).
Revised: Additionally, they require β 1 to be inverselyrelated to the upper bound of gradient, which forces β 1 to be small (as a side note, this result only applies to full-batch Adam).

Original: Défossez et al.
Revised: Défossez et al.

Original: (2020) analyze Adam with β 1 < β 2 and provide someinsights on the momentum mechanisms.
Revised: (2020) analyze Adam with β 1 < β 2 and provide someinsights on the momentum mechanisms.

Original: However, their bound is inversely proportional to ϵ (thehyperparameter for numerical stability) and the bound goes to infinity when ϵ goes to 0.
Revised: However, their bound is inversely proportional to ϵ (thehyperparameter for numerical stability) and the bound goes to infinity when ϵ goes to 0.

Original: This isdifferent from practical application since small ϵ such as 10 − 8 often works well.
Revised: This isdifferent from practical application since small ϵ such as 10 − 8 often works well.

Original: Further, using largeϵ is against the nature of adaptive gradient methods because √ v no longer dominates in the choiceof stepsize.
Revised: Further, using largeϵ is against the nature of adaptive gradient methods because √ v no longer dominates in the choiceof stepsize.

Original: In this case, Adam is essentially transformed back to SGD.
Revised: In this case, Adam is essentially transformed back to SGD.

Original: Two recent works (Huanget al., 2021) and (Guo et al., 2021) propose novel and simple frameworks to analyze Adam-familywith large β 1 .
Revised: Two recent works (Huanget al., 2021) and (Guo et al., 2021) propose novel and simple frameworks to analyze Adam-familywith large β 1 .

Original: Yet, they require the effective stepsize of Adam to be bounded in certain interval, i.e.,1 √ v t + ϵ ∈ [ C l , C u ] 2 .
Revised: Yet, they require the effective stepsize of Adam to be bounded in certain interval, i.e.,1 √ v t + ϵ ∈ [ C l , C u ] 2 .

Original: This boundedness condition changes Adam into AdaBound (Luo et al.
Revised: This boundedness condition changes Adam into AdaBound (Luo et al.

Original: , 2019)and thus they cannot explain the observations on original Adam in Section 1.
Revised: , 2019)and thus they cannot explain the observations on original Adam in Section 1.

Original: Additionally, all ofthese works (including those for new variants) require bounded gradient assumptions.
Revised: Additionally, all ofthese works (including those for new variants) require bounded gradient assumptions.

Original: Among all these works, (Shi et al., 2020) is the first work that proves RMSProp (a non-momentumversion of Adam with β 1 = 0 ) can converge without bounded gradient assumption.
Revised: Among all these works, (Shi et al., 2020) is the first work that proves RMSProp (a non-momentumversion of Adam with β 1 = 0 ) can converge without bounded gradient assumption.

Original: When the2nd-order parameter β 2 is large, they show that RMSProp converges to a bounded region in general,and to stationary points under SGC.
Revised: When the2nd-order parameter β 2 is large, they show that RMSProp converges to a bounded region in general,and to critical points under SGC.

Original: They also provide a convergence result for Adam with β 1 closeenough to 0.
Revised: They also provide a convergence result for Adam with β 1 closeenough to 0.

Original: However, a simple calculation by Zhang et al.
Revised: However, a simple calculation by Zhang et al.

Original: (2022) shows that they require β 1 < 10 − 7on real datasets.
Revised: (2022) shows that they require β 1 < 10 − 7on real datasets.

Original: Thus their result does not provide much extra information other than RMSProp.
Revised: Thus their result does not provide much extra information other than RMSProp.

Original: Itis still unclear how β 1 affects the (non-)convergence of Adam.
Revised: Itis still unclear how β 1 affects the (non-)convergence of Adam.

Original: Proving convergence under large β 1is non-trivial.
Revised: Proving convergence under large β 1is non-trivial.

Original: We point out there are at least two challenges.
Revised: We point out there are at least two challenges.

Original: First, it seems unclear how to controlthe massive momentum m t .
Revised: First, it seems unclear how to controlthe massive momentum m t .

Original: Second, the momentum m t in Adam is multiplied by 1 / √ v t , causingnon-linear perturbation.
Revised: Second, the momentum m t in Adam is multiplied by 1 / √ v t , causingnon-linear perturbation.

Original: We use much different techniques to resolve these two issues.
Revised: We use much different techniques to resolve these two issues.

Original: We highlightour technical novelties in Section 5.
Revised: We highlightour technical novelties in Section 5.

Original: 2.3 The Importance and Difficulties of Removing Bounded Gradient Assumptions
Revised: 2.3 The Importance and Difficulties of Removing Bounded Gradient Assumptions

Original: Here, we emphasize the importance to remove bounded gradient assumption.
Revised: Here, we emphasize the importance to remove bounded gradient assumption.

Original: First, unlike theassumptions in Section 2.1, bounded gradient is not common in SGD analysis.
Revised: First, unlike theassumptions in Section 2.1, bounded gradient is not common in SGD analysis.

Original: So it is of theoreticalinterests to remove this condition for Adam.
Revised: So it is of theoreticalinterests to remove this condition for Adam.

Original: Second, bounded gradient condition rules out thechances of gradient divergence a priori.
Revised: Second, bounded gradient condition rules out thechances of gradient divergence a priori.

Original: However, there are numerical evidences showing that Adam’sgradient can diverge (see Appendix B and Appendix B.1).
Revised: However, there are numerical evidences showing that Adam’sgradient can diverge (see Appendix B and Appendix B.1).

Original: Removing the boundedness assumptionhelps us point out the divergence and convergence behavior of Adam in the ( β 1 , β 2 ) diagram.
Revised: Removing the boundedness assumptionhelps us point out the divergence and convergence behavior of Adam in the ( β 1 , β 2 ) diagram.

Original: However, it is often difficult to remove the boundedness condition, especially for stochastic algorithmswith momentum.
Revised: However, it is often difficult to remove the boundedness condition, especially for stochastic algorithmswith momentum.

Original: For instance, An early paper Bertsekas & Tsitsiklis (2000) analyzed SGD-typemethods without any boundedness condition.
Revised: For instance, An early paper Bertsekas & Tsitsiklis (2000) analyzed SGD-typemethods without any boundedness condition.

Original: But it is not until recently that Yu et al.
Revised: But it is not until recently that Yu et al.

Original: Liuet al. (2020b) prove SGDM (SGD with momentum) converges without bounded gradient assumption.
Revised: Liuet al. (2020b) prove SGDM (SGD with momentum) converges without bounded gradient assumption.

Original: Such attempts of removing boundedness assumptions are often appreciated for general optimizationproblems where “bounded-assumption-free" is considered as a major contribution.
Revised: Such attempts of removing boundedness assumptions are often appreciated for general optimizationproblems where “bounded-assumption-free" is considered as a major contribution.

Original: For Adam, the role of momentum m t is even more intricate since it is multiplied by 1 / √ v t .
Revised: For Adam, the role of momentum m t is even more intricate since it is multiplied by 1 / √ v t .

Original: Combinedwith v t , the impact of previous signals not only affect the update direction, but also change the stepsizefor each component.
Revised: Combinedwith v t , the impact of previous signals not only affect the update direction, but also change the stepsizefor each component.

Original: Further, both momentum m t and stepsize 1 / √ v t are random variables andthey are highly correlated.
Revised: Further, both momentum m t and stepsize 1 / √ v t are random variables andthey are highly correlated.

Original: Such statistical dependency causes trouble for analysis.
Revised: Such statistical dependency causes trouble for analysis.

Original: In summary,the role of momentum in Adam could be much different from that in SGDM or GDM.
Revised: In summary,the role of momentum in Adam could be much different from that in SGDM or GDM.

Original: Even withboundedness conditions, the convergence of large- β 1 Adam is still not well understood (see relatedworks in Section 2.2).
Revised: Even withboundedness conditions, the convergence of large- β 1 Adam is still not well understood (see relatedworks in Section 2.2).

Original: In this work, we propose new techniques to handle Adam’s momentum underany large β 1 , regardless of the gradient magnitude.
Revised: In this work, we propose new techniques to handle Adam’s momentum underany large β 1 , regardless of the gradient magnitude.

Original: These techniques are not revealed in any existingworks.
Revised: These techniques are not revealed in any existingworks.

Original: We introduce our technical contribution in Section 5.
Revised: We introduce our technical contribution in Section 5.

Original:  Main Results
Revised:  Main Results

Original: 3.1 Convergence Results
Revised: 3.1 Convergence Results

Original: Here, we give the convergence results under large β 2 .
Revised: Here, we give the convergence results under large β 2 .

Original: Theorem 3.1.
Revised: Theorem 3.1.

Original: For any f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) , we assume the hyperparameters in Algorithm 1satisfy: β 1 < √ β 2 < 1 ; β 2 is greater or equal to a threshold γ 1 ( n ) ; and η k = η 1 √ nk .
Revised: For any f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) , we assume the hyperparameters in Algorithm 1satisfy: β 1 < √ β 2 < 1 ; β 2 is greater or equal to a threshold γ 1 ( n ) ; and η k = η 1 √ nk .

Original: Let k m ∈
Revised: Let k m ∈

Original: Nsatisfies k m ≥ 4 and β ( k m − 1)
Revised: Nsatisfies k m ≥ 4 and β ( k m − 1)

Original: n≤ β n 1 √ k m − 1 , 4 we have the following results for any T > k m :
Revised: n≤ β n 1 √ k m − 1 , 4 we have the following results for any T > k m :

Original: [Table]
Revised: [Table]

Original: Remark 1: the choice of β 2 .
Revised: Remark 1: the choice of β 2 .

Original: Our theory suggests that large β 2 should be used to ensure convergence.
Revised: Our theory suggests that large β 2 should be used to ensure convergence.

Original: This message matches our experimental findings in Figure 1.
Revised: This message matches our experimental findings in Figure 1.

Original: We would like to point outthat the requirement of “large β 2 " is neccessary, because small β 2 will indeed lead to divergence(shown later in Section 3.2).
Revised: We would like to point outthat the requirement of “large β 2 " is neccessary, because small β 2 will indeed lead to divergence(shown later in Section 3.2).

Original: We here comment a bit on the the threshold γ 1 ( n ) .
Revised: We here comment a bit on the the threshold γ 1 ( n ) .

Original: γ 1 ( n ) satisfiesβ 2 ≥ 1 − O (cid:16) 1 − β n 1 n 2 ρ (cid:17) (see inequality (33) and Remark G.7), where ρ is a constant that depends on thetraining trajectory.
Revised: γ 1 ( n ) satisfiesβ 2 ≥ 1 − O (cid:16) 1 − β n 1 n 2 ρ (cid:17) (see inequality (33) and Remark G.7), where ρ is a constant that depends on thetraining trajectory.

Original: In worst cases, ρ is upper bounded by n 2 . 5 , but we find the practical ρ to be muchsmaller.
Revised: In worst cases, ρ is upper bounded by n 2 . 5 , but we find the practical ρ to be muchsmaller.

Original: In Appendix B.1, we estimate ρ on MNIST and CIFAR-10.
Revised: In Appendix B.1, we estimate ρ on MNIST and CIFAR-10.

Original: In practical training process, weempirically observe that ρ ≈ O ( n ) , thus the required γ 1 ( n ) ≈ 1 − O (cid:0) n − 3 (cid:1) .
Revised: In practical training process, weempirically observe that ρ ≈ O ( n ) , thus the required γ 1 ( n ) ≈ 1 − O (cid:0) n − 3 (cid:1) .

Original: Note that our thresholdof β 2 is a sufficient condition for convergence, so there may be a gap between the practical choicesand the theoretical bound of β 2 .
Revised: Note that our thresholdof β 2 is a sufficient condition for convergence, so there may be a gap between the practical choicesand the theoretical bound of β 2 .

Original: Closing the gap will be an interesting future direction.
Revised: Closing the gap will be an interesting future direction.

Original: We find that γ 1 ( n ) increases with n .
Revised: We find that γ 1 ( n ) increases with n .

Original: This property suggests that larger β 2 should be used when n islarge.
Revised: This property suggests that larger β 2 should be used when n islarge.

Original: This phenomenon is also verified by our experiments in Figure 8 (d), Appendix B. We alsoremark that γ 1 ( n ) slowly increases with β 1 .
Revised: This phenomenon is also verified by our experiments in Figure 8 (d), Appendix B. We alsoremark that γ 1 ( n ) slowly increases with β 1 .

Original: This property is visualized in Figure 1 (d) where thelower boundary of blue region slightly lifts up when β 1 increases.
Revised: This property is visualized in Figure 1 (d) where thelower boundary of blue region slightly lifts up when β 1 increases.

Original: Remark 2: the choice of β 1 .
Revised: Remark 2: the choice of β 1 .

Original: β 1 < √ β 2 is also a requirement in (Kingma & Ba, 2014), but theirconvergence analysis requires bounded gradient and Reddi et al.
Revised: β 1 < √ β 2 is also a requirement in (Kingma & Ba, 2014), but theirconvergence analysis requires bounded gradient and Reddi et al.

Original: (2018) later pointed out that theproof has flaws.
Revised: (2018) later pointed out that theproof has flaws.

Original: Theorem 3.1 is the first result that proves the convergence of Adam with the samechoice of β 1 , but with less assumptions.
Revised: Theorem 3.1 is the first result that proves the convergence of Adam with the samechoice of β 1 , but with less assumptions.

Original: Since β 2 is suggested to be large, our convergence result cancover flexible choice of β 1 ∈ [0 , 1) .
Revised: Since β 2 is suggested to be large, our convergence result cancover flexible choice of β 1 ∈ [0 , 1) .

Original: For instance, β 2 = 0 . 999 brings the threshold of β 1 < 0 .
Revised: For instance, β 2 = 0 . 999 brings the threshold of β 1 < 0 .

Original: 9995 ,which covers basically all practical choices of β 1 reported in the literature (see Section 1),
Revised: 9995 ,which covers basically all practical choices of β 1 reported in the literature (see Section 1),

Original: includingthe default setting β 1 = 0 . 9 .
Revised: includingthe default setting β 1 = 0 . 9 .

Original: Remark 3: convergence to a bounded region.
Revised: Remark 3: convergence to a bounded region.

Original: When D 0 > 0 , Adam converges to a boundedregion near stationary points.
Revised: When D 0 > 0 , Adam converges to a boundedregion near critical points.

Original: As discussed in Section 2.1, converging to bounded region is commonfor stochastic methods including constant-stepsize SGD (Yan et al., 2018;
Revised: As discussed in Section 2.1, converging to bounded region is commonfor stochastic methods including constant-stepsize SGD (Yan et al., 2018;

Original: Yu et al., 2019;
Revised: Yu et al., 2019;

Original: Liu et al.,2020b) and diminishing-stepsize RMSProp (Zaheer et al., 2018; Shi et al., 2020).
Revised: Liu et al.,2020b) and diminishing-stepsize RMSProp (Zaheer et al., 2018; Shi et al., 2020).

Original: This phenomenonis also observed in practice: even for convex quadratic function with D 0 > 0 , Adam with diminishingstepsize cannot reach exactly zero gradient (see Appendix B.1).
Revised: This phenomenonis also observed in practice: even for convex quadratic function with D 0 > 0 , Adam with diminishingstepsize cannot reach exactly zero gradient (see Appendix B.1).

Original: The good news is that, the constant
Revised: The good news is that, the constant

Original: O ( √ D 0 ) vanishes to 0 as β 2 goes to 1.
Revised: O ( √ D 0 ) vanishes to 0 as β 2 goes to 1.

Original: This can be seen in Remark G.14 in Appendix G.9.
Revised: This can be seen in Remark G.14 in Appendix G.9.

Original: As a corollary of Theorem 3.1, we have the following result under SGC (i.e., D 0 = 0 ).
Revised: As a corollary of Theorem 3.1, we have the following result under SGC (i.e., D 0 = 0 ).

Original: Corollary 3.2.
Revised: Corollary 3.2.

Original: Under the setting in Theorem 3.1.
Revised: Under the setting in Theorem 3.1.

Original: When D 0 = 0 for Assumption 2.2, we have
Revised: When D 0 = 0 for Assumption 2.2, we have

Original: [Equation]
Revised: [Equation]

Original: Under SGC (i.e. D 0 = 0 ), Corollary 3.2 states that Adam can converge to stationary points.
Revised: Under SGC (i.e. D 0 = 0 ), Corollary 3.2 states that Adam can converge to critical points.

Original: This isindeed the case in practice.
Revised: This isindeed the case in practice.

Original: For instance, function (2) satisfies SGC and we observe 0 gradient normafter Adam converges (see Appendix B and Appendix B.1).
Revised: For instance, function (2) satisfies SGC and we observe 0 gradient normafter Adam converges (see Appendix B and Appendix B.1).

Original: The convergence rate in Corollary 3.2 iscomparable to that of SGD under the same condition in (Vaswani et al., 2019).
Revised: The convergence rate in Corollary 3.2 iscomparable to that of SGD under the same condition in (Vaswani et al., 2019).

Original: 3.2 Divergence Results
Revised: 3.2 Divergence Results

Original: Theorem 3.1 shows that when β 2 is large, any β 1 < √ β 2 ensures convergence.
Revised: Theorem 3.1 shows that when β 2 is large, any β 1 < √ β 2 ensures convergence.

Original: Now we consider 253 the case where β 2 is small.
Revised: Now we consider 254 the case where β 2 is small.

Original: We will show that in this case, a wide range of β 1 is facing the risk 254 of diverging to infinity.
Revised: We will show that in this case, a wide range of β 1 is facing the risk 255 of diverging to infinity.

Original: The divergence of small- β 2 Adam suggests that “large β 2 " is necessary inthe convergence result Theorem 3.1.
Revised: The divergence of small- β 2 Adam suggests that “large β 2 " is necessary inthe convergence result Theorem 3.1.

Original: We construct a counter-example in F n,f ∗ L,D 0 ,D 1 ( R d ) .
Revised: We construct a counter-example in F n,f ∗ L,D 0 ,D 1 ( R d ) .

Original: Considerf ( x ) = (cid:80) n − 1 i =

Revised: Considerf ( x ) = (cid:80) n − 1 i =

Original: f i ( x ) for x ∈ R , we define f i ( x ) as:
Revised: f i ( x ) for x ∈ R , we define f i ( x ) as:

Original: [Equation]
Revised: [Equation]

Original: Summing up all the f i ( x ) , we can see that
Revised: Summing up all the f i ( x ) , we can see that

Original: [Equation]
Revised: [Equation]

Original: is a lower bounded convex smooth function with optimal solution x ∗ = − 2 .
Revised: is a lower bounded convex smooth function with optimal solution x ∗ = − 2 .

Original: Function (2) allowsboth iterates and gradients to diverge to infinity.
Revised: Function (2) allowsboth iterates and gradients to diverge to infinity.

Original: As shown in Figure 1 (a), when running Adam on(2), there exists a red large-error region.
Revised: As shown in Figure 1 (a), when running Adam on(2), there exists a red large-error region.

Original: This shows the sign of divergence.
Revised: This shows the sign of divergence.

Original: We further theoreticallyverify the conjecture in Proposition 3.3.
Revised: We further theoreticallyverify the conjecture in Proposition 3.3.

Original: Proposition 3.3.
Revised: Proposition 3.3.

Original: For any function class F n,f ∗ L,D 0 ,D 1 ( R d ) , there exists a f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) , s.t.when ( β 1 , β 2 ) satisfies analytic condition (11) , (12) , (13) in Appendix E, Adam’s iterates and functionvalues diverge to infinity.
Revised: For any function class F n,f ∗ L,D 0 ,D 1 ( R d ) , there exists a f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) , s.t.when ( β 1 , β 2 ) satisfies analytic condition (11) , (12) , (13) in Appendix E, Adam’s iterates and functionvalues diverge to infinity.

Original: By solving these conditions in NumPy , we plot the orange region in Figure2.
Revised: By solving these conditions in NumPy , we plot the orange region in Figure2.

Original: The size of the region depends on n and it expands to the whole region when n goes to infinity.0.
Revised: The size of the region depends on n and it expands to the whole region when n goes to infinity.0.

Original: The proof can be seen in
Revised: The proof can be seen in

Original: Appendix E.
Revised: Appendix E.

Original: We find the “divergenceregion" always stays below the “convergence threshold" γ 1 ( n ) in
Revised: We find the “divergenceregion" always stays below the “convergence threshold" γ 1 ( n ) in

Original: Theorem 3.1, so the two results are self-consistent (see the remarkin Appendix E).
Revised: Theorem 3.1, so the two results are self-consistent (see the remarkin Appendix E).

Original: Proposition 3.3 states the divergence of iteratesand function values.
Revised: Proposition 3.3 states the divergence of iteratesand function values.

Original: Consistently, our experiments also show thedivergence of gradient (see Appendix B and Appendix B.1).
Revised: Consistently, our experiments also show thedivergence of gradient (see Appendix B and Appendix B.1).

Original: Theseresults characterize Adam’s divergence behavior both numericallyand theoretically.
Revised: Theseresults characterize Adam’s divergence behavior both numericallyand theoretically.

Original: We emphasize that the orange region is not discussed in (Reddi et al.,2018) because we consider n fixed while they allow n changing.
Revised: We emphasize that the orange region is not discussed in (Reddi et al.,2018) because we consider n fixed while they allow n changing.

Original: When n is allowed to increase, our orange region will expand to thewhole region and thus we can derive a similar (actually stronger)result as (Reddi et al., 2018).
Revised: When n is allowed to increase, our orange region will expand to thewhole region and thus we can derive a similar (actually stronger)result as (Reddi et al., 2018).

Original: We provide more explanation in
Revised: We provide more explanation in

Original: Section 4.
Revised: Section 4.

Original: Combining Theorem 3.1 and Proposition 3.3, we establisha clearer image on the relation between ( β 1 , β 2 ) and qualitativebehavior of Adam.
Revised: Combining Theorem 3.1 and Proposition 3.3, we establisha clearer image on the relation between ( β 1 , β 2 ) and qualitativebehavior of Adam.

Original:  Reconciling Our Results with (Reddi et al., 2018)
Revised:  Reconciling Our Results with (Reddi et al., 2018)

Original: We discuss more on the relation between (Reddi et al., 2018) and our results.
Revised: We discuss more on the relation between (Reddi et al., 2018) and our results.

Original: Recall Reddi et al.(2018) show that “for any β 1 < √ β 2 < 1 , Adam can diverge".
Revised: Recall Reddi et al.(2018) show that “for any β 1 < √ β 2 < 1 , Adam can diverge".

Original: This does not contradict with ourconvergence results in Theorem 3.1.
Revised: This does not contradict with ourconvergence results in Theorem 3.1.

Original: Further, it is different from our divergence result in Proposition3.3.
Revised: Further, it is different from our divergence result in Proposition3.3.

Original: As discussed in Section 1, the key difference lies in whether ( β 1 , β 2 ) is picked before or afterpicking n , which often stands for sample size or the number of mini-batch.
Revised: As discussed in Section 1, the key difference lies in whether ( β 1 , β 2 ) is picked before or afterpicking n , which often stands for sample size or the number of mini-batch.

Original: Since n is fixed when the (sub-)function class F n,f ∗ L,D 0 ,D 1 ( R d ) is given, the above key difference canalso be expressed as whether ( β 1 , β 2 ) is picked before or after picking F n,f ∗ L,D 0 ,D 1 ( R d ) .
Revised: Since n is fixed when the (sub-)function class F n,f ∗ L,D 0 ,D 1 ( R d ) is given, the above key difference canalso be expressed as whether ( β 1 , β 2 ) is picked before or after picking F n,f ∗ L,D 0 ,D 1 ( R d ) .

Original: Now wediscuss the following two cases.
Revised: Now wediscuss the following two cases.

Original: Case I: When ( β 1 , β 2 ) is picked before picking F n,f ∗ L,D 0 ,D 1 ( R d ) .
Revised: Case I: When ( β 1 , β 2 ) is picked before picking F n,f ∗ L,D 0 ,D 1 ( R d ) .

Original: As discussed in Section 1, the divergence result requires differentn for different ( β 1 , β 2 ) .
Revised: As discussed in Section 1, the divergence result requires differentn for different ( β 1 , β 2 ) .

Original: In this sense, the considered function classis constantly changing.
Revised: In this sense, the considered function classis constantly changing.

Original: It does not contradict with our Theorem 3.1which considers a fixed function class with fixed n .
Revised: It does not contradict with our Theorem 3.1which considers a fixed function class with fixed n .

Original: For Case I , weillustrate Adam’s behavior in Figure 3.
Revised: For Case I , weillustrate Adam’s behavior in Figure 3.

Original: The red region is proved by(Reddi et al., 2018).
Revised: The red region is proved by(Reddi et al., 2018).

Original: For completeness, we remove the condition“ β 1 < √ β 2 " and further prove that Adam will diverge to infinityfor any ( β 1 , β 2 ) ∈ [0 , 1) 2 .
Revised: For completeness, we remove the condition“ β 1 < √ β 2 " and further prove that Adam will diverge to infinityfor any ( β 1 , β 2 ) ∈ [0 , 1) 2 .

Original: The result is shown in the following
Revised: The result is shown in the following

Original: Corollary 4.1. Corollary 4.1.
Revised: Corollary 4.1. Corollary 4.1.

Original: For any ( β 1 , β 2 ) ∈ [0 , 1) 2 , there exists a functionsatisfying Assumption 2.1 and 2.2 that the Adam’s iterates andfunction values diverge to infinity.
Revised: For any ( β 1 , β 2 ) ∈ [0 , 1) 2 , there exists a functionsatisfying Assumption 2.1 and 2.2 that the Adam’s iterates andfunction values diverge to infinity.

Original: Proof of Corollary 4.1 can be seen in the final paragraph in Appendix
Revised: Proof of Corollary 4.1 can be seen in the final paragraph in Appendix

Original: E.
Revised: E.

Original: In the proof, we also require different n to cause divergence for different ( β 1 , β 2 ) .
Revised: In the proof, we also require different n to cause divergence for different ( β 1 , β 2 ) .

Original: So the functionclass is constantly changing.
Revised: So the functionclass is constantly changing.

Original: As a result, in Case I , we cannot prove any convergence result.
Revised: As a result, in Case I , we cannot prove any convergence result.

Original: [Table]
Revised: [Table]

Original: Case II: When ( β 1 , β 2 ) is picked after picking F n,f ∗ L,D 0 ,D 1 ( R d ) .
Revised: Case II: When ( β 1 , β 2 ) is picked after picking F n,f ∗ L,D 0 ,D 1 ( R d ) .

Original: When the function class is pickedin advance, sample size n will also be fixed.
Revised: When the function class is pickedin advance, sample size n will also be fixed.

Original: This case is closer to most practical applications.
Revised: This case is closer to most practical applications.

Original: Inthis case, we find that Adam’s behavior changes significantly in the different region of Figure 3.
Revised: Inthis case, we find that Adam’s behavior changes significantly in the different region of Figure 3.

Original: First, ∀ f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) will converge when β 1 < √ β 2 and β 2 is large.
Revised: First, ∀ f ( x ) ∈ F n,f ∗ L,D 0 ,D 1 ( R d ) will converge when β 1 < √ β 2 and β 2 is large.

Original: Second, ∃ f ( x ) ∈
Revised: Second, ∃ f ( x ) ∈

Original: F n,f ∗ L,D 0 ,D 1 ( R d ) will diverge to infinity when ( β 1 , β 2 ) are in the orange region in Figure 2.
Revised: F n,f ∗ L,D 0 ,D 1 ( R d ) will diverge to infinity when ( β 1 , β 2 ) are in the orange region in Figure 2.

Original: Since Case
Revised: Since Case

Original: II is closer to practical scenarios, these results can provide better guidance for hyperparameter tuningfor Adam users.
Revised: II is closer to practical scenarios, these results can provide better guidance for hyperparameter tuningfor Adam users.

Original: We provide some suggestions for practitioners in Appendix C.
Revised: We provide some suggestions for practitioners in Appendix C.

Original: For Case II , we summarize the possible behaviors of Adam in Table 1.
Revised: For Case II , we summarize the possible behaviors of Adam in Table 1.

Original: We also illustrate ourconvergence and divergence results in Figure 1 (d).
Revised: We also illustrate ourconvergence and divergence results in Figure 1 (d).

Original: Note that there are some blanket areas where
Revised: Note that there are some blanket areas where

Original: Adam’s behavior remains unknown, this part will be left as interesting future work.
Revised: Adam’s behavior remains unknown, this part will be left as interesting future work.

Original:  Proof Ideas for the Convergence Result
Revised:  Proof Ideas for the Convergence Result

Original: We now introduce our proof ideas for the convergence result in Theorem 3.1.
Revised: We now introduce our proof ideas for the convergence result in Theorem 3.1.

Original: Simply put, we want to 318 control the update direction m k,i / √ v k,i inside the dual cone of gradient direction.
Revised: Simply put, we want to 319 control the update direction m k,i / √ v k,i inside the dual cone of gradient direction.

Original: Namely:
Revised: Namely:

Original: E ⟨∇ f
Revised: E ⟨∇ f

Original: ( x k, 0 ) ,
Revised: ( x k, 0 ) ,

Original: [Equation]
Revised: [Equation]

Original: However, directly proving (3) could be difficult because both m k,i and v k,i distort the trajectory.
Revised: However, directly proving (3) could be difficult because both m k,i and v k,i distort the trajectory.

Original: To start with, we try to control the movement of v k,i .
Revised: To start with, we try to control the movement of v k,i .

Original: We use a standard approach for Adam-type algorithms: use large or increasing β 2 (e.g. (Shi et al., 2020;
Revised: We use a standard approach for Adam-type algorithms: use large or increasing β 2 (e.g. (Shi et al., 2020;

Original: Zou et al., 2019; Chen et al., 2018b)).
Revised: Zou et al., 2019; Chen et al., 2018b)).

Original: Recall v k,i = (1 − β 2 ) (cid:80) ij =1 β i − j 2 ∇ f τ k,j ( x k,j ) ◦ ∇ f τ k,j ( x k,j ) +
Revised: Recall v k,i = (1 − β 2 ) (cid:80) ij =1 β i − j 2 ∇ f τ k,j ( x k,j ) ◦ ∇ f τ k,j ( x k,j ) +

Original: β
Revised: β

Original: i 2 v k, 0 , we have v k,i ≈ v k, 0 when β 2 is large.
Revised: i 2 v k, 0 , we have v k,i ≈ v k, 0 when β 2 is large.

Original: In this case, we have:
Revised: In this case, we have:

Original: [Equation]
Revised: [Equation]

Original: where the first “ ≈ " is due to the large β 2 and the second “ ≈ " is our goal.
Revised: where the first “ ≈ " is due to the large β 2 and the second “ ≈ " is our goal.

Original: Now we need to show:
Revised: Now we need to show:

Revised: E

Original: [Equation]
Revised: [Equation]

Original:  √
Revised: f [Equation] x k, 0 ) √

Original: [Equation]
Revised: [Equation]

Original: where ∂ l f ( x k, 0 ) is the l -th component of ∇ f ( x k, 0 ) , similarly for m l,k, 

Revised: where ∂ l f ( x k, 0 ) is the l -th component of ∇ f ( x k, 0 ) , similarly for m l,k,

Original: and v l,k, 0 .
Revised: and v l,k, 0 .

Original: ( ∗ ) is due to thefinite-sum structure.
Revised: ( ∗ ) is due to thefinite-sum structure.

Original: However, it is not easy to prove (4).
Revised: However, it is not easy to prove (4).

Original: We point out some technical issues below.
Revised: We point out some technical issues below.

Original: Issue I: massive momentum.
Revised: Issue I: massive momentum.

Original: Directly proving (4) is still not easy.
Revised: Directly proving (4) is still not easy.

Original: We need to first consider asimplified problem: for every l ∈ [ d ] , assume we treat ∂ l f ( x k, 0 ) / √ v l,k, 0 as a constant, how to boundE [Equation]
Revised: We need to first consider asimplified problem: for every l ∈ [ d ] , assume we treat ∂ l f ( x k, 0 ) / √ v l,k, 0 as a constant, how to bound

Original:  i [Equation] (cid:0) [Equation]
Revised: E (cid:80) n − 1 i =0 (cid:0) m l,k,i − ∂ l f τ k,i ( x k, 0 ) (cid:1) ?

Original: It turns out that this simplified problem is still non-trivial.
Revised: It turns out that this simplified problem is still non-trivial.

Original: When β 1 is large, m l,k,i contains heavyhistorical signals which significantly distort the trajectory from gradient direction.
Revised: When β 1 is large, m l,k,i contains heavyhistorical signals which significantly distort the trajectory from gradient direction.

Original: Existing literatures(Zaheer et al., 2018; De et al., 2018; Shi et al., 2020) take a naive approach: they set β 1 ≈ 

Revised: Existing literature(Zaheer et al., 2018; De et al., 2018; Shi et al., 2020) take a naive approach: they set β 1 ≈

Original: so thatm l,k,i ≈ ∂
Revised: so thatm l,k,i ≈ ∂

Original: l f τ k,i ( x k,i ) .
Revised: l f τ k,i ( x k,i ) .

Original: Then we get (4) ≈ 0 .
Revised: Then we get (4) ≈ 0 .

Original: However, this method cannot be applied here since weare interested in practical cases where β 1 is large in [0 , 1) .
Revised: However, this method cannot be applied here since weare interested in practical cases where β 1 is large in [0 , 1) .

Original: Issue II: stochastic non-linear dynamics.
Revised: Issue II: stochastic non-linear dynamics.

Original: Even if we solve Issue I , it is still unclear how to prove(4).
Revised: Even if we solve Issue I , it is still unclear how to prove(4).

Original: This is because: for every l ∈ [ d ] , ∂ l f ( x k, 0 ) / √ v l,k, 0 is a r.v. instead of a constant.
Revised: This is because: for every l ∈ [ d ] , ∂ l f ( x k, 0 ) / √ v l,k, 0 is a r.v. instead of a constant.

Original: With this terminvolved, we are facing with a stochastic non-linear dynamics, which could be difficult to analyze.
Revised: With this terminvolved, we are facing with a stochastic non-linear dynamics, which could be difficult to analyze.

Original: Further, ∂ l f ( x k, 0 ) / √ v l,k, 0 is statistically dependent with (cid:0) m l,k,i − ∂ l f τ k,i ( x k, 0 ) (cid:1) , so we are notallowed to handle the expectation E ( ∂ l f ( x k, 0 ) / √ v l,k, 0 ) separately .
Revised: Further, ∂ l f ( x k, 0 ) / √ v l,k, 0 is statistically dependent with (cid:0) m l,k,i − ∂ l f τ k,i ( x k, 0 ) (cid:1) , so we are notallowed to handle the expectation E ( ∂ l f ( x k, 0 ) / √ v l,k, 0 ) separately .

Original: Unfortunately, even with additional assumptions like bounded gradient, there is no general approachto tackle the above issues.
Revised: Unfortunately, even with additional assumptions like bounded gradient, there is no general approachto tackle the above issues.

Original: In this work, we propose solutions regardless of gradient magnitude.
Revised: In this work, we propose solutions regardless of gradient magnitude.

Original: Solution to Issue I.
Revised: Solution to Issue I.

Original: We prove the following Lemma to resolve Issue I .
Revised: We prove the following Lemma to resolve Issue I .

Original: Lemma 5.1.
Revised: Lemma 5.1.

Original: (Informal) Consider Algorithm 1.
Revised: (Informal) Consider Algorithm 1.

Original: For every l ∈ [ d ] and any β 1 ∈ [0 , 1) , we have the 339 following result under Assumption 2.1.
Revised: For every l ∈ [ d ] and any β 1 ∈ [0 , 1) , we have the 340 following result under Assumption 2.1.

Original: [Equation]
Revised: [Equation]

Original: where ∂ l f ( x k, 0 ) is the l -th component of ∇ f ( x k, 0 ) ; m l,k,i
Revised: where ∂ l f ( x k, 0 ) is the l -th component of ∇ f ( x k, 0 ) ; m l,k,i

Original: = (1 − β 1 ) ∂ l f τ k,i ( x k,i ) + β 1 m l,k,i − 1 .
Revised: = (1 − β 1 ) ∂ l f τ k,i ( x k,i ) + β 1 m l,k,i − 1 .

Original: We present the proof idea in Appendix A. Simply put, we construct a simple toy example called“color-ball" model (of the 1st kind).
Revised: We present the proof idea in Appendix A. Simply put, we construct a simple toy example called“color-ball" model (of the 1st kind).

Original: This toy model shows a special property of δ ( β 1 ) .
Revised: This toy model shows a special property of δ ( β 1 ) .

Original: We find out:for Algorithm 1, error terms from successive epochs can be canceled, which keeps the momentumroughly in the descent direction.
Revised: We find out:for Algorithm 1, error terms from successive epochs can be canceled, which keeps the momentumroughly in the descent direction.

Original: This important property is not revealed in any existing work.
Revised: This important property is not revealed in any existing work.

Original: Remark 4: When assuming bounded gradient ∥∇ f ( x ) ∥ ≤ G , a naive upper bound would beδ ( β 1 ) =
Revised: Remark 4: When assuming bounded gradient ∥∇ f ( x ) ∥ ≤ G , a naive upper bound would beδ ( β 1 ) =

Original: O ( G ) .
Revised: O ( G ) .

Original: However, such constant upper bound does not imply δ ( β 1 ) is close to 0.
Revised: However, such constant upper bound does not imply δ ( β 1 ) is close to 0.

Original: It will nothelp prove the convergence.
Revised: It will nothelp prove the convergence.

Original: This might be partially the reason why large- β 1 Adam is hard to analyzeeven under bounded gradient (see related works in Section 2.2).
Revised: This might be partially the reason why large- β 1 Adam is hard to analyzeeven under bounded gradient (see related works in Section 2.2).

Original: We emphasize Lemma 5.1 holds trueregardless of gradient norm, so it could be deployed in both bounded or unbounded gradient analysis.
Revised: We emphasize Lemma 5.1 holds trueregardless of gradient norm, so it could be deployed in both bounded or unbounded gradient analysis.

Original: Solution to Issue II.
Revised: Solution to Issue II.

Original: We try to show (4) by adopting Lemma 5.1.
Revised: We try to show (4) by adopting Lemma 5.1.

Original: However, the direct applicationcannot work since ∂ l f ( x k, 0 ) √ v l,k, 0 is not constant.
Revised: However, the direct applicationcannot work since ∂ l f ( x k, 0 ) √ v l,k, 0 is not constant.

Original: Despite its randomness, we find out that when β 2 islarge, (cid:110) ∂
Revised: Despite its randomness, we find out that when β 2 islarge, (cid:110) ∂

Original: l f ( x k, 0 ) √ v l,k, 0 (cid:111) ∞
Revised: l f ( x k, 0 ) √ v l,k, 0 (cid:111) ∞

Original: k =1 behaves like Cauchy sequence which has unique limit point.
Revised: k =1 behaves like Cauchy sequence which has unique limit point.

Original: As such, although∂ l f ( x k, 0 ) √ v l,k, 0 brings extra perturbation, the quantity in (4) share the similar asymptotic behavior as δ ( β 1 ) .
Revised: As such, although∂ l f ( x k, 0 ) √ v l,k, 0 brings extra perturbation, the quantity in (4) share the similar asymptotic behavior as δ ( β 1 ) .

Original: We prove the following Lemma 5.2.
Revised: We prove the following Lemma 5.2.

Original: Lemma 5.2.
Revised: Lemma 5.2.

Original: (Informal) Under Assumption 2.1 and 2.2, consider Algorithm 1 with large β 2 andβ 1 < √ β 2 .
Revised: (Informal) Under Assumption 2.1 and 2.2, consider Algorithm 1 with large β 2 andβ 1 < √ β 2 .

Original: For those l with gradient component larger than certain threshold, we have:
Revised: For those l with gradient component larger than certain threshold, we have:

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: In Appendix A, we introduce how to derive (6) from (5).
Revised: In Appendix A, we introduce how to derive (6) from (5).

Original: To do so, we introduce a new type of“color-ball" model (we call it color-ball of the 2nd kind) which adopts the random perturbation of∂ l f ( x k, 0 ) √ v l,k, 0 .
Revised: To do so, we introduce a new type of“color-ball" model (we call it color-ball of the 2nd kind) which adopts the random perturbation of∂ l f ( x k, 0 ) √ v l,k, 0 .

Original: Understanding color-ball model of the 2nd kind is crucial for proving Lemma 5.2.
Revised: Understanding color-ball model of the 2nd kind is crucial for proving Lemma 5.2.

Original: We conclude the proof of (4) by some additional analysis on “those l with small gradient component".
Revised: We conclude the proof of (4) by some additional analysis on “those l with small gradient component".

Original: This case is a bit easier since it reduces to bounded gradient case.
Revised: This case is a bit easier since it reduces to bounded gradient case.

Original: For readers who wants to learn moreabout the idea of tackling Issue I and II , please refer to Appendix A where we formally introduce the1st and 2nd kind of color-ball models.
Revised: For readers who wants to learn moreabout the idea of tackling Issue I and II , please refer to Appendix A where we formally introduce the1st and 2nd kind of color-ball models.

Original: Since the whole proof is quite long, we provide a proof sketchin Appendix G.1.
Revised: Since the whole proof is quite long, we provide a proof sketchin Appendix G.1.

Original: The whole proof is presented in Appendix G.
Revised: The whole proof is presented in Appendix G.

Original:  Conclusions
Revised:  Conclusions

Original: In this work, we explore the (non-)convergence of Adam.
Revised: In this work, we explore the (non-)convergence of Adam.

Original: When β 2 is large, we prove that Adamcan converge with any β 1 < √ β 2 .
Revised: When β 2 is large, we prove that Adamcan converge with any β 1 < √ β 2 .

Original: When β 2 is small, we further show that Adam might diverge toinfinity for a wide range of β 1 .
Revised: When β 2 is small, we further show that Adam might diverge toinfinity for a wide range of β 1 .

Original: One interesting question is to verify the advantage of Adam over
Revised: One interesting question is to verify the advantage of Adam over

Original: SGD.
Revised: SGD.

Original: In this work, we focus on the fundamental issue of convergence.
Revised: In this work, we focus on the fundamental issue of convergence.

Original: Proving faster convergence of
Revised: Proving faster convergence of

Original: Adam would be our future work.
Revised: Adam would be our future work.

Original: Appendix Organization
Revised: Appendix Organization

Original: The Appendix is organized as follows.
Revised: The Appendix is organized as follows.

Original: • Appendix A introduces two “color-ball" models and their applications in proving Lemma 5.

Revised: • Appendix A introduces two “color-ball" models and their applications in proving Lemma 5.

Original: andLemma 5.2.
Revised: andLemma 5.2.

Original: This part is important for proving Theorem 3.1.• Appendix B introduces experiments to support our theory.• Appendix C provide some suggestions for hyperparameter tuning of Adam.• Appendix D.1 provide more discussions on some recent related works.
Revised: This part is important for proving Theorem 3.1.• Appendix B introduces experiments to support our theory.• Appendix C provide some suggestions for hyperparameter tuning of Adam.• Appendix D.1 provide more discussions on some recent related works.

Original: Appendix D.2 re-state thenon-convergence results in (Reddi et al., 2018). • Appendix E provides detailed proof for Proposition 3.3.• Appendix F provides some more notations and technical lemmas that serve for the proof of
Revised: Appendix D.2 re-state thenon-convergence results in (Reddi et al., 2018).• Appendix E provides detailed proof for Proposition 3.3.• Appendix F provides some more notations and technical lemmas that serve for the proof of

Original: Theorem 3.1.
Revised: Theorem 3.1.

Original: • Appendix G provides detailed proof for Theorem 3.1.
Revised: • Appendix G provides detailed proof for Theorem 3.1.

Original: Especially, Appendix G.1 provide a proofroadmap.
Revised: Especially, Appendix G.1 provide a proof roadmap.

Original: A Introduction to the “Color-Ball” Models: the Key Ingredients to ProveTheorem 3.1
Revised: A Introduction to the “Color-Ball” Models: the Key Ingredients to ProveTheorem 3.1

Original: We now introduce two “color-ball" models and their applications in tackling Issue I and Issue IImentioned in Section 5.
Revised: We now introduce two “color-ball" models and their applications in tackling Issue I and Issue IImentioned in Section 5.

Original: These two color-ball models are important for proving Theorem 3.1.
Revised: These two color-ball models are important for proving Theorem 3.1.

Original: Solution to Issue I.
Revised: Solution to Issue I.

Original: As discussed in Section 5, we wish to show that δ ( β 1 ) vanishes with k .
Revised: As discussed in Section 5, we wish to show that δ ( β 1 ) vanishes with k .

Original: Formally, we wish to get the following equation (7) for every l ∈ [ d ] .
Revised: Formally, we wish to get the following equation (7) for every l ∈ [ d ] .

Original: [Equation]
Revised: [Equation]

Original: When k is large, O ( β nk 1 ) vanishes faster than O ( 1 √ k ) and thus Lemma 5.1 can be proved.
Revised: When k is large, O ( β nk 1 ) vanishes faster than O ( 1 √ k ) and thus Lemma 5.1 can be proved.

Original: In thefollowing context, we will carefully quantify the mismatch between m l,k,i and the ∂ l f ( x k, 0 ) .
Revised: In thefollowing context, we will carefully quantify the mismatch between m l,k,i and the ∂ l f ( x k, 0 ) .

Original: Wefind out that the error terms from successive epochs can be cancelled, which keeps the momentumroughly in the descent direction.
Revised: Wefind out that the error terms from successive epochs can be cancelled, which keeps the momentumroughly in the descent direction.

Original: To help readers understand our idea, we introduce the “color-ball "model (of the 1st kind) as follows.
Revised: To help readers understand our idea, we introduce the “color-ball "model (of the 1st kind) as follows.

Original: The color-ball model of the 1st kind. Consider a box containing two balls labeled with constantc 0 , c 1 ∈ R , respectively.
Revised: The color-ball model of the 1st kind. Consider a box containing two balls labeled with constantc 0 , c 1 ∈ R , respectively.

Original: In each round (epoch), we randomly sample balls from the box withoutreplacement, then we put them back.
Revised: In each round (epoch), we randomly sample balls from the box withoutreplacement, then we put them back.

Original: We denote the 1st sampled label in the k -th epoch as a k and the2nd sampled one as b k ; a k , b k ∈ { c 0 , c 1 } .
Revised: We denote the 1st sampled label in the k -th epoch as a k and the2nd sampled one as b k ; a k , b k ∈ { c 0 , c 1 } .

Original: We define two random variables m 0 and m 1 as follows(assume β ∈ [0 , 1) ):
Revised: We define two random variables m 0 and m 1 as follows(assume β ∈ [0 , 1) ):

Original: [Equation]
Revised: [Equation]

Original: where m 0 ,k denotes the summand of m 0 in k -th epoch, similarly for m 1 ,k .
Revised: where m 0 ,k denotes the summand of m 0 in k -th epoch, similarly for m 1 ,k .

Original: Note that in each epoch,m 0 and m 1 share the same sample order.
Revised: Note that in each epoch,m 0 and m 1 share the same sample order.

Original: Further, we introduce the following deterministic constants.
Revised: Further, we introduce the following deterministic constants.

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: where f 0 ,k denotes the summand of f 0 in k -th epoch, similarly for f 1 ,k .
Revised: where f 0 ,k denotes the summand of f 0 in k -th epoch, similarly for f 1 ,k .

Original: Now we prove the following
Revised: Now we prove the following

Original: Lemma A.1. Lemma A.1.
Revised: Lemma A.1. Lemma A.1.

Original: In the color-ball model of the 1st kind, we have(cid:12)(cid:12)(cid:12)(cid:12)(cid:12) E (cid:34)
Revised: In the color-ball model of the 1st kind, we have

Original: [Equation]
Revised: [Equation]

Original: (cid:88) i =
Revised: E

Original: [Equation]
Revised: [Equation]

Original: (cid:88) 
Revised: (cid:88) i =

Original: [Equation]
Revised: [Equation]

Original: where the expectation is taken on all the possible draws.
Revised: where the expectation is taken on all the possible draws.

Original: For the color-ball example with n ≥ 2 balls,we have(cid:12)(cid:12)(cid:12)(cid:12)(cid:12) E (cid:34) n − 1 (cid:88) i
Revised: For the color-ball example with n ≥ 2 balls,we have(cid:12)(cid:12)(cid:12)(cid:12)(cid:12) E (cid:34) n − 1 (cid:88) i

Original: = m i − n − 1 (cid:88)
Revised: = m i − n − 1 (cid:88)

Original: [Equation]
Revised: [Equation]

Original: How is Lemma A.1 related to (7) ?
Revised: How is Lemma A.1 related to (7) ?

Original: In this color-ball toy example, m i mimics the possiblerealization of momentum up to the i -th inner loop in k -th epoch.
Revised: In this color-ball toy example, m i mimics the possiblerealization of momentum up to the i -th inner loop in k -th epoch.

Original: f i mimics the stochastic gradient∇
Revised: f i mimics the stochastic gradient∇

Original: f i ( x k, 0 ) .
Revised: f i ( x k, 0 ) .

Original: This is because we can expand ∇ f i ( x k, 0 ) into an infinite-sum sequence ∇ f i ( x k, 0 ) =(1 − β 1 ) ∇ f i ( x k, 0 ) (cid:80) ∞
Revised: This is because we can expand ∇ f i ( x k, 0 ) into an infinite-sum sequence ∇ f i ( x k, 0 ) =(1 − β 1 ) ∇ f i ( x k, 0 ) (cid:80) ∞

Original: j =0 β
Revised: j =0 β

Original: j 1 , which shares a similar structure as f i .
Revised: j 1 , which shares a similar structure as f i .

Original: In this sense, Lemma A.1 mayprovide ideas to prove (7).
Revised: In this sense, Lemma A.1 mayprovide ideas to prove (7).

Original: Nevertheless, there are still gap between these two, we will explain thegap later.
Revised: Nevertheless, there are still gap between these two, we will explain thegap later.

Original: Proof.
Revised: Proof.

Original: We use E k [ · ] to denote the conditional expectation given all the history up to the beginningof k -th epoch.
Revised: We use E k [ · ] to denote the conditional expectation given all the history up to the beginningof k -th epoch.

Original: Since E [ · ] = E [ E k [ · ]] , we first calculate E k (cid:104)(cid:80) 1 i =

Revised: Since E [ · ] = E [ E k [ · ]] , we first calculate E k (cid:104)(cid:80) 1 i =

Original: m i (cid:105) .
Revised: m i (cid:105) .

Original: Since all the history beforek -th epoch is fixed, we relegate this part to later discussion and first focus on the expectation of 528 (cid:80)i =

Revised: Since all the history beforek -th epoch is fixed, we relegate this part to later discussion and first focus on the expectation of 529 (cid:80)i =

Original: m i,k .
Revised: m i,k .

Original: As shown in Figure 4 (upper part), there are 2 possible realization.
Revised: As shown in Figure 4 (upper part), there are 2 possible realization.

Original: With the help of Figure 4 (upper part), we have the following result. 
Revised: With the help of Figure 4 (upper part), we have the following result. E k [Equation] (cid:88)

Revised: i

Original: [Equation]
Revised: [Equation]

Revised: m

Revised: [Equation]

Revised: −

Revised: [Equation]

Revised: (cid:88) i = f

Original:  Now we move one step further to calculate E k − 1 E k (cid:104)(cid:80) 1 i =

Revised: [Equation] (cid:35) [Equation] Now we move one step further to calculate E k − 1 E k (cid:104)(cid:80) 1 i =

Original: m i − (cid:80) 1 i =

Revised: m i − (cid:80) 1 i =

Original: f i (cid:105) .
Revised: f i (cid:105) .

Original: Using the similarstrategy as (8), we have the following result.
Revised: Using the similarstrategy as (8), we have the following result.

Original: The calculation is illustrated in Figure 4 (lower part)and Figure 5.
Revised: The calculation is illustrated in Figure 4 (lower part)and Figure 5.

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation] 
Revised: [Equation] [Figure] [Equation] [Figure]

Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation] 
Revised: [Equation] [Figure] [Equation] [Figure]

Revised: (cid:88)

Original: [Figure]
Revised: [Figure]

Original: (cid:88)
Revised: (cid:88)

Original: [Figure]
Revised: [Figure]

Original: (cid:35)(cid:41)
Revised: (cid:35)(cid:41)

Original: [Equation]
Revised: [Equation]

Original: [Figure][Figure] [Equation]
Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: We observe that only the highest order term remains in the calculation.
Revised: We observe that only the highest order term remains in the calculation.

Original: Repeat this process untilk = 1 , we will get the results in Lemma A.1.
Revised: Repeat this process untilk = 1 , we will get the results in Lemma A.1.

Original: The above analysis also holds for general n ≥ 2 .
Revised: The above analysis also holds for general n ≥ 2 .

Original: The gap between Lemma A.1 and Equation (7) .
Revised: The gap between Lemma A.1 and Equation (7) .

Original: Lemma A.1 shows the key idea of provingequation (7).
Revised: Lemma A.1 shows the key idea of provingequation (7).

Original: However, due to its idealized setting, the color-ball toy example is still far from our realgoal (
Revised: However, due to its idealized setting, the color-ball toy example is still far from our realgoal (

Original: We list some of the gaps here.
Revised: We list some of the gaps here.

Original: • In each possible trajectory: x k,i is changing with k and i , while the balls are fixed in the color-ballexample. • When taking expectation: x is changing in different trajectory, while the balls is fixed in thecolor-ball example.• x is a vector in R d while the label of the balls are constant in R .
Revised: • In each possible trajectory: x k,i is changing with k and i , while the balls are fixed in the color-ballexample.• When taking expectation: x is changing in different trajectory, while the balls is fixed in thecolor-ball example.• x is a vector in R d while the label of the balls are constant in R .

Original: It requires extra technical lemmas to handle these differences.
Revised: It requires extra technical lemmas to handle these differences.

Original: We provide more discussions in
Revised: We provide more discussions in

Original: Appendix G
Revised: Appendix G

Original: Solution to Issue II.
Revised: Solution to Issue II.

Original: We now discuss how to resolve Issue II mentioned in Section 5.
Revised: We now discuss how to resolve Issue II mentioned in Section 5.

Original: The solutioncontains two parts.
Revised: The solutioncontains two parts.

Original: First, we need to prove (5).
Revised: First, we need to prove (5).

Original: Second, we derive (6) from (5).
Revised: Second, we derive (6) from (5).

Original: Due to the limitedspace, we relegate the first part to Appendix G.1 (related to Lemma G.2).
Revised: Due to the limitedspace, we relegate the first part to Appendix G.1 (related to Lemma G.2).

Original: Now, we discuss the secondpart: Assume we have (5), how do we use it to prove (6)?
Revised: Now, we discuss the secondpart: Assume we have (5), how do we use it to prove (6)?

Original: To answer this question, we introduce thecolor-ball model of the 2nd kind.
Revised: To answer this question, we introduce thecolor-ball model of the 2nd kind.

Original: The color-ball model of the 2nd kind. Consider the same setting as the color-ball model in Step 1 .
Revised: The color-ball model of the 2nd kind. Consider the same setting as the color-ball model in Step 1 .

Original: We define a sequence of real random variable { r j } kj =1 with the following relation.
Revised: We define a sequence of real random variable { r j } kj =1 with the following relation.

Original: [Equation]
Revised: [Equation]

Original: Further, we assume r j is fixed when fixing the history up to j -th round.
Revised: Further, we assume r j is fixed when fixing the history up to j -th round.

Original: The sequence { r j } kj =1mimics the sequence { ∂ l f ( x k, 0 ) √ v l,k, 0 } ∞ k =1 in (5).
Revised: The sequence { r j } kj =1mimics the sequence { ∂ l f ( x k, 0 ) √ v l,k, 0 } ∞ k =1 in (5).

Original: Now
Revised: Now

Original: [Equation]
Revised: [Equation]

Original: we consider the following quantities.
Revised: we consider the following quantities.

Original: [Equation]
Revised: [Equation]

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: k −
Revised: k −

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: ( k −
Revised: ( k −

Original: [Equation]
Revised: [Equation]

Original:  β 
Revised: + β 2( k − 1)+ (cid:124) (cid:123)(cid:122) (cid:125) f 1 ,

Original: [Equation]
Revised: [Equation]

Original: ( k − 1)+ (cid:124) (cid:123)(cid:122) (cid:125) f 1 ,) 

Original:   
Revised:   

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: (cid:125)
Revised: (cid:125)

Original: [Equation]
Revised: [Equation]

Revised: [Equation]

Original: ( k −
Revised: ( k −

Original: [Equation]
Revised: [Equation]

Original: ( k − 1)
Revised: ( k − 1)

Original: [Equation]
Revised: [Equation]

Original: (cid:124) (cid:123)(cid:122) (cid:125) f
Revised: (cid:124) (cid:123)(cid:122) (cid:125) f

Original: [Equation]
Revised: [Equation]

Original: )   ; .
Revised: )   ; .

Original: We now prove the following Lemma A.2.
Revised: We now prove the following Lemma A.2.

Original: Lemma A.2.
Revised: Lemma A.2.

Original: Consider the color-ball model of the 2nd kind, we have
Revised: Consider the color-ball model of the 2nd kind, we have

Original: [Equation]
Revised: [Equation]

Original: For general n = 1 , 2 , 3 , · · · , we have
Revised: For general n = 1 , 2 , 3 , · · · , we have

Original: [Equation]
Revised: [Equation]

Original: Proof.
Revised: Proof.

Original: We only describe the proof idea here.
Revised: We only describe the proof idea here.

Original: The proof contains the following 4 steps.
Revised: The proof contains the following 4 steps.

Original: Step 2.1.
Revised: Step 2.1.

Original: We firstly take E k [ · ] and thus r k can be viewed as a constant.
Revised: We firstly take E k [ · ] and thus r k can be viewed as a constant.

Original: We use the color-ballprocedure as in Figure 6 to calculate E k (cid:104)
Revised: We use the color-ballprocedure as in Figure 6 to calculate E k (cid:104)

Original: [Figure]
Revised: [Figure]

Original: Step 2.2.
Revised: Step 2.2.

Original: We change E k (cid:104) r k (cid:80) 1 i =

Revised: We change E k (cid:104) r k (cid:80) 1 i =

Original: m i,k − 1 (cid:105) into E k (cid:104) r k − 1 (cid:80) 1 i =

Revised: m i,k − 1 (cid:105) into E k (cid:104) r k − 1 (cid:80) 1 i =

Original: m i,k − 1 (cid:105) + Error; where Error =
Revised: m i,k − 1 (cid:105) + Error; where Error =

Original: O (1 / √ k ) .
Revised: O (1 / √ k ) .

Original: Step 2.3.
Revised: Step 2.3.

Original: We calculate E k − 1 E k (cid:104) r k − 1 (cid:80) 1 i =

Revised: We calculate E k − 1 E k (cid:104) r k − 1 (cid:80) 1 i =

Original: m i,k − 1 (cid:105) =
Revised: m i,k − 1 (cid:105) =

Original: E k − 1 (cid:104) r k − 1 (cid:80) 1 i =

Revised: E k − 1 (cid:104) r k − 1 (cid:80) 1 i =

Original: m i,k − 1 (cid:105) .
Revised: m i,k − 1 (cid:105) .

Original: This part isillustrated in the top row in Figure 

Revised: This part isillustrated in the top row in Figure

Original: Step 2.4.
Revised: Step 2.4.

Original: For the leftovers in Step 2.1 , we change all r k into r k − 1 .
Revised: For the leftovers in Step 2.1 , we change all r k into r k − 1 .

Original: Then we do the cancellation tocalculate E k − 1 E k (cid:104)
Revised: Then we do the cancellation tocalculate E k − 1 E k (cid:104)

Original: r k − 1 (cid:80) 1 i =

Revised: r k − 1 (cid:80) 1 i =

Original: m i,k − 1 + r k − 1 (cid:80) 1 i =
 m i,k − r k − 1 (cid:80) 1 i =

Revised: m i,k − 1 + r k − 1 (cid:80) 1 i =
 m i,k − r k − 1 (cid:80) 1 i =

Original: f i (cid:105) .
Revised: f i (cid:105) .

Original: This step is shown inthe second and third row in Figure 7.
Revised: This step is shown inthe second and third row in Figure 7.

Original: Repeat this process to the 1st epoch.
Revised: Repeat this process to the 1st epoch.

Original: We can prove the Lemma A.2.
Revised: We can prove the Lemma A.2.

Original: We emphasize that here are still some gap between Lemma A.2 and our goal in (6) in Lemma 5.2.
Revised: We emphasize that here are still some gap between Lemma A.2 and our goal in (6) in Lemma 5.2.

Original: First, we have the similar gap as discussed at the end of the Solution to Issue I .
Revised: First, we have the similar gap as discussed at the end of the Solution to Issue I .

Original: Second, the conditionin Lemma 5.2 has requirement on the gradient norm, while this requirement is temporarily ignored inthe color-ball method of the 2nd kind.
Revised: Second, the conditionin Lemma 5.2 has requirement on the gradient norm, while this requirement is temporarily ignored inthe color-ball method of the 2nd kind.

Original: It requires some technical lemmas to handle these gaps.
Revised: It requires some technical lemmas to handle these gaps.

Original: For more details, please refer to the complete proof in Appendix G.
Revised: For more details, please refer to the complete proof in Appendix G.

Original: B Experiments
Revised: B Experiments

Original: To support our theory, we provide more simulations and real-data experiments.
Revised: To support our theory, we provide more simulations and real-data experiments.

Original: All the experimentalsettings and hyperparameters are presented in Appendix B.2.
Revised: All the experimentalsettings and hyperparameters are presented in Appendix B.2.

Original: We aim to show:(I).
Revised: We aim to show:(I

Original: When β 2 is large, a large range of β 1 gives good performance, including all β 1 < √ β 2 .(II).
Revised: When β 2 is large, a large range of β 1 gives good performance, including all β 1 < √ β 2 . II)

Original: When β 2 is small, a large range of β 1 performs relatively badly.
Revised: When β 2 is small, a large range of β 1 performs relatively badly.

Original: [Figure]
Revised: [Figure]

Original: [Figure][Figure]
Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: g r a d i e n t n o r m
Revised: g r a d i e n t n o r m

Original: [Figure]
Revised: [Figure]

Original: Counter-example (2) .
Revised: Counter-example (2) .

Original: We run more experiments on (2) with initialization x = − 5 and n =5 , 10 , 15 , 20 .
Revised: We run more experiments on (2) with initialization x = − 5 and n =5 , 10 , 15 , 20 .

Original: We show the result of n = 20 in Figure 8 (a), (b); the rest are shown in Appendix B.1.
Revised: We show the result of n = 20 in Figure 8 (a), (b); the rest are shown in Appendix B.1.

Original: We find that: first, when β 2 is small, gradient norms increase dramatically.
Revised: We find that: first, when β 2 is small, gradient norms increase dramatically.

Original: This emphasizes the importance of discarding bounded gradient assumptions.
Revised: This emphasizes the importance of discarding bounded gradient assumptions.

Original: Second, when β 2 is large, Adam convergesto stationary points for β 1 < √ β 2 .
Revised: Second, when β 2 is large, Adam convergesto critical points for β 1 < √ β 2 .

Original: This matches our theory since (2) satisfies D 0 = 0 .
Revised: This matches our theory since (2) satisfies D 0 = 0 .

Original: For functionswith D 0 > 0 , we further show that Adam can only converge to a bounded region in Appendix B.1.
Revised: For functionswith D 0 > 0 , we further show that Adam can only converge to a bounded region in Appendix B.1.

Original: MNIST and CIFAR-10.
Revised: MNIST and CIFAR-10.

Original: As shown in Figure 1 (b)& (c) in Section 1, the training results matchboth claim (I) and (II).
Revised: As shown in Figure 1 (b)& (c) in Section 1, the training results matchboth claim (I) and (II).

Original: In addition, there is a convex-shaped boundary on the transition from low lossto higher loss, this boundary roughly matches the condition in Theorem 3.1.
Revised: In addition, there is a convex-shaped boundary on the transition from low lossto higher loss, this boundary roughly matches the condition in Theorem 3.1.

Original: NLP.
Revised: NLP.

Original: We use Adam to train Transformer XL (Dai et al., 2019) on the WikiText-103 dataset (Merityet al., 2016).
Revised: We use Adam to train Transformer XL (Dai et al., 2019) on the WikiText-103 dataset (Merityet al., 2016).

Original: This architecture and dataset is widely used in NLP tasks (e.g. (Howard & Ruder, 2018;
Revised: This architecture and dataset is widely used in NLP tasks (e.g. (Howard & Ruder, 2018;

Original: See et al., 2017)).
Revised: See et al., 2017)).

Original: As shown in Figure 8 (c), the training results match both claim (I) and (II).
Revised: As shown in Figure 8 (c), the training results match both claim (I) and (II).

Original: Batchsize and β 2 .
Revised: Batchsize and β 2 .

Original: As shown in Figure 8 (d): on MNIST, smaller batchsize requires larger β 2 toreach small loss.
Revised: As shown in Figure 8 (d): on MNIST, smaller batchsize requires larger β 2 toreach small loss.

Original: Since batchsize equals to (number of total sample)/(number of batches).
Revised: Since batchsize equals to (number of total sample)/(number of batches).

Original: In thecontext of finite-sum setting with n summand, n usually stands for the number of batches (e.g., Inthe extreme case when batchsize = 1 , n equals to the number of total samples).
Revised: In thecontext of finite-sum setting with n summand, n usually stands for the number of batches (e.g., Inthe extreme case when batchsize = 1 , n equals to the number of total samples).

Original: Therefore, smallerbatchsize brings larger n .
Revised: Therefore, smallerbatchsize brings larger n .

Original: As such, Figure 8 (d) matches the message by Theorem 3.1: the thresholdof β 2 increases with n .
Revised: As such, Figure 8 (d) matches the message by Theorem 3.1: the thresholdof β 2 increases with n .

Original: B.1 More experiments
Revised: B.1 More experiments

Original: Estimation on ρ in Theorem 3.1.
Revised: Estimation on ρ in Theorem 3.1.

Original: To ensure convergence, Theorem 3.1 requires β 2 ≥ γ 1 ( n ) =1 − O ((1 − β n 1 ) /n 2 ρ ) .
Revised: To ensure convergence, Theorem 3.1 requires β 2 ≥ γ 1 ( n ) =1 − O ((1 − β n 1 ) /n 2 ρ ) .

Original: Now we estimate the constant ρ .
Revised: Now we estimate the constant ρ .

Original: According to our definition in Remark G.7in Appendix G.1, ρ = ρ 1 ρ 2 ρ 3 , where ρ 1 , ρ 2 , ρ 3 are defined as follows.
Revised: According to our definition in Remark G.7in Appendix G.1, ρ = ρ 1 ρ 2 ρ 3 , where ρ 1 , ρ 2 , ρ 3 are defined as follows.

Original: [Equation]
Revised: [Equation]

Original: These definitions can also be seen in Appendix F.1.
Revised: These definitions can also be seen in Appendix F.1.

Original: In worst case, we have 0 ≤ ρ 3 ≤ √ nρ 1 ≤ n .
Revised: In worst case, we have 0 ≤ ρ 3 ≤ √ nρ 1 ≤ n .

Original: However, ρ is highly dependent on the problem instance f ( x ) and training process.
Revised: However, ρ is highly dependent on the problem instance f ( x ) and training process.

Original: We now estimatehow ρ changes with Adam’s trajectory on MNIST and CIFAR-10.
Revised: We now estimatehow ρ changes with Adam’s trajectory on MNIST and CIFAR-10.

Original: We use β 1 = 0 .
Revised: We use β 1 = 0 .

Original: 9 , β 2 = 0 .
Revised: 9 , β 2 = 0 .

Original: On both datasets, we set batchsize to be 64, which brings n = 937 on MNIST and n = 781 on
Revised: On both datasets, we set batchsize to be 64, which brings n = 937 on MNIST and n = 781 on

Original: CIFAR-10.
Revised: CIFAR-10.

Original: We collect ρ 1 , ρ 2 , ρ 3 along the training process and estimate their distribution density.
Revised: We collect ρ 1 , ρ 2 , ρ 3 along the training process and estimate their distribution density.

Original: The results are shown in Figure 9 and 10.
Revised: The results are shown in Figure 9 and 10.

Original: [Figure]
Revised: [Figure]

Original: On both CIFAR-10 and MNIST, we observe that the maximal ρ 1 < 25 ≈ O ( √ n ) , ρ 2 < 

Revised: On both CIFAR-10 and MNIST, we observe that the maximal ρ 1 < 25 ≈ O ( √ n ) , ρ 2 <

Original: ≈ O ( n ) ,ρ 3 < 0 .
Revised: ≈ O ( n ) ,ρ 3 < 0 .

Original: 1 ≈ O (1 / √ n ) .
Revised: 1 ≈ O (1 / √ n ) .

Original: Therefore, ρ = ρ 1 ρ 2 ρ 3 ≈ O ( n ) .
Revised: Therefore, ρ = ρ 1 ρ 2 ρ 3 ≈ O ( n ) .

Original: [Figure]
Revised: [Figure]

Original: More experiments on function (2) .
Revised: More experiments on function (2) .

Original: Figure 1 (a) shows the optimality gap after 50k iterationswhen n = 10 and initialization x = 1 .
Revised: Figure 1 (a) shows the optimality gap after 50k iterationswhen n = 10 and initialization x = 1 .

Original: Here, we provide more relevant experiments.
Revised: Here, we provide more relevant experiments.

Original: First, wheninitialized at x = 1 , we run experiments with n = 5 , 10 , 15 , 20 .
Revised: First, wheninitialized at x = 1 , we run experiments with n = 5 , 10 , 15 , 20 .

Original: The results are shown in Figure 11.
Revised: The results are shown in Figure 11.

Original: We observe that the blue region shrinks as n increases.
Revised: We observe that the blue region shrinks as n increases.

Original: This matches our conclusion in Theorem 3.1:when n increases, the convergent threshold of β 2 increases, which means we need larger β 2 to ensureconvergence.
Revised: This matches our conclusion in Theorem 3.1:when n increases, the convergent threshold of β 2 increases, which means we need larger β 2 to ensureconvergence.

Original: It also matches the conclusion in Theorem 3.3: when n increases, the divergence regionwill expand (more evidence can be seen in Appendix E).
Revised: It also matches the conclusion in Theorem 3.3: when n increases, the divergence regionwill expand (more evidence can be seen in Appendix E).

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure][Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: n

Original: [Figure]
Revised: [Figure]

Original: When initializing at x = − 5 , we further demonstrate that the gradient norm of f ( x ) can dramaticallyincrease.
Revised: When initializing at x = − 5 , we further demonstrate that the gradient norm of f ( x ) can dramaticallyincrease.

Original: All the setting is the same as that in Figure 11 except for the change of initialization.
Revised: All the setting is the same as that in Figure 11 except for the change of initialization.

Original: Starting at x = − 5 , the algorithm can touch the “quadratic" side of function (2) where the gradient isunbounded.
Revised: Starting at x = − 5 , the algorithm can touch the “quadratic" side of function (2) where the gradient isunbounded.

Original: The results are shown in Figure 12.
Revised: The results are shown in Figure 12.

Original: We observe a similar pattern as that in Figure 11.
Revised: We observe a similar pattern as that in Figure 11.

Original: Asa result, the gradient norm of f ( x ) is large in the left bottom corner.
Revised: Asa result, the gradient norm of f ( x ) is large in the left bottom corner.

Original: We further plot the change of gradient norm along the iterations.
Revised: We further plot the change of gradient norm along the iterations.

Original: We pick β 1 = 0 .
Revised: We pick β 1 = 0 .

Original: 1 and β 2 =0 , 0 . 1 , 0 . 3 , 0 . 5 , 0 .
Revised: 1 and β 2 =0 , 0 . 1 , 0 . 3 , 0 . 5 , 0 .

Original: 99 to see the phase transition when increasing β 2 .
Revised: 99 to see the phase transition when increasing β 2 .

Original: The result is shown in
Revised: The result is shown in

Original: Figure 13.
Revised: Figure 13.

Original: When β 2 is small, the gradient norm of f ( x ) increases rapidly along the iteration.
Revised: When β 2 is small, the gradient norm of f ( x ) increases rapidly along the iteration.

Original: Mostof them are even much larger than the upper bound of color bar in Figure 12.
Revised: Mostof them are even much larger than the upper bound of color bar in Figure 12.

Original: As a result, there is aphase transition from diverge to converge when increasing β 2 from 0 to 1.
Revised: As a result, there is aphase transition from diverge to converge when increasing β 2 from 0 to 1.

Original: [Figure]
Revised: [Figure]

Original: Adam converges to a exact stationary point under SGC where D 0 = 0 .
Revised: Adam converges to a exact critical point under SGC where D 0 = 0 .

Original: Since function (2)satisfies SGC, we further provide empirical evidence that Adam converges to a exact stationary pointunder SGC.
Revised: Since function (2) satisfies SGC, we further provide empirical evidence that Adam converges to a exact critical point under SGC.

Original: We use function (2) n = 20 and initialization x = − 5 .
Revised: We use function (2) n = 20 and initialization x = − 5 .

Original: We choose some large enough β [-
-]
Revised: We choose some large enough β [-
-]2 to ensure

Original: [Figure]
Revised: [Figure]

Original: to ensure the convergence. As shown in Figure14, We observe 0 gradient norm after Adam converges.
Revised: As shown in Figure

Original: All the hyperparameter setting is the same as before.
Revised: All thehyperparameter setting is the same as before.

Original: [Figure]
Revised: [Figure]

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: Adam converges to a bounded region when D 0 > 0 .
Revised: Adam converges to a bounded region when D 0 > 0 .

Original: Now we show that Adam may not convergeto an exact stationary point when D 0 > 0 .
Revised: Now we show that Adam may not convergeto an exact critical point when D 0 > 0 .

Original: Instead, it converges to a bounded region near thestationary point.
Revised: Instead, it converges to a bounded region near the criticalpoint.

Original: For this part, we re-state the example from (Shi et al., 2020).
Revised: For this part, we re-state the example from (Shi et al., 2020).

Original: Consider the followingfunction.
Revised: Consider the following function.

Original: [Equation]
Revised: [Equation]

Original: Summing up f j  x ) we get
Revised: Summing up f j ( x ) we get

Original: [Equation]
Revised: [Equation]

Original: This f ( x ) is lower bounded by − a 2 9 with optimal solution x ∗ = 0 .
Revised: This f ( x ) is lower bounded by − a 2 9 with optimal solution x ∗ = 0 .

Original: At the optimal point x ∗ = 0 ,∇ f j ( x ∗ ) ̸ = 

Revised: At the optimal point x ∗ = 0 ,∇ f j ( x ∗ ) ̸ =

Original: so we have D 0 > 0 .
Revised: so we have D 0 > 0 .

Original: When running Algorithm 1 on this function, we observe that Adamwith diminishing stepsize does not converge to 0 gradient norm.
Revised: When running Algorithm 1 on this function, we observe that Adamwith diminishing stepsize does not converge to 0 gradient norm.

Original: Instead, it converge to a boundedregion.
Revised: Instead, it converge to a boundedregion.

Original: Further, the size of the region shrinks when β 2 increases.
Revised: Further, the size of the region shrinks when β 2 increases.

Original: These phenomena matches ourdiscussion in Remark 3.
Revised: These phenomena matches ourdiscussion in Remark 3.

Original: The result is shown in Figure 15.
Revised: The result is shown in Figure 15.

Original: In the experiment, we choose β 1 = 0 .
Revised: In the experiment, we choose β 1 = 0 .

Original: 9 ,a = 3 , x 

Revised: 9 ,a = 3 , x

Original: = − 2 and diminishing stepsize η k = 0 .
Revised: = − 2 and diminishing stepsize η k = 0 .

Original: 1 √ k .
Revised: 1 √ k .

Original: B.2 Experimental Settings
Revised: B.2 Experimental Settings

Original: Here, we introduce our experimental settings.
Revised: Here, we introduce our experimental settings.

Original: • Experiments on function (2) .
Revised: • Experiments on function (2) .

Original: We use Algorithm 1 with cyclic order f 0 , f 1 , f 2 and so on. 
Revised: We use Algorithm 1 with cyclic order f 0 , f 1 , f 2 and so on. We

Original: Wereport the optimality gap x − x ∗ after 50k iteration, or equivalently 50000 /n epochs
Revised: report the optimality gap x − x ∗ after 50k iteration, or equivalently 50000 /n epochs.

Original: We useϵ =
Revised: We use

Original:  10 − 8 for numerical stability.
Revised: ϵ = 10 − 8 for numerical stability.

Original: We use diminishing stepsize η k = 0 .
Revised: We use diminishing stepsize η k = 0 .

Original: 1 / √ k , where k is the indexof epoch.
Revised: 1 / √ k , where k is the indexof epoch.

Original: Unless otherwise stated, this setting applies to all the other experiments on function (2).
Revised: Unless otherwise stated, this setting applies to all the other experiments on function (2).

Original: In Figure 1 (a), we use n = 10 and initialization x = 1 .
Revised: In Figure 1 (a), we use n = 10 and initialization x = 1 .

Original: We will report more results with differentn and different initialization in Appendix B.1.
Revised: We will report more results with differentn and different initialization in Appendix B.1.

Original: Instead, it only converge to a bounded region near the point
Revised: Instead, it only converge to a bounded region near the critical

Original: • MNIST (Deng, 2012).
Revised: • MNIST (Deng, 2012).

Original: We use one-hidden-layer neural network with width =16.
Revised: We use one-hidden-layer neural network with width =16.

Original: We set batchsize
Revised: We set batchsize

Original: =1, weight decay =0, stepsize =0.0001 and train for 20 epochs.
Revised: =1, weight decay =0, stepsize =0.0001 and train for 20 epochs.

Original: We use ϵ = 10 − 8 for numericalstability.• CIFAR-10 (Krizhevsky et al., 2009).
Revised: We use ϵ = 10 − 8 for numericalstability.• CIFAR-10 (Krizhevsky et al., 2009).

Original: We use ResNet-18 (He et al., 2016) as the architecture.
Revised: We use ResNet-18 (He et al., 2016) as the architecture.

Original: Wechoose batchsize =16, weight decay =5e-4 , initial stepsize=1e-3.
Revised: Wechoose batchsize =16, weight decay =5e-4 , initial stepsize=1e-3.

Original: We use a stage-wise constantlearning rate scheduling with a multiplicative factor of 0.1 on epoch 30, 60 and 90.
Revised: We use a stage-wise constantlearning rate scheduling with a multiplicative factor of 0.1 on epoch 30, 60 and 90.

Original: We useϵ =
Revised: We useϵ =

Original: 10 − 8 for numerical stability.
Revised: 10 − 8 for numerical stability.

Original: For MNIST and CIFAR-10, larger batchsize will bring similar pattern as that in Figure 1, but thephase transition will occur at some smaller β 2 .
Revised: For MNIST and CIFAR-10, larger batchsize will bring similar pattern as that in Figure 1, but thephase transition will occur at some smaller β 2 .

Original: • NLP.
Revised: • NLP.

Original: The WikiText-103 dataset is a collection of over 100 million tokens extracted from the setof verified ‘Good’ and ‘Featured’ articles on Wikipedia.
Revised: The WikiText-103 dataset is a collection of over 100 million tokens extracted from the setof verified ‘Good’ and ‘Featured’ articles on Wikipedia.

Original: The base model of Transformer XLcontains 16 self-attention layers.
Revised: The base model of Transformer XLcontains 16 self-attention layers.

Original: In each self-attention layer, there are 10 heads.
Revised: In each self-attention layer, there are 10 heads.

Original: The encodingdimension of each head is set to be 41.
Revised: The encodingdimension of each head is set to be 41.

Original: We set batchsize = 60, number of iteration =
Revised: We set batchsize = 60, number of iteration =

Original: 200k, andinitial stepsize = 0.00025.
Revised: 200k, andinitial stepsize = 0.00025.

Original: We use cosine learning rate scheduler, which is a popular choice fortraining Transformer.
Revised: We use cosine learning rate scheduler, which is a popular choice fortraining Transformer.

Original: We use ϵ = 10 − 8 for numerical stability.
Revised: We use ϵ = 10 − 8 for numerical stability.

Original: C Some Potential Implications for Practical Users
Revised: C Some Potential Implications for Practical Users

Original: Theorem 3.1 and Proposition 3.3 establish a clearer image on the relation between ( β 1 , β 2 ) and qualitative behavior of Adam, which may have certain implication for practical users.
Revised: Theorem 3.1 and Proposition 3.3 establish a clearer image on the relation between ( β 1 , β 2 ) and qualitative behavior of Adam, which may have certain implication for practical users.

Original: Many practitionersare still doing grid or random search over ( β 1 , β 2 ) , which could be costly.
Revised: Many practitionersare still doing grid or random search over ( β 1 , β 2 ) , which could be costly.

Original: The following advice maycut down a large portion of search space.
Revised: The following advice maycut down a large portion of search space.

Original: Suppose we start with some random point ( β ∗ 1 , β ∗ 2 ) , weprovide the following suggestions for tuning β 1 and β 2 .
Revised: Suppose we start with some random point ( β ∗ 1 , β ∗ 2 ) , weprovide the following suggestions for tuning β 1 and β 2 .

Original: Case 1: If ( β ∗ 1 , β ∗ 2 ) lies above the blue curve in Figure 2.
Revised: Case 1: If ( β ∗ 1 , β ∗ 2 ) lies above the blue curve in Figure 2.

Original: We point out two sub-cases.
Revised: We point out two sub-cases.

Original: First, if
Revised: First, if

Original: Adam with ( β ∗ 1 , β ∗ 2 ) diverges, then any points with “ β 1 < √ β 2 " and “ β 2 < β ∗ 2 " shall not be trieddue to the risk of divergence.
Revised: Adam with ( β ∗ 1 , β ∗ 2 ) diverges, then any points with “ β 1 < √ β 2 " and “ β 2 < β ∗ 2 " shall not be trieddue to the risk of divergence.

Original: Second, if Adam with ( β ∗ 1 , β ∗ 2 ) converges, then any points above thispoint will converge.
Revised: Second, if Adam with ( β ∗ 1 , β ∗ 2 ) converges, then any points above thispoint will converge.

Original: You may either fix β 1 = β ∗ 1 and increase β 2 , or fix β 2 = β ∗ 2 and try smaller β 1 .
Revised: You may either fix β 1 = β ∗ 1 and increase β 2 , or fix β 2 = β ∗ 2 and try smaller β 1 .

Original: Both ways have convergence guarantee.
Revised: Both ways have convergence guarantee.

Original: Case 2: If ( β ∗ 1 , β ∗ 2 ) lies below the blue curve in Figure 2.
Revised: Case 2: If ( β ∗ 1 , β ∗ 2 ) lies below the blue curve in Figure 2.

Original: We still discuss two sub-cases.
Revised: We still discuss two sub-cases.

Original: First, ifyou observe divergence at ( β ∗ 1 , β ∗ 2 ) , then do not further try any point on the left.
Revised: First, ifyou observe divergence at ( β ∗ 1 , β ∗ 2 ) , then do not further try any point on the left.

Original: We do not suggestexploring the points in below either, since the majority of them will still face the risk of divergence.
Revised: We do not suggestexploring the points in below either, since the majority of them will still face the risk of divergence.

Original: Instead, we suggest fix β 1 = β ∗ 1 and increase β 2 .
Revised: Instead, we suggest fix β 1 = β ∗ 1 and increase β 2 .

Original: Since our Theorem 3.1 applies for any β 1 < √ β 2 ,algorithm will converge when β 2 is large enough.
Revised: Since our Theorem 3.1 applies for any β 1 < √ β 2 ,algorithm will converge when β 2 is large enough.

Original: Second, if you observe convergence at ( β ∗ 1 , β ∗ 2 ) (which is also possible according to Figure 1 (a)),then we suggest: (i) either fix β 1 = β ∗ 1 and increase β 2 , or (ii) fix β 2 = β ∗ 2 and try smaller β 1 .
Revised: Second, if you observe convergence at ( β ∗ 1 , β ∗ 2 ) (which is also possible according to Figure 1 (a)),then we suggest: (i) either fix β 1 = β ∗ 1 and increase β 2 , or (ii) fix β 2 = β ∗ 2 and try smaller β 1 .

Original: Bothways push ( β 1 , β 2 ) into the region of Theorem 3.1 with convergence guarantee.
Revised: Bothways push ( β 1 , β 2 ) into the region of Theorem 3.1 with convergence guarantee.

Original: D More Discussion on Related Works.D.1 More Related Works on the Convergence Analysis of Adam Family
Revised: D More Discussion on Related Works.D.1 More Related Works on the Convergence Analysis of Adam Family

Original: New variants of Adam.
Revised: New variants of Adam.

Original: Ever since Reddi et al.
Revised: Ever since Reddi et al.

Original: (2018) pointed out the non-convergence issueof Adam, one active line of work has tried to design new variants of Adam that can be proved toconverge.
Revised: (2018) pointed out the non-convergence issue of Adam, one active line of work has tried to design new variants of Adam that can be proved to converge.

Original: For instance, Zou et al.
Revised: For instance, Zou et al.

Original: (2019); Gadat & Gavra (2020)
Revised: (2019); Gadat & Gavra (2020)

Original: ; Chen et al.
Revised: ; Chen et al.

Original: (2018b, 2021) replacethe constant hyperparameters by iterate-dependent ones e.g. β 1 t or β 2 t .
Revised: (2018b, 2021) replace the constanthyperparameters by iterate-dependent ones e.g. β 1 t or β 2 t .

Original: AMSGrad (Reddi et al.,2018) and AdaFom (Chen et al., 2018b) modify { v t } to be an non-decreasing sequence.
Revised: AMSGrad (Reddi et al., 2018) and AdaFom (Chen et al., 2018b) modify { v t } to be an non-decreasing sequence.

Original: Similarly, AdaBound (Luo et al., 2019) impose lower and upper bounds on { v t } to prevent the effective stepsizefrom vanishing or exploding.
Revised: Similarly, AdaBound(Luo et al., 2019) impose lower and upper bounds on { v t } to prevent the effective stepsize fromvanishing or exploding.

Original: Zhou et al.
Revised: Zhou et al.

Original: (2018b) also adopt a new estimate of v t to correct thebias.
Revised: (2018b) also adopt a new estimate of v t to correct the bias.

Original: There are also attempts to combine Adam with Nesterov momentum (Dozat, 2016) as well aswarm-up techniques (Liu et al., 2020a).
Revised: Thereare also attempts to combine Adam with Nesterov momentum (Dozat, 2016) as well as warm-uptechniques (Liu et al., 2020a).

Original: Padam (Chen et al., 2018a) also introduce a partial adaptiveparameter to improve the generalization performance.
Revised: Padam (Chen et al., 2018a) also introduce a partial adaptive parameterto improve the generalization performance.

Original: As reported in the literature, these variantsoften reach good performance in many applications.

Original: There are also some works providing theoreticalanalysis on the variants of Adam.
Revised: There are also some works providing theoretical analysison the variants of Adam.

Original: For instance, Zhou et al.
Revised: For instance, Zhou et al.

Original: (2018a) study the convergence of AdaGradand AMSGrad with zero and non-zero β 1 under bounded gradient condition.
Revised: (2018a) study the convergence of AdaGrad and AMSGrad under bounded gradient condition.

Original: Gadat & Gavra (2020)study the asymptotic behavior of a subclass of adaptive gradient methods from landscape point ofview.
Revised: Gadat & Gavra (2020) study the asymptotic behaviorof a subclass of adaptive gradient methods from landscape point of view.

Original: Their analysis applies to Adam-variants with β 1 = 0 and β 2 increasing along the iterates (itcould also be understood as RMSProp with increasing β 2 ).
Revised: Their analysis appliesto Adam-variants with β 1 = 0 and β 2 increasing along the iterates (it could also be understood as RMSProp with increasing β 2 ).

Original: When this script is under review, a newwork (Iiduka, 2022) appear on line.
Revised: When this script is under review, a new work (Iiduka, 2022) appearon line.

Original: Iiduka (2022) analyze the convergence of AMSGrad by relaxingthe Lipschitz-gradient condition.
Revised: Iiduka (2022) analyze the convergence of AMSGrad by relaxing the Lipschitz-gradientcondition.

Original: However, their analysis requires extra conditions on bothboundedgradient and bounded domain.
Revised: However, their analysis requires extra conditions on both bounded gradient and boundeddomain.

Original: Some more discussions on (Guo et al., 2021) and (Huang et al., 2021).
Revised: Some more discussions on (Guo et al., 2021) and (Huang et al., 2021).

Original: Here, we discuss more ontwo recent works Guo et al.
Revised: Here, we discuss more ontwo recent works Guo et al.

Original: and Huang et al.
Revised: and Huang et al.

Original: As mentioned in Section 2.2, they bothrequire some extra conditions.
Revised: As mentioned in Section 2.2, they bothrequire some extra conditions.

Original: First, both Guo et al.
Revised: First, both Guo et al.

Original: and Huang et al.
Revised: and Huang et al.

Original: (2021) requires boundedgradient assumption.
Revised: (2021) requires boundedgradient assumption.

Original: This can be seen in Assumption 2 in (Guo et al., 2021).
Revised: This can be seen in Assumption 2 in (Guo et al., 2021).

Original: In (Huang et al.
Revised: In (Huang et al.

Original: , 2021),they require bounded iterates ( their Theorem 1) or change Adam into AdaBound (Luo et al.
Revised: , 2021),they require bounded iterates ( their Theorem 1) or change Adam into AdaBound (Luo et al.

Original: , 2019)by clipping (their Remark 2, Corollary 1).
Revised: , 2019)by clipping (their Remark 2, Corollary 1).

Original: Both settings inherent boundedness on gradient.
Revised: Both settings inherent boundedness on gradient.

Original: Besides bounded gradient, both (Huang et al., 2021) and (Guo et al., 2021) requires 1 / ( √ v t + ϵ ) ≤ C u .
Revised: Besides bounded gradient, both (Huang et al., 2021) and (Guo et al., 2021) requires 1 / ( √ v t + ϵ ) ≤ C u .

Original: This condition is stated in Assumption 2 in (Guo et al., 2021) and Assumption 3 in (Huang et al.,2021)
Revised: This condition is stated in Assumption 2 in (Guo et al., 2021) and Assumption 3 in (Huang et al.,2021)

Original: (they presented it as H t ⪰ ρI d ≻ 0 , where matrix H t = diag ( √ v t + ϵ ) ).
Revised: (they presented it as H t ⪰ ρI d ≻ 0 , where matrix H t = diag ( √ v t + ϵ ) ).

Original: Combining these twoconditions, the effective stepsize of Adam will be bounded in certain interval 1 √ v t + ϵ ∈
Revised: Combining these twoconditions, the effective stepsize of Adam will be bounded in certain interval 1 √ v t + ϵ ∈

Original: [ C l , C u ] .
Revised: [ C l , C u ] .

Original: Suchboundedness condition changes Adam into AdaBound (Luo et al., 2019) and thus cannot explain theobservations of Adam in Section 1.
Revised: Suchboundedness condition changes Adam into AdaBound (Luo et al., 2019) and thus cannot explain theobservations of Adam in Section 1.

Original: D.2 A Brief Introduction to (Reddi et al., 2018)
Revised: D.2 A Brief Introduction to (Reddi et al., 2018)

Original: Here, we re-state two counter examples by (Reddi et al., 2018).
Revised: Here, we re-state two counter examples by (Reddi et al., 2018).

Original: For the consistence of notation, wewill re-state their results under our notation in the full script.
Revised: For the consistence of notation, wewill re-state their results under our notation in the full script.

Original: They consider the convex problem((Reddi et al., 2018)): min (cid:80) n − 1 i =

Revised: They consider the convex problem((Reddi et al., 2018)): min (cid:80) n − 1 i =

Original: f i ( x ) where x ∈ [ − 1 , 1] , n ≥ 3 :f i ( x ) = (cid:26) nx, for i =− x, otherwise, (9)
Revised: f i ( x ) where x ∈ [ − 1 , 1] , n ≥ 3 :f i ( x ) = (cid:26) nx, for i =− x, otherwise, (9)

Original: Note that (9) satisfy both Assumption 2.1 and 2.2 (with D 1 = n 2 + n − 1 and D 0 = 0 ), so ourassumptions do not rule out this counter-example a priori.
Revised: Note that (9) satisfy both Assumption 2.1 and 2.2 (with D 1 = n 2 + n − 1 and D 0 = 0 ), so ourassumptions do not rule out this counter-example a priori.

Original: This is a constrained problem with feasibleset x ∈ [ − 1 , 1] , the optimal solution is x ∗ = − 1 .
Revised: This is a constrained problem with feasibleset x ∈ [ − 1 , 1] , the optimal solution is x ∗ = − 1 .

Original: Since they consider constrained problems, theirclaimed “divergence" actually means the iterates will stay in a huge region with the size of wholefeasible set.
Revised: Since they consider constrained problems, theirclaimed “divergence" actually means the iterates will stay in a huge region with the size of wholefeasible set.

Original: Here, we call it “non-convergence" to distinguish from our result of “diverge to infinity"in Proposition 3.3.
Revised: Here, we call it “non-convergence" to distinguish from our result of “diverge to infinity"in Proposition 3.3.

Original: They consider sampling f i in the cyclic order: f 0 , f 1 , f 2 .
Revised: They consider sampling f i in the cyclic order: f 0 , f 1 , f 2 .

Original: In (Reddi et al., 2018), Function (9) ispresented as an “online optimization problem with non-zero average regret".
Revised: In (Reddi et al., 2018), Function (9) ispresented as an “online optimization problem with non-zero average regret".

Original: We choose to use theform of (9) since it is more consistent with our notation in Algorithm 1.
Revised: We choose to use theform of (9) since it is more consistent with our notation in Algorithm 1.

Original: We re-state their results asfollows.
Revised: We re-state their results asfollows.

Original: Theorem D.1 (Theorem 2 in (Reddi et al., 2018)) .
Revised: Theorem D.1 (Theorem 2 in (Reddi et al., 2018)) .

Original: For any fixed ( β 1 , β 2 ) s.t.
Revised: For any fixed ( β 1 , β 2 ) s.t.

Original: β 1 < √ β 2 , there existsfunction (9) with large enough n , s.t.
Revised: β 1 < √ β 2 , there existsfunction (9) with large enough n , s.t.

Original: Adam will converge to highly sub-optimal solution x = 1 .
Revised: Adam will converge to highly sub-optimal solution x = 1 .

Original: We briefly re-state the non-convergent condition for this Theorem.
Revised: We briefly re-state the non-convergent condition for this Theorem.

Original: As stated in Equation (7), Appendix
Revised: As stated in Equation (7), Appendix

Original: B in (Reddi et al., 2018), for every fixed ( β 1 , β 2 ) , they need a “constant n that depends on β 1 and β 2 ".
Revised: B in (Reddi et al., 2018), for every fixed ( β 1 , β 2 ) , they need a “constant n that depends on β 1 and β 2 ".

Original: As such, they require different n to cause non-convergence on different ( β 1 , β 2 ) .
Revised: As such, they require different n to cause non-convergence on different ( β 1 , β 2 ) .

Original: So the consideredfunction class is constantly changing.
Revised: So the consideredfunction class is constantly changing.

Original: For completeness, we further re-state Theorem 1 in (Reddi et al., 2018).
Revised: For completeness, we further re-state Theorem 1 in (Reddi et al., 2018).

Original: Theorem D.2 (Theorem 1 in (Reddi et al., 2018)) .
Revised: Theorem D.2 (Theorem 1 in (Reddi et al., 2018)) .

Original: For function (9) , when β 1 = 0 and β 2 = 1 / ( n 2 +1) ,
Revised: For function (9) , when β 1 = 0 and β 2 = 1 / ( n 2 +1) ,

Original: Adam will converge to highly sub-optimal solution x = 1 .
Revised: Adam will converge to highly sub-optimal solution x = 1 .

Original: This theorem considers choosing ( β 1 , β 2 ) after n .
Revised: This theorem considers choosing ( β 1 , β 2 ) after n .

Original: However, this result only show non-convergence ona single point ( β 1 , β 2 ) =
Revised: However, this result only show non-convergence ona single point ( β 1 , β 2 ) =

Original: (0 , 1 / ( n 2 + 1)) .
Revised: (0 , 1 / ( n 2 + 1)) .

Original: This point lies somewhere on the left boundary of Figure3.
Revised: This point lies somewhere on the left boundary of Figure3.

Original: It seems unclear how Adam’s behavior would change as we change the ( β 1 , β 2 ) to anywhere else.
Revised: It seems unclear how Adam’s behavior would change as we change the ( β 1 , β 2 ) to anywhere else.

Original: E Proof of Proposition 3.3
Revised: E Proof of Proposition 3.3

Original: We restate our counter-example here.
Revised: We restate our counter-example here.

Original: Consider f ( x ) = (cid:80) n − 1 i =

Revised: Consider f ( x ) = (cid:80) n − 1 i =

Original: f i ( x ) for x ∈ R , we define f i ( x ) asfollows:[Equation] x < [Equation]
Revised: f i ( x ) for x ∈ R , we define f i ( x ) asfollows:

Original: for

Original: [Equation]
Revised: [Equation]

Original: Summing up all the f i ( x ) , we can see thatf ( x ) =
Revised: Summing up all the f i ( x ) , we can see thatf ( x ) =

Original: (cid:26) x, x ≥ −12 ( x + 2) 2 − 32 , x < −is a convex smooth function with optimal solution x ∗ = − 2 and optimal value f ( x ∗ ) = − 3 / 2 .
Revised: (cid:26) x, x ≥ −12 ( x + 2) 2 − 32 , x < −is a convex smooth function with optimal solution x ∗ = − 2 and optimal value f ( x ∗ ) = − 3 / 2 .

Original: However, we are going to show that, for any fixed n > 2 , there exists an orange region shown in
Revised: However, we are going to show that, for any fixed n > 2 , there exists an orange region shown in

Original: Figure 2, s.t., Adam with any ( β 1 , β 2 ) combination in the yellow region diverge to x = ∞ ratherthan the optimal solution x = − 2 , causing the divergence.
Revised: Figure 2, s.t., Adam with any ( β 1 , β 2 ) combination in the yellow region diverge to x = ∞ ratherthan the optimal solution x = − 2 , causing the divergence.

Original: Now we introduce the formal statement of
Revised: Now we introduce the formal statement of

Original: Proposition 3.3.
Revised: Proposition 3.3.

Original: Proposition E.1.
Revised: Proposition E.1.

Original: (Formal statement of Proposition 3.3.) Consider the convex function (10) for afixed n .
Revised: (Formal statement of Proposition 3.3.) Consider the convex function (10) for afixed n .

Original: Starting at the initialization x = 1 and initial stepsize η 1 , the iterates of Adam diverge toinfinity if the following holds:
Revised: Starting at the initialization x = 1 and initial stepsize η 1 , the iterates of Adam diverge toinfinity if the following holds:

Original: [Equation]
Revised: [Equation]

Original: The analysis is motivated by that in (Reddi et al., 2018, Theorem 1).
Revised: The analysis is motivated by that in (Reddi et al., 2018, Theorem 1).

Original: However, (Reddi et al., 2018,
Revised: However, (Reddi et al., 2018,

Original: Theorem 1) considers a simplified case with β 1 = 0 .
Revised: Theorem 1) considers a simplified case with β 1 = 0 .

Original: Here, we consider non-zero β 1 , especially forthose β 1 > √ β 2 .
Revised: Here, we consider non-zero β 1 , especially forthose β 1 > √ β 2 .

Original: To show the divergence, we aim to prove the following claim: (we denote x k,i asthe value of x at the k -th outer loop and i -th inner loop )
Revised: To show the divergence, we aim to prove the following claim: (we denote x k,i asthe value of x at the k -th outer loop and i -th inner loop )

Original: Claim: for any fixed n > 2 , there exists an orange region shown in Figure 2 s.t., Adam with anyβ 1 - β 2 combination in the orange regiongives
Revised: Claim: for any fixed n > 2 , there exists an orange region shown in Figure 2 s.t., Adam with anyβ 1 - β 2 combination in the orange region

Original: Since the gradient stays constant when x > 1 , so x will go to infinity if the claim holds.
Revised: Since the gradient stays constant when x > 1 , so x will go to infinity if the claim holds.

Original: To prove thisclaim, we only need to analyze the trajectory of Adam within one particular outer loop, e.g., the k -thouter loop.
Revised: To prove thisclaim, we only need to analyze the trajectory of Adam within one particular outer loop, e.g., the k -thouter loop.

Original: We will show that x k +1 , 0 > 1 if this outer loop is initialized with x k, 0 = 1 .
Revised: We will show that x k +1 , 0 > 1 if this outer loop is initialized with x k, 0 = 1 .

Original: Similarly as(Reddi et al., 2018), we assume f i ( x ) are sampled in the order of f 0 ( x ) , f 1 ( x ) , · · · , f n − 1 ( x ) withinthe k -th outer loop.
Revised: Similarly as(Reddi et al., 2018), we assume f i ( x ) are sampled in the order of f 0 ( x ) , f 1 ( x ) , · · · , f n − 1 ( x ) withinthe k -th outer loop.

Original: Now let us prove the claim.
Revised: Now let us prove the claim.

Original: For function (10), the update rule of Adam is shown as follows.
Revised: For function (10), the update rule of Adam is shown as follows.

Original: [Equation]
Revised: [Equation]

Original: where
Revised: where

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: We decompose the total movement (cid:80) n − 1 i =

Revised: We decompose the total movement (cid:80) n − 1 i =

Original: δ k,i into three terms as follows.
Revised: δ k,i into three terms as follows.

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: [Table]
Revised: [Table]

Original: [Equation]
Revised: [Equation]

Original: We will show that for some β 1 and β 2 : ( I ) , ( II ) > 0 and ( III ) < 0 .
Revised: We will show that for some β 1 and β 2 : ( I ) , ( II ) > 0 and ( III ) < 0 .

Original: However, ( I ) and ( II )outweigh ( III ) , causing the divergence.
Revised: However, ( I ) and ( II )outweigh ( III ) , causing the divergence.

Original: First, we show that m k − 1 ,n − 1 < 0 when β 1 is small.
Revised: First, we show that m k − 1 ,n − 1 < 0 when β 1 is small.

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: when β 1 is small,we have (1 − β n − 1) > (1 − β 1 ) β n − 1n , which implies − m k − 1 ,n − 1 > 0 .
Revised: when β 1 is small,we have (1 − β n − 1) > (1 − β 1 ) β n − 1n , which implies − m k − 1 ,n − 1 > 0 .

Original: For thesechoices of β 1 , we have ( I ) > 0 .
Revised: For thesechoices of β 1 , we have ( I ) > 0 .

Original: Now we derive a lower bound for ( II ) .
Revised: Now we derive a lower bound for ( II ) .

Original: [Equation]
Revised: [Equation]

Original: The inequality is due to the fact that v k, 0 ≤ n 2 .
Revised: The inequality is due to the fact that v k, 0 ≤ n 2 .

Original: Since β j 2 n 2 is small when β 2 is small and j is closeto n , there exists some small β 2 such that β j 2 n 2 ≤ 0 .
Revised: Since β j 2 n 2 is small when β 2 is small and j is closeto n , there exists some small β 2 such that β j 2 n 2 ≤ 0 .

Original: 1 for at least one j < n .
Revised: 1 for at least one j < n .

Original: For these small enough β 2 , we keep the summand with j ≥ log β 2 (0 .
Revised: For these small enough β 2 , we keep the summand with j ≥ log β 2 (0 .

Original: 1 /n 2 ) and drop the rest.
Revised: 1 /n 2 ) and drop the rest.

Original: We have the following lowerbound for ( II ) .
Revised: We have the following lowerbound for ( II ) .

Original: [Equation]
Revised: [Equation]

Original: However, this lower bound only holds for the small β 2 .
Revised: However, this lower bound only holds for the small β 2 .

Original: With simple modification, we derive auniversal lower bound of ( II ) for any β 2 ∈ (0 , 1) .
Revised: With simple modification, we derive auniversal lower bound of ( II ) for any β 2 ∈ (0 , 1) .

Original: [Equation]
Revised: [Equation]

Original: Now we derive a lower bound for ( III ) .
Revised: Now we derive a lower bound for ( III ) .

Original: [Equation]
Revised: [Equation]

Original: The remaining step is to show for small enough step size η 1 , the iterates will stay in the linear region,thus the above gradient holds for all iterates in the trajectory.
Revised: The remaining step is to show for small enough step size η 1 , the iterates will stay in the linear region,thus the above gradient holds for all iterates in the trajectory.

Original: As we initial x as x 0 = 1 , if for all m < n and k > 0 , (cid:80) mi =0 δ k,i ≥ − 2 , we can conclude all iteratesin the trajectory stay in the linear region.
Revised: As we initial x as x 0 = 1 , if for all m < n and k > 0 , (cid:80) mi =0 δ k,i ≥ − 2 , we can conclude all iteratesin the trajectory stay in the linear region.

Original: Because it holds that m k − 1 ,n − 1 ≤ 0 and v k − 1 ,n − 1 ≥ 0 , we have the following result after droppingmost negative terms in the definition of δ k,i :
Revised: Because it holds that m k − 1 ,n − 1 ≤ 0 and v k − 1 ,n − 1 ≥ 0 , we have the following result after droppingmost negative terms in the definition of δ k,i :

Original: [Equation]
Revised: [Equation]

Original: Therefore, to make (cid:80) m i =

Revised: Therefore, to make (cid:80) m i =

Original: δ k,i ≥
Revised: δ k,i ≥

Original: [Equation]
Revised: [Equation]

Original: mi =0 − η
Revised: mi =0 − η

Original: [Equation]
Revised: [Equation]

Original: 1 − β
Revised: 1 − β

Original: [Equation]
Revised: [Equation]

Original: β
Revised: β

Original: [Equation]
Revised: [Equation]

Original: 1 √
Revised: 1 √

Original: [Equation]
Revised: [Equation]

Original: 1 −
Revised: 1 −

Original: [Equation]
Revised: [Equation]

Original:  β n 2 ≥ − 2 , we have
Revised: ) β n 2 ≥ − 2 , we have

Original: [Equation]
Revised: [Equation]

Original: To show the divergence, we want to show that there exists some β 1 and β 2 s.t.
Revised: To show the divergence, we want to show that there exists some β 1 and β 2 s.t.

Original: the both of thefollowing conditions hold:
Revised: the both of thefollowing conditions hold:

Original: [Equation]
Revised: [Equation]

Original: The proof of Theorem 3.3 is completed.
Revised: The proof of Theorem 3.3 is completed.

Original: With the help of Python, we visualize the region where( C1 ) and ( C2 ) hold.
Revised: With the help of Python, we visualize the region where( C1 ) and ( C2 ) hold.

Original: The results are shown in Figure 16.
Revised: The results are shown in Figure 16.

Original: We use orange color to indicate the regionwhere ( C1 ) holds.
Revised: We use orange color to indicate the regionwhere ( C1 ) holds.

Original: White color is used for the counter part.
Revised: White color is used for the counter part.

Original: As for ( C2 ) , we use the gray verticalline to indicate the line where (1 − β n − 1) =
Revised: As for ( C2 ) , we use the gray verticalline to indicate the line where (1 − β n − 1) =

Original: (1 − β 1 ) β n − 1n .
Revised: (1 − β 1 ) β n − 1n .

Original: Note that there are two solutions tothis equation: one solution is β 1 = 1 and the other solution lies in 0 < β 1 < 1 , this is why there aretwo vertical lines in the figure.
Revised: Note that there are two solutions tothis equation: one solution is β 1 = 1 and the other solution lies in 0 < β 1 < 1 , this is why there aretwo vertical lines in the figure.

Original: ( C2 ) holds on the left hand side of the left gray vertical line.0.
Revised: ( C2 ) holds on the left hand side of the left gray vertical line.0.

Original: 0.8 beta 0.
Revised: 0.8 beta 0.

Original: b e t a(a) n =0.
Revised: b e t a(a) n =0.

Original: 0.8 beta 0.
Revised: 0.8 beta 0.

Original: b e t a(b) n =0.
Revised: b e t a(b) n =0.

Original: 0.8 beta 0.
Revised: 0.8 beta 0.

Original: b e t a(c) n =0.
Revised: b e t a(c) n =0.

Original: 0.8 beta 0.
Revised: 0.8 beta 0.

Original: b e t a(d) n =
Revised: b e t a(d) n =

Original: The intersection of two regions will be the region where Adam diverges, which is actually the orangeregion in Figure 16.
Revised: The intersection of two regions will be the region where Adam diverges, which is actually the orangeregion in Figure 16.

Original: As n increases, ( C2 ) holds for a wide range of β 1 , so the grey vertical linesmove towards β 1 = 1 and finally get overlapped.
Revised: As n increases, ( C2 ) holds for a wide range of β 1 , so the grey vertical linesmove towards β 1 = 1 and finally get overlapped.

Original: In addition, the size of divergence region increaseswith n
Revised: In addition, the size of divergence region increaseswith n

Original: Relation with γ 1 ( n ) in Theorem 3.1 According to Theorem 3.1, γ 1 ( n ) is at least in the order of1 − O ( n − 2 ) .
Revised: Relation with γ 1 ( n ) in Theorem 3.1 According to Theorem 3.1, γ 1 ( n ) is at least in the order of1 − O ( n − 2 ) .

Original: Combining with Figure 16.
Revised: Combining with Figure 16.

Original: It is not hard to see that γ 1 ( n ) is always larger than theupper boundary of the orange region, so there is no contradiction.
Revised: It is not hard to see that γ 1 ( n ) is always larger than theupper boundary of the orange region, so there is no contradiction.

Original: Proof of Corollary 4.1.
Revised: Proof of Corollary 4.1.

Original: For any ( β 1 , β 2 ) ∈ [0 , 1) 2 , condition (C1), (C2) and (C3) can be satisfiedby some sufficiently large n .
Revised: For any ( β 1 , β 2 ) ∈ [0 , 1) 2 , condition (C1), (C2) and (C3) can be satisfiedby some sufficiently large n .

Original: Therefore, Adam will diverge and the proof is concluded.
Revised: Therefore, Adam will diverge and the proof is concluded.

Original: F Some More Notations and Useful Lemmas for Convergence Analysis
Revised: F Some More Notations and Useful Lemmas for Convergence Analysis

Original: F.1 More notations
Revised: F.1 More notations

Original: • We denote x k,i , m k,i , v k,i ∈
Revised: • We denote x k,i , m k,i , v k,i ∈

Original: R
Revised: R

Original: d as the value of x, m, v at the k -th outer loop and i -th inner loop.
Revised: d as the value of x, m, v at the k -th outer loop and i -th inner loop.

Original: Further, we denote x l,k,i , m l,k,i , v l,k,i ∈ R as the l -th component of x k,i , m k,i , v k,i .•
Revised: Further, we denote x l,k,i , m l,k,i , v l,k,i ∈ R as the l -th component of x k,i , m k,i , v k,i .•

Original: We denote η k as the stepsize at the k -th epoch (outer loop).
Revised: We denote η k as the stepsize at the k -th epoch (outer loop).

Original: We will focus mainly on diminishingstep size, especially η k = η 1 √ nk , where n is the number of batches (inner loop).
Revised: We will focus mainly on diminishingstep size, especially η k = η 1 √ nk , where n is the number of batches (inner loop).

Original: • We denote ∂ l f ( x ) =
Revised: • We denote ∂ l f ( x ) =

Original: ∂∂x l f ( x ) , ∂ l f j ( x ) =
Revised: ∂∂x l f ( x ) , ∂ l f j ( x ) =

Original: ∂∂x l f j ( x ) .
Revised: ∂∂x l f j ( x ) .

Original: Further, we will use τ k,i to index the i -thrandomly chosen batch in the k -th epoch.
Revised: Further, we will use τ k,i to index the i -thrandomly chosen batch in the k -th epoch.

Original: In this sense, we denote ∂ l f τ k,i ( x ) as ∂∂x l f τ k,i ( x ) .
Revised: In this sense, we denote ∂ l f τ k,i ( x ) as ∂∂x l f τ k,i ( x ) .

Original: [Table]
Revised: [Table]

Original: • Given an epoch k , we denote α as the index of the coordinate with the greatest gradient:
Revised: • Given an epoch k , we denote α as the index of the coordinate with the greatest gradient:

Original: [Equation]
Revised: [Equation]

Original: • We define ρ 1 , ρ 2 , ρ 3 as constants satisfying the following condition for any l = 1 , · · · , d :
Revised: • We define ρ 1 , ρ 2 , ρ 3 as constants satisfying the following condition for any l = 1 , · · · , d :

Original: [Table]
Revised: [Table]

Original: ρ 1 , ρ 2 , ρ 3 are problem-dependent constants.
Revised: ρ 1 , ρ 2 , ρ 3 are problem-dependent constants.

Original: In worst case, we have 0 ≤ ρ 3 ≤ √ nρ 1 ≤ n .•
Revised: In worst case, we have 0 ≤ ρ 3 ≤ √ nρ 1 ≤ n .•

Original: We define the constant △ x := η 0 √ x L √ d √ 1 − β 2 1 − β 1 1 − β 1 √ β .
Revised: We define the constant △ x := η 0 √ x L √ d √ 1 − β 2 1 − β 1 1 − β 1 √ β .

Original: This constant will be used repeatedly, especiallyin Lemma F.2.•
Revised: This constant will be used repeatedly, especiallyin Lemma F.2.•

Original: We define Q k := △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 .
Revised: We define Q k := △ 1 n √ n √ k 32 √ [Equation] 1 − β 2 ) n β n 2 .

Original: This constant will be used repeatedly.•
Revised: This constant will be used repeatedly.•

Original: We define
Revised: We define

Original: [Equation]
Revised: [Equation]

Original: This constant will be used repeatedly.
Revised: This constant will be used repeatedly.

Original: Note that δ 1 → 0 when β 2 → 1 .• E ( · ) means taking expectation over the whole trajectory.• We will use E k [ · ] as a shorthand for E [ · (cid:12)(cid:12) x k, 0 , x k − 1 ,n − 1 , · · · , x 1 , 0 ] , i.e. the conditional expectation given all the history up to x k, 0 .
Revised: Note that δ 1 → 0 when β 2 → 1 .• E ( · ) means taking expectation over the whole trajectory.• We will use E k [ · ] as a shorthand for E [ · (cid:12)(cid:12) x k, 0 , x k − 1 ,n − 1 , · · · , x 1 , 0 ] , i.e. the conditional expectation given all the history up to x k, 0 .

Original: Similarly, E k − 1 stands for the conditional expectation givenall the history up to x k − 1 , 0 .
Revised: Similarly, E k − 1 stands for the conditional expectation givenall the history up to x k − 1 , 0 .

Original: We further introduce some ‘index’ notation of the stochastic gradients, these notations will be usefulin the proof.
Revised: We further introduce some ‘index’ notation of the stochastic gradients, these notations will be usefulin the proof.

Original: For all the stochastic gradient, we have
Revised: For all the stochastic gradient, we have

Original: • True index: every stochastic gradient has its own true index: f 1 ( · ) , f 2 ( · ) , · · · , f n − 1 ( · ) .
Revised: • True index: every stochastic gradient has its own true index: f 1 ( · ) , f 2 ( · ) , · · · , f n − 1 ( · ) .

Original: All these{ f i ( · ) } n − 1 i =0 are fixed once the optimization problem is formulated.
Revised: All these{ f i ( · ) } n − 1 i =0 are fixed once the optimization problem is formulated.

Revised: Note that

Original: Note that for a fixed x , we have the following relationship between f i and f :
Revised: for a fixed x , we have the following relationship between f i and f :

Original: [Equation]
Revised: [Equation]

Original: • Random-shuffle index: At the k -th epoch, all the stochastic gradients are sampled in the order off τ k, 0 ( · ) , f τ k, 1 ( · ) , · · · , f τ k,n − 1 ( · ) .
Revised: • Random-shuffle index: At the k -th epoch, all the stochastic gradients are sampled in the order off τ k, 0 ( · ) , f τ k, 1 ( · ) , · · · , f τ k,n − 1 ( · ) .

Original: Since Algorithm 1 is sampling without replacement, there is an implicit bijective mapping between{ f i ( · ) } n − 1 i =

Revised: Since Algorithm 1 is sampling without replacement, there is an implicit bijective mapping between{ f i ( · ) } n − 1 i =

Original: and { f τ k,i ( · ) } n − 1 i =0 .
Revised: and { f τ k,i ( · ) } n − 1 i =0 .

Original: Further, for a fixed x , we have the following useful property:n
Revised: Further, for a fixed x , we have the following useful property:n

Original: [Equation]
Revised: [Equation]

Original: • Index for m and v : As shown in Algorithm 1, we denote :
Revised: • Index for m and v : As shown in Algorithm 1, we denote :

Original: [Equation]
Revised: [Equation]

Revised: F.2 Some useful lemmas

Original: We begin with proving some useful lemmas.
Revised: We begin with proving some useful lemmas.

Original: Lemma F.1.
Revised: Lemma F.1.

Original: For any β ̸ = 1 , we have
Revised: For any β ̸ = 1 , we have

Original: [Equation]
Revised: [Equation]

Original: Proof.
Revised: Proof.

Original: Proof only involves basic calculation, we omit the proof here.
Revised: Proof only involves basic calculation, we omit the proof here.

Original: Lemma F.2.
Revised: Lemma F.2.

Original: Under Assumption 2.1, if β 1 < √ β 2 , we have
Revised: Under Assumption 2.1, if β 1 < √ β 2 , we have

Original: [Equation]
Revised: [Equation]

Original: Proof.
Revised: Proof.

Original: We start with bounding | x l,k,i +1 − x l,k,i | .
Revised: We start with bounding | x l,k,i +1 − x l,k,i | .

Original: By the update formula of Adam, we have| x l
Revised: By the update formula of Adam, we have| x l

Original: [Equation]
Revised: [Equation]

Original: Now, by Assumption 2.1, we have
Revised: Now, by Assumption 2.1, we have

Original: [Equation]
Revised: [Equation]

Original: Proof is completed.
Revised: Proof is completed.

Original: Lemma F.3.
Revised: Lemma F.3.

Original: Under Assumption 2.1 and 2.2, for any integer i ∈ [0 , n − 1] , we have the following two results,
Revised: Under Assumption 2.1 and 2.2, for any integer i ∈ [0 , n − 1] , we have the following two results,

Original: [Equation]
Revised: [Equation]

Original: where | ∂ α f ( x k, 0 ) | = max l ∈ [1 ,d ] | ∂ l f ( x k, 0 ) | .
Revised: where | ∂ α f ( x k, 0 ) | = max l ∈ [1 ,d ] | ∂ l f ( x k, 0 ) | .

Original: Proof.
Revised: Proof.

Original: [Equation]
Revised: [Equation]

Original: where ( ∗ ) is because of the definition (see Appendix F.1): ρ 1 is a constant satisfying ρ 1 ≥
Revised: where ( ∗ ) is because of the definition (see Appendix F.1): ρ 1 is a constant satisfying ρ 1 ≥

Original: [Equation]
Revised: [Equation]

Original: ni =
Revised: ni =

Original: [Equation]
Revised: [Equation]

Original: f i
Revised: |

Original: [Equation]
Revised: [Equation]

Original:  x k
Revised: l f i ( x k, 0 ) |

Original: [Equation]
Revised: [Equation]

Original: 0 ) | 2 ; ( ∗∗ ) is due to ∥∇ f ( x k, 0 )
Revised: ( ∗∗ ) is due to ∥∇ f ( x k, 0 )

Original: ∥ 22 ≤ d | ∂ α f ( x k, 0 ) | 2 .
Revised: ∥ 22 ≤ d | ∂ α f ( x k, 0 ) | 2 .

Original: The proof of (23) follows the same procedure as above.
Revised: The proof of (23) follows the same procedure as above.

Original: G Proof of Theorem 3.1G.1 A roadmap of the proof
Revised: G Proof of Theorem 3.1G.1 A roadmap of the proof

Original: Our proof is based on the Descent Lemma:
Revised: Our proof is based on the Descent Lemma:

Original: [Equation]
Revised: [Equation]

Original: The expectation E ( · ) is taken on the whole trajectory.
Revised: The expectation E ( · ) is taken on the whole trajectory.

Original: Summing both sides from the initializationk =
Revised: Summing both sides from the initializationk =

Original: t 0 to k = T , we have the following re-arranged inequality: (usually we set t 0 = 1 .)
Revised: t 0 to k = T , we have the following re-arranged inequality: (usually we set t 0 = 1 .)

Original: [Equation]
Revised: [Equation]

Original: To prove the convergence, we need an upper bound for L 2 E ∥ x k +1 , 0 − x k, 0 ∥ 22 , as well as a lower boundfor E ⟨∇ f
Revised: To prove the convergence, we need an upper bound for L 2 E ∥ x k +1 , 0 − x k, 0 ∥ 22 , as well as a lower boundfor E ⟨∇ f

Original: ( x k, 0 ) , x k, 0 − x k +1 , 0 ⟩ (and such a lower bound should be in the order of 1 √ k E ∥∇ f
Revised: ( x k, 0 ) , x k, 0 − x k +1 , 0 ⟩ (and such a lower bound should be in the order of 1 √ k E ∥∇ f

Original: ( x k, 0 ) ∥ ).
Revised: ( x k, 0 ) ∥ ).

Original: The upper bound for L 2 E ∥ x k +1 , 0 − x k, 

Revised: The upper bound for L 2 E ∥ x k +1 , 0 − x k,

Original: ∥ 22 is relatively easy to get: according to the update rule of
Revised: ∥ 22 is relatively easy to get: according to the update rule of

Original: Adam, this term is in the order of O ( η 2 k m l,k,i /v l,k,i )
Revised: Adam, this term is in the order of O ( η 2 k m l,k,i /v l,k,i )

Original: =
Revised: =

Original: O ( η 2 k ∥∇ f
Revised: O ( η 2 k ∥∇ f

Original: ( x k, 0 ) ∥ / ∥∇ f ( x k, 0 ) ∥ )
Revised: ( x k, 0 ) ∥ / ∥∇ f ( x k, 0 ) ∥ )

Original: =
Revised: =

Original: O ( 1 k ) .
Revised: O ( 1 k ) .

Original: Further, recall (cid:80) Tk = t 0 1 k ≤ log T +1 t 0 , so L 2 E ∥ x k +1 , 0 − x k, 0 ∥ 22 contributes to the log term in Theorem3.1.
Revised: Further, recall (cid:80) Tk = t 0 1 k ≤ log T +1 t 0 , so L 2 E ∥ x k +1 , 0 − x k, 0 ∥ 22 contributes to the log term in Theorem3.1.

Original: The proof is shown in Lemma F.2.
Revised: The proof is shown in Lemma F.2.

Original: However, the lower bound for E ⟨∇ f
Revised: However, the lower bound for E ⟨∇ f

Original: ( x k, 0 ) , x k
Revised: ( x k, 0 ) , x k

Original: [Equation]
Revised: [Equation]

Original: requires sophisticated derivation, we explain as follows.
Revised: requires sophisticated derivation, we explain as follows.

Original: Before taking the expectation, we firstwork on every possible realization of (cid:80) dl
Revised: Before taking the expectation, we firstwork on every possible realization of (cid:80) dl

Original: =1 (cid:80) n − 1 i =

Revised: =1 (cid:80) n − 1 i =

Original: ∂ l f ( x k, 0 ) m l,k,i √ v l,k,i .
Revised: ∂ l f ( x k, 0 ) m l,k,i √ v l,k,i .

Original: We perform the followingdecomposition for every l ∈ [1 , d ] .
Revised: We perform the followingdecomposition for every l ∈ [1 , d ] .

Original: [Equation]
Revised: [Equation]

Original: First of all, we introduce the following Lemma G.1 to further decompose ( a ) and ( b ) .
Revised: First of all, we introduce the following Lemma G.1 to further decompose ( a ) and ( b ) .

Original: The intuitionof this decomposition is as follows: by increasing β 2 , we can control the movement of the movingaverage factor v l,k,i , so the trajectory of Adam is easier to control.
Revised: The intuitionof this decomposition is as follows: by increasing β 2 , we can control the movement of the movingaverage factor v l,k,i , so the trajectory of Adam is easier to control.

Original: Lemma G.1.
Revised: Lemma G.1.

Original: Under Assumption 2.1, for those l satisfying max i | ∂ l f i ( x k, 0 )
Revised: Under Assumption 2.1, for those l satisfying max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k :=
Revised: | ≥ Q k :=

Original: △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 , we have the following lower bound for ( a ) in (25) :
Revised: △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 , we have the following lower bound for ( a ) in (25) :

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where δ 1 = (1 − β 2 )4 nρ 2 β n
Revised: where δ 1 = (1 − β 2 )4 nρ 2 β n

Original: [Equation]
Revised: [Equation]

Original: ρ 2 , ρ 3 are constants satisfying ρ 2 ≥ | max i ∂ l f i ( x k, 0 )
Revised: ρ 2 , ρ 3 are constants satisfying ρ 2 ≥ | max i ∂ l f i ( x k, 0 )

Original: | 2 1 n (cid:80) ni
Revised: | 2 1 n (cid:80) ni

Original: =1 | ∂ l f i ( x k, 0 )
Revised: =1 | ∂ l f i ( x k, 0 )

Original: | 2 ,ρ 3 ≥ |
Revised: | 2 ,ρ 3 ≥ |

Original: [Equation]
Revised: [Equation]

Original: l f i
Revised: l f i

Original: [Equation]
Revised: [Equation]

Original:  k
Revised: x k

Original: [Equation]
Revised: [Equation]

Original: n
Revised: n

Original: [Equation]
Revised: [Equation]

Original: l f i
Revised: l f i

Original: [Equation]
Revised: [Equation]

Original: k
Revised: k

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: | 2 .
Revised: | 2 .

Original: Note that δ 1 → 0 when β 2 → 1 .
Revised: Note that δ 1 → 0 when β 2 → 1 .

Original: Under the same condition, we also have a lower bound for ( b ) in (25) :
Revised: Under the same condition, we also have a lower bound for ( b ) in (25) :

Original: [Equation]
Revised: [Equation]

Original: The proof can be seen in Appendix G.2.
Revised: The proof can be seen in Appendix G.2.

Original: Lemma G.1 decomposes both ( a ) and ( b ) : in the denominator,we approximate v l,k,i (which is changing with i ) by v l,k, 0 (which is fixed).
Revised: Lemma G.1 decomposes both ( a ) and ( b ) : in the denominator,we approximate v l,k,i (which is changing with i ) by v l,k, 0 (which is fixed).

Original: Accordingly, theapproximation error can be controlled by increasing β 2 (since δ 1 → 0 when β 2 → 1 ).
Revised: Accordingly, theapproximation error can be controlled by increasing β 2 (since δ 1 → 0 when β 2 → 1 ).

Original: However,
Revised: However,

Original: Lemma G.1 can only be applied to those l with max i | ∂ l f i ( x k, 0 )
Revised: Lemma G.1 can only be applied to those l with max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k , so we need to discuss thefollowing two cases to complete the whole picture.
Revised: | ≥ Q k , so we need to discuss thefollowing two cases to complete the whole picture.

Original: Case 1: unbounded gradient.
Revised: Case 1: unbounded gradient.

Original: Given x k, 0 , consider those l with max i | ∂ l f i ( x k, 0 )
Revised: Given x k, 0 , consider those l with max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k , we havethe following decomposition:
Revised: | ≥ Q k , we havethe following decomposition:

Original: [Equation]
Revised: [Equation]

Original: ( a ) + (cid:34) ∂
Revised: ( a ) + (cid:34) ∂

Original: l f ( x k, 0 ) n − 1 (cid:88) i = 1 √ v l,k,i ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: l f ( x k, 0 ) n − 1 (cid:88) i = 1 √ v l,k,i ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:35) (cid:124) (cid:123)(cid:122) (cid:125) ( b ) .
Revised: (cid:35) (cid:124) (cid:123)(cid:122) (cid:125) ( b ) .

Original: LemmaG.1 ≥ ∂ l f ( x k, 0 ) 2 √ v l,k,− δ 1 (cid:12)(cid:12)(cid:12)(cid:12) ∂
Revised: LemmaG.1 ≥ ∂ l f ( x k, 0 ) 2 √ v l,k,− δ 1 (cid:12)(cid:12)(cid:12)(cid:12) ∂

Original: l f ( x k, 0 ) √ v l,k, 0 (cid:12)(cid:12)(cid:12)(cid:12) n − 1 (cid:88) i =
Revised: l f ( x k, 0 ) √ v l,k, 0 (cid:12)(cid:12)(cid:12)(cid:12) n − 1 (cid:88) i =

Original: | ∂ l f i ( x k, 0 ) | + ∂ l f ( x k, 0 ) √ v l,k,n − 1 (cid:88) i =
Revised: | ∂ l f i ( x k, 0 ) | + ∂ l f ( x k, 0 ) √ v l,k,n − 1 (cid:88) i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 )) − δ 1 (cid:12)(cid:12)(cid:12)(cid:12) ∂
Revised: ( m l,k,i − ∂ l f i ( x k, 0 )) − δ 1 (cid:12)(cid:12)(cid:12)(cid:12) ∂

Original: l f ( x k, 0 ) √ v l,k, 0 (cid:12)(cid:12)(cid:12)(cid:12) n − 1 (cid:88) i = | m l,k,i − ∂ l f i ( x k, 0 ) |
Revised: l f ( x k, 0 ) √ v l,k, 0 (cid:12)(cid:12)(cid:12)(cid:12) n − 1 (cid:88) i = | m l,k,i − ∂ l f i ( x k, 0 ) |

Original: LemmaG.10 ≥ (cid:20) ∂
Revised: LemmaG [Table] (cid:20) ∂

Revised: l f ( x

Revised: [Table]

Revised: [Table]

Original: l f ( x k, 0 ) 2 √ v l,k,(cid:21) (cid:124) (cid:123)(cid:122) (cid:125) ( a 1 ) − (cid:34) δ(cid:115) 2 ρβ nn− 1 (cid:88) i =
Revised: [Table] √ v l, [Table] ,(cid:21) (cid:124) (cid:123)(cid:122) (cid:125) ( a 1 ) − (cid:34) δ(cid:115) 2 ρβ nn

Revised: [Table]

Original:  | ∂ l f i ( x k, 0 ) | (cid:35) (cid:124) (cid:123)(cid:122) (cid:125) ( a 2 ) 
Revised: i = | ∂ l f i ( x k, 0 ) | (cid:35) (cid:124) (cid:123)(cid:122) (cid:125) ( a 2 ) + (cid:34)

Original: +
Revised: [Equation] [Equation] (cid:35) (cid:124) (cid:123)(cid:122) (cid:125) (

Original: [Equation]
Revised: [Equation]

Original: Case 2: bounded gradient.
Revised: Case 2: bounded gradient.

Original: Given x k, 0 , consider those l with max i | ∂ l f i ( x k, 0 ) | ≤ Q k , the analysisdegenerates to the “bounded gradient” scenario, we have the following lower bound:
Revised: Given x k, 0 , consider those l with max i | ∂ l f i ( x k, 0 ) | ≤ Q k , the analysisdegenerates to the “bounded gradient” scenario, we have the following lower bound:

Original: [Equation]
Revised: [Equation]

Original: where F 1 :=
Revised: where F 1 :=

Original: [Equation]
Revised: [Equation]

Original: n
Revised: n

Original: [Equation]
Revised: [Equation]

Original: n , ( ∗ ) is due to the fact that | ∂ l f ( x k, 0 ) | ≤n max i | ∂ l f i ( x k, 0 ) | ≤ nQ k .
Revised: n , ( ∗ ) is due to the fact that | ∂ l f ( x k, 0 ) | ≤n max i | ∂ l f i ( x k, 0 ) | ≤ nQ k .

Original: Combining Case 1 & 2 together, we have the following result (note that ( a 1 ) , ( a 2 ) , ( b 1 ) , ( b 2 ) varywith l .
Revised: Combining Case 1 & 2 together, we have the following result (note that ( a 1 ) , ( a 2 ) , ( b 1 ) , ( b 2 ) varywith l .

Original: A more precise notation will be ( a 1 ) l , etc.
Revised: A more precise notation will be ( a 1 ) l , etc.

Original: We drop the subscript for brevity).
Revised: We drop the subscript for brevity).

Original: [Equation]
Revised: [Equation]

Original: l large
Revised: l large

Original: [Equation]
Revised: [Equation]

Original: (cid:125) unboundedgradient
Revised: (cid:125) unboundedgradient

Original: [Equation]
Revised: [Equation]

Original: l small
Revised: l small

Original: F
Revised: F

Original: [Equation]
Revised: [Equation]

Original: k
Revised: k

Original: [Equation]
Revised: [Equation]

Original: (cid:125) boundedgradient ≥
Revised: (cid:125) boundedgradient ≥

Original: [Equation]
Revised: [Equation]

Original: l large
Revised: l large

Original: [Equation]
Revised: [Equation]

Original: ) } (cid:124) (cid:123)(cid:122) (cid:125) unboundedgradient − d
Revised: ) } (cid:124) (cid:123)(cid:122) (cid:125) unboundedgradient − d

Original: [Equation]
Revised: [Equation]

Original: (cid:26) F
Revised: (cid:26) F

Original: [Equation]
Revised: [Equation]

Original: √ k (cid:27) (cid:124) (cid:123)(cid:122) (cid:125) boundedgradient ≥   (cid:88)
Revised: √ k (cid:27) (cid:124) (cid:123)(cid:122) (cid:125) boundedgradient ≥   (cid:88)

Original: l large ( a 1 )   +   (cid:88)
Revised: l large ( a 1 )   +   (cid:88)

Original: l large ( b 1 )   −   (cid:88) l large { ( a 2 ) + ( b 2 ) }   − dF 

Revised: l large ( b 1 )   −   (cid:88) l large { ( a 2 ) + ( b 2 ) }   − dF

Original: [Equation]
Revised: [Equation]

Original: where “ l large” stands for the gradient component in Case 1 and “ l small” indicates those in Case2 .
Revised: where “ l large” stands for the gradient component in Case 1 and “ l small” indicates those in Case2 .

Original: We assume “ l large” is not an empty set, otherwise the analysis degenerates to the proof withbounded gradient assumption.
Revised: We assume “ l large” is not an empty set, otherwise the analysis degenerates to the proof withbounded gradient assumption.

Original: Now we take expectation on (29).
Revised: Now we take expectation on (29).

Original: The expectation is taken on all the possible trajectories up to thei -th iteration in k -th epoch.
Revised: The expectation is taken on all the possible trajectories up to thei -th iteration in k -th epoch.

Original: [Equation]
Revised: [Equation]

Original: In the following context, we will discuss how to bound all terms in (30), respectively.
Revised: In the following context, we will discuss how to bound all terms in (30), respectively.

Original: First andforemost, we derive a lower bound for E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .
Revised: First andforemost, we derive a lower bound for E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .

Original: Since E (cid:104)(cid:80) l large ( b 1 ) (cid:105) contains all thehistorical gradient information, it involves great effort to handle it.
Revised: Since E (cid:104)(cid:80) l large ( b 1 ) (cid:105) contains all thehistorical gradient information, it involves great effort to handle it.

Original: We will show that the lower boundof E (cid:104)(cid:80) l large ( b 1 ) (cid:105) will vanish when β 2 is large and k goes to infinity.
Revised: We will show that the lower boundof E (cid:104)(cid:80) l large ( b 1 ) (cid:105) will vanish when β 2 is large and k goes to infinity.

Original: Recall E (cid:104)(cid:80) l large ( b 1 ) (cid:105) =
Revised: Recall E (cid:104)(cid:80) l large ( b 1 ) (cid:105) =

Original: E (cid:104)(cid:80) l large ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Revised: E (cid:104)(cid:80) l large ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: When β 1 is large,m l,k,i contains heavy historical signals.
Revised: When β 1 is large,m l,k,i contains heavy historical signals.

Original: It seems unclear how large E (cid:104)(cid:80) l large ( b 1 ) (cid:105) would bewhen β 1 goes to 1.
Revised: It seems unclear how large E (cid:104)(cid:80) l large ( b 1 ) (cid:105) would bewhen β 1 goes to 1.

Original: Existing literatures (Zaheer et al., 2018; De et al., 2018; Shi et al., 2020)
Revised: Existing literatures (Zaheer et al., 2018; De et al., 2018; Shi et al., 2020)

Original: takea naive approach: they set β 1 ≈ 0 so that m k,i ≈ ∇ f τ k,i ( x k,i ) .
Revised: takea naive approach: they set β 1 ≈ 0 so that m k,i ≈ ∇ f τ k,i ( x k,i ) .

Original: Then we get δ ( β 1 ) ≈ 0 .
Revised: Then we get δ ( β 1 ) ≈ 0 .

Original: However,this naive method cannot be applied here since we are interested in practical cases where β 1 is largein [0 , 1) .
Revised: However,this naive method cannot be applied here since we are interested in practical cases where β 1 is largein [0 , 1) .

Original: We emphasize the following technical difficulties in bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) for anyβ 1 ∈ [0 , 1) :
Revised: We emphasize the following technical difficulties in bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) for anyβ 1 ∈ [0 , 1) :

Original: • Issue (i)
Revised: • Issue (i)

Original: In order to bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) , we need to first know how to bound its simplerversion: E (cid:104)(cid:80) n − 1 i =

Revised: In order to bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) , we need to first know how to bound its simpler version: E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: This term measures the difference between the currentgradient and weighted historical gradients.
Revised: This term measures the difference between the current gradient and weighted historical gradients.

Original: It seems unclear that how large this term could bewhen β 1 goes to 1.• Issue (ii) Even if we can bound E (cid:104)(cid:80) n − 1 i =

Revised: It seems unclear that how large this term could bewhen β 1 goes to 1.• Issue (ii) Even if we can bound E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) , it is still unclear how to bound
Revised: (cid:105) , it is still unclear how to bound

Original: E (cid:104) ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Revised: E (cid:104) ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) , which is further multiplied by a random variable∂ l f ( x k, 0 ) √ v l,k, 0 .
Revised: (cid:105) , which is further multiplied by a random variable∂ l f ( x k, 0 ) √ v l,k, 0 .

Original: • Issue (iii)
Revised: • Issue (iii)

Original: Even if we can bound E (cid:104) ∂
Revised: Even if we can bound E (cid:104) ∂

Original: l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Revised: l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) , it is still different from E (cid:104)(cid:80) l large ( b 1 ) (cid:105) which contains additional operation “ (cid:80) l large " inside the expectation.
Revised: (cid:105) , it is still different from E (cid:104)(cid:80) l large ( b 1 ) (cid:105) which contains additional operation “ (cid:80) l large " inside the expectation.

Original: Note
Revised: Note

Original: that the set “ l large" is a random variable which changes along different trajectories, so there is still non-negligible gap between bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105)
Revised: that the set “ l large" is a random variable which changes along different trajectories, so there is still non-negligible gap between bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105)

Original: andE (cid:104) ∂
Revised: andE (cid:104) ∂

Original: l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Revised: l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: To our best knowledge, there is no general approach to tackle the above issues.
Revised: To our best knowledge, there is no general approach to tackle the above issues.

Original: In the followingcontent, we will overcome difficulties (i), (ii) and (iii) in Step 1, 2 and 3 respectively.
Revised: In the followingcontent, we will overcome difficulties (i), (ii) and (iii) in Step 1, 2 and 3 respectively.

Original: In Step 1, wewill discuss how to bound E (cid:104)(cid:80) n − 1 i =

Revised: In Step 1, wewill discuss how to bound E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) , which is a simplified version of E [( b 1 )] .
Revised: (cid:105) , which is a simplified version of E [( b 1 )] .

Original: Bounding this term will shed light on bounding the whole term E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .
Revised: Bounding this term will shed light on bounding the whole term E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .

Original: Then in Step 2 and3, we will prove several technical lemmas to handle the effect of ∂ l f ( x k, 0 ) √ v l,k, 0 and “ (cid:80) l large ", by whichwe can tackle (ii) and (iii) .
Revised: Then in Step 2 and3, we will prove several technical lemmas to handle the effect of ∂ l f ( x k, 0 ) √ v l,k, 0 and “ (cid:80) l large ", by whichwe can tackle (ii) and (iii) .

Original: Combining all together we can bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .
Revised: Combining all together we can bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .

Original: Bounding
Revised: Bounding

Original: [Equation]
Revised: [Equation]

Original: Step 1.
Revised: Step 1.

Original: We now introduce the key idea of bounding
Revised: We now introduce the key idea of bounding

Original: E (cid:104)(cid:80) n − 1 i =

Revised: E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: In Appendix A, we distill our idea into a toy example called "thecolor-ball model of the 1st kind" and thus we prove Lemma A.1.
Revised: In Appendix A, we distill our idea into a toy example called "thecolor-ball model of the 1st kind" and thus we prove Lemma A.1.

Original: This lemma is crucial for bounding
Revised: This lemma is crucial for bounding

Original: E (cid:104)(cid:80) n − 1 i =

Revised: E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: We refer the readers to Appendix A for more explanation.
Revised: We refer the readers to Appendix A for more explanation.

Original: Lemma A.1 can provide insights in bounding E (cid:104)(cid:80) n − 1 i =

Revised: Lemma A.1 can provide insights in bounding E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) .
Revised: (cid:105) .

Original: However, there isstill certain gap between E (cid:104)(cid:80) n − 1 i =

Revised: However, there isstill certain gap between E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) and the quantities in Lemma A.1.
Revised: (cid:105) and the quantities in Lemma A.1.

Original: Weelaborate as follows:
Revised: Weelaborate as follows:

Original: • To mimic the color-ball example, we need to expand (cid:80) n − 1 i =

Revised: • To mimic the color-ball example, we need to expand (cid:80) n − 1 i =

Original: ∂ l f i ( x k, 0 ) into an infinite sumsequence: (cid:80) n − 1 i =

Revised: ∂ l f i ( x k, 0 ) into an infinite sumsequence: (cid:80) n − 1 i =

Original: ∂
Revised: ∂

Original: l f i ( x k, 0 )(1 − β 1 )(1 + β 1 + · · · β ∞ 1 ) .
Revised: l f i ( x k, 0 )(1 − β 1 )(1 + β 1 + · · · β ∞ 1 ) .

Original: However, (cid:80) n − 1 i =

Revised: However, (cid:80) n − 1 i =

Original: m l,k,i is a finite sumsequence up to the order of β kn .
Revised: m l,k,i is a finite sumsequence up to the order of β kn .

Original: In contrast, both sequences in the color-ball example are “finite sum”.
Revised: In contrast, both sequences in the color-ball example are “finitesum”.

Original: As such, there is an error term caused by “finite sum v.s. infinite sum”.
Revised: As such, there is an error term caused by “finite sum v.s. infinite sum”.

Original: • When taking the expectation, the variable x in each possible trajectory is different.
Revised: • When taking the expectation, the variable x in each possible trajectory is different.

Original: In contrast, inthe color-ball example, { a i } 2 i =0 are fixed in all shuffling order (
Revised: In contrast, inthe color-ball example, { a i } 2 i =0 are fixed in all shuffling order (

Original: so it is much easier to calculate
Revised: so it is much easier to calculate

Original: the expectation
Revised: the expectation

Original: by summing them up
Revised: by summing them up

Original: To mimic the color-ball example, we repeatedly take conditional expectation at the beginning ofeach k -th epoch.
Revised: To mimic the color-ball example, we repeatedly take conditional expectation at the beginning ofeach k -th epoch.

Original: In this way, x k, 0 will be fix.
Revised: In this way, x k, 0 will be fix.

Original: Despite x k,i is still changing across the trajectory,we can transform
Revised: Despite x k,i is still changing across the trajectory,we can transform

Original: x k,
Revised: x k

Original: i into x k, 0 by using Lipschitz property.
Revised: i into x k, 0 by using Lipschitz property.

Original: • In each trajectory of (cid:80) n − 1 i =

Revised: • In each trajectory of (cid:80) n − 1 i =

Original: m l,k,i , the variable x in the summand of m l,k,i varies with k and i ;
Revised: m l,k,i , the variable x in the summand of m l,k,i varies with k and i ;

Original: while the variable in ∂ l f i ( x k, 0 ) is fixed to be x k, 0 .
Revised: while the variable in ∂ l f i ( x k, 0 ) is fixed to be x k, 0 .

Original: In contrast, in the color-ball example, { a i } 2 i =0are the same across the epoch.
Revised: In contrast, in the color-ball example, { a i } 2 i = are the same across the epoch.

Original: To mimic the final step in the color-ball example (Figure 5), at each step of conditional expectation,we need to simultaneously move the variables in m l,k,i and ∂ l f i ( x k, 0 ) (using Lipschitz property)so that they can match and cancel out with each other.
Revised: To mimic the final step in the color-ball example (Figure 5), at each step of conditional expectation,we need to simultaneously move the variables in m l,k,i and ∂ l f i ( x k, 0 ) (using Lipschitz property)so that they can match and cancel out with each other.

Original: This operation will introduce new errorterms and it is our duty to put them under control.
Revised: This operation will introduce new errorterms and it is our duty to put them under control.

Original: We omit the proof for bounding E (cid:104)(cid:80) n − 1 i =

Revised: We omit the proof for bounding E (cid:104)(cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) since this is not our actual goal.
Revised: (cid:105) since this is not our actual goal.

Original: Instead, we will directly use the above ideas to bound E [( b 1 )] .
Revised: Instead, we will directly use the above ideas to bound E [( b 1 )] .

Original: To do so, we need to further tacklethe issue (ii) and (iii) mentioned before.
Revised: To do so, we need to further tacklethe issue (ii) and (iii) mentioned before.

Original: We explain as follows.
Revised: We explain as follows.

Original: Bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) :
Revised: Bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) :

Original: Step 2.
Revised: Step 2.

Original: We now resolve issue (ii) , i.e., handle the effect of ∂ l f ( x k, 0 ) √ v l,k, 0 .
Revised: We now resolve issue (ii) , i.e., handle the effect of ∂ l f ( x k, 0 ) √ v l,k, 0 .

Original: The key idea is as follows: when we calculate E [( b 1 )] , we sequentially take conditional expectation 953 E k ( · ) , E k − 1 ( · ) , etc.. When taking E k ( · ) , we will fix all the historical information up to k -th epoch, so ∂ l f ( x k, 0 ) √ v l,k, 0 can be regarded as a constant.
Revised: The key idea is as follows: when we calculate E [( b 1 )] , we sequentially take conditional expectation 952 E k ( · ) , E k − 1 ( · ) , etc.. When taking E k ( · ) , we will fix all the historical information up to k -th epoch, so ∂ l f ( x k, 0 ) √ v l,k, 0 can be regarded as a constant.

Original: In this sense, E k (cid:104)
Revised: In this sense, E k (cid:104)

Original: [Equation]
Revised: [Equation]

Revised: [Equation]

Original: can be calculated following the same idea as color-ball toy example.
Revised: can be calculated following the same idea as color-ball toy example.

Original: However, when taking E k − 1 ( · ) , ∂ l f ( x k, 0 ) √ v l,k, 0 will become a random variable which changes with differenttrajectories.
Revised: However, when taking E k − 1 ( · ) , ∂ l f ( x k, 0 ) √ v l,k, 0 will become a random variable which changes with differenttrajectories.

Original: In this case, the color-ball method cannot be applied.
Revised: In this case, the color-ball method cannot be applied.

Original: To fix this issue, we introducethe following lemma to change ∂ l f ( x k, 0 ) √ v l,k, 0 into ∂ l f ( x k − 1 , 0 ) √ v k − 1 , 0 , which can again be regarded as a fixedconstant when taking E k − 1 ( · ) .
Revised: To fix this issue, we introducethe following lemma to change ∂ l f ( x k, 0 ) √ v l,k, 0 into ∂ l f ( x k − 1 , 0 ) √ v k − 1 , 0 , which can again be regarded as a fixedconstant when taking E k − 1 ( · ) .

Original: Lemma G.2.
Revised: Lemma G.2.

Original: Suppose Assumption 2.1 holds and β 1 < √ β 2 .
Revised: Suppose Assumption 2.1 holds and β 1 < √ β 2 .

Original: For any integer j ∈ [0 , k ] , ifmax
Revised: For any integer j ∈ [0 , k ] , ifmax

Original: [Equation]
Revised: [Equation]

Original: [Equation]

Original: | ≥ Q k
Revised: | ≥ Q k

Original: [Equation]
Revised: [Equation]

Original: j (where
Revised: j (where

Original: Q
Revised: Q

Original: [Equation]
Revised: [Equation]

Original: :=
Revised: :=

Original: [Equation]
Revised: [Equation]

Original: 2 ), then we have the following result:
Revised: 2 ), then we have the following result:

Original: [Equation]
Revised: [Equation]

Original: (cid:16) [Equation] [Equation] n(cid:17)
Revised: (cid:16) 1 − (1 − β 2 )4 nρ 2 β n(cid:17)

Original: [Equation]

Original:  1 , where δ 1 is defined in (16) .
Revised: δ 1 , where δ 1 is defined in (16) .

Original: The proof of Lemma G.2 can be seen in Appendix G.3.
Revised: The proof of Lemma G.2 can be seen in Appendix G.3.

Original: To proceed, we combine Lemma G.2 and the“color-ball method of the 2nd kind", and thus we can prove Lemma A.2.
Revised: To proceed, we combine Lemma G.2 and the“color-ball method of the 2nd kind", and thus we can prove Lemma A.2.

Original: This lemma is crucial forour current goal: bounding E ( b 1 ) .
Revised: This lemma is crucial forour current goal: bounding E ( b 1 ) .

Original: We refer the readers to Appendix A for more information.
Revised: We refer the readers to Appendix A for more information.

Original: We emphasize that here are still the following gap between Lemma A.2 and our goal E ( b 1 ) .
Revised: We emphasize that here are still the following gap between Lemma A.2 and our goal E ( b 1 ) .

Original: • We have the similar gap as discussed at the end of the Step 1 .
Revised: • We have the similar gap as discussed at the end of the Step 1 .

Original: • The condition in Lemma G.2 has requirement on the gradient norm, while this requirement is
Revised: • The condition in Lemma G.2 has requirement on the gradient norm, while this requirement is

Original: temporarily ignored in the color-ball method of the 2nd kind.
Revised: temporarily ignored in the color-ball method of the 2nd kind.

Original: • The result in Lemma G.2 has additional error terms other than O (1 / √ k ) .
Revised: • The result in Lemma G.2 has additional error terms other than O (1 / √ k ) .

Original: This is slightly different
Revised: This is slightly different

Original: from the setting in Lemma A.2.
Revised: from the setting in Lemma A.2.

Original: It requires some technical lemmas to handle these gaps.
Revised: It requires some technical lemmas to handle these gaps.

Original: We fill in these gaps in Lemma G.4.
Revised: We fill in these gaps in Lemma G.4.

Original: Thetechnical details can be seen Appendix G.4.
Revised: Thetechnical details can be seen Appendix G.4.

Original: Bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) :
Revised: Bounding E (cid:104)(cid:80) l large ( b 1 ) (cid:105) :

Original: Step 3.
Revised: Step 3.

Original: Now we shift gear to tackle (iii): handling the random variable“ l large".
Revised: Now we shift gear to tackle (iii): handling the random variable“ l large".

Original: We rewrite “ l large" into the indicator function as follows:
Revised: We rewrite “ l large" into the indicator function as follows:

Original: E  (cid:88) l large

Original: [Equation]

Original: b

Original: [Equation]

Original: )  [Equation] [Equation] E (cid:34)
Revised: E 

Original: [Equation]
Revised: [Equation]

Original: l =
Revised: l =

Original: I
Revised: I

Original: [Equation]
Revised: [Equation]

Original: l f(
Revised: f

Original: [Equation]
Revised: [Equation]

Original: 0 ) √ v l,k,n − 1 (cid:88) i =
Revised: [Equation] v l,k,n − 1 (cid:88) i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:35) , where I k :
Revised: (cid:35) , where I k :

Original: = I (cid:16) max i | ∂ l f i ( x k, 0 ) | ≥ Q k := △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 (cid:17) is the indicator function ( I ( A ) =
Revised: = I (cid:16) max i | ∂ l f i ( x k, 0 ) | ≥ Q k := △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 (cid:17) is the indicator function ( I ( A ) =

Original: 1when event A holds and I ( A )
Revised: 1when event A holds and I ( A )

Original: = 

Revised: =

Original: otherwise.)
Revised: otherwise.)

Original: Similarly as before, when taking E k ( · ) , “ I k " canbe regarded as a constant index.
Revised: Similarly as before, when taking E k ( · ) , “ I k " canbe regarded as a constant index.

Original: Therefore, E k (cid:104)(cid:80) dl
Revised: Therefore, E k (cid:104)(cid:80) dl

Original: =

Revised: =

Original: I k ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Revised: I k ∂ l f ( x k, 0 ) √ v l,k, 0 (cid:80) n − 1 i =

Original: ( m l,k,i − ∂ l f i ( x k, 0 ))
Revised: ( m l,k,i − ∂ l f i ( x k, 0 ))

Original: (cid:105) canbe calculated following the same idea as color-ball model of the 1st kind.
Revised: (cid:105) canbe calculated following the same idea as color-ball model of the 1st kind.

Original: However, when taking 981 E k − 1 ( · ) , I k will become a random variable which changes with different trajectories.
Revised: However, when taking 980 E k − 1 ( · ) , I k will become a random variable which changes with different trajectories.

Original: In this case,the color-ball method cannot be applied.
Revised: In this case,the color-ball method cannot be applied.

Original: Similarly as in Step 2 , we introduce the following lemma tochange I k into I k − 1 (defined later), which can again be regarded as a fixed when taking E k − 1 ( · ) .
Revised: Similarly as in Step 2 , we introduce the following lemma tochange I k into I k − 1 (defined later), which can again be regarded as a fixed when taking E k − 1 ( · ) .

Original: Lemma G.3.
Revised: Lemma G.3.

Original: Suppose Assumption 2.1 holds and β 1 < √ β 2 .
Revised: Suppose Assumption 2.1 holds and β 1 < √ β 2 .

Original: For 0 ≤ j ≤ k , we define
Revised: For 0 ≤ j ≤ k , we define

Original: I k − j := I (cid:16) max i | ∂ l f i ( x k − j, 0 ) | ≥ (cid:80) kp = k − j Q p (cid:17) , where Q k :=
Revised: I k − j := I (cid:16) max i | ∂ l f i ( x k − j, 0 ) | ≥ (cid:80) kp = k − j Q p (cid:17) , where Q k :=

Original: △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 , then we havethe following results.
Revised: △ 1 n √ n √ k 32 √ 2 (1 − β 2 ) n β n 2 , then we havethe following results.

Original: I k
Revised: I k

Original: = I   max i | ∂ l f i ( x k, 0 )
Revised: = I   max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k and max i | ∂ l f i ( x k − j, 0 )
Revised: | ≥ Q k and max i | ∂ l f i ( x k − j, 0 )

Original: | ≥ k (cid:88) p =
Revised: | ≥ k (cid:88) p =

Original: k − j
Revised: k − j

Original: Q p 
Revised: Q p 

Original: 
Revised: 

Original: + I   max i | ∂ l f i ( x k, 0 ) | ≥ Q k and max i | ∂ l f i ( x k − j, 0 ) |
Revised: + I   max i | ∂ l f i ( x k, 0 ) | ≥ Q k and max i | ∂ l f i ( x k − j, 0 ) |

Original: ≤ k (cid:88) p =
Revised: ≤ k (cid:88) p =

Original: k − j
Revised: k − j

Original: Q p 
Revised: Q p 

Original:  , (31)
Revised:  , (31)

Original: I   max i | ∂ l f i ( x k, 0 )
Revised: I   max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k and max i | ∂ l f i ( x k − j, 0 )
Revised: | ≥ Q k and max i | ∂ l f i ( x k − j, 0 )

Original: | ≥ k (cid:88) p =
Revised: | ≥ k (cid:88) p =

Original: k − j
Revised: k − j

Original: Q p 
Revised: Q p 

Original:  =
Revised:  =

Original: I   max i | ∂ l f i ( x k − j, 0 )
Revised: I   max i | ∂ l f i ( x k − j, 0 )

Original: | ≥ k (cid:88) p =
Revised: | ≥ k (cid:88) p =

Original: k − j
Revised: k − j

Original: Q p 
Revised: Q p 

Original:  =
Revised:  =

Original: I k − j .
Revised: I k − j .

Original: Proof.
Revised: Proof.

Original: Equation (31) is straightforward, we only prove (32) here.
Revised: Equation (31) is straightforward, we only prove (32) here.

Original: Under Assumption 2.1, we have| ∂ l f i ( x k, 0 ) − ∂ l f i ( x k − j, 0 ) | ≤ n △ n ( k − 1) + · · · + n △ n ( k − j ) ≤ Q k − 1 + · · · + Q k − j .
Revised: Under Assumption 2.1, we have| ∂ l f i ( x k, 0 ) − ∂ l f i ( x k − j, 0 ) | ≤ n △ n ( k − 1) + · · · + n △ n ( k − j ) ≤ Q k − 1 + · · · + Q k − j .

Original: To show the 989 second inequality, it requires comparing the value between Q k and n △ n ( k ) , which are both problemdependent constants.
Revised: To show the 988 second inequality, it requires comparing the value between Q k and n △ n ( k ) , which are both problemdependent constants.

Original: Here, the inequality holds when n △ n ( k ) ≤ Q k .
Revised: Here, the inequality holds when n △ n ( k ) ≤ Q k .

Original: If otherwise, we can alwaysdefine ˜ Q k :=
Revised: If otherwise, we can alwaysdefine ˜ Q k :=

Original: max { Q k , n △ n ( k ) } and the inequality still holds by changing all the Q k into ˜ Q k .
Revised: max { Q k , n △ n ( k ) } and the inequality still holds by changing all the Q k into ˜ Q k .

Original: Wetemporarily omit this step for now.
Revised: Wetemporarily omit this step for now.

Original: Since | ∂ l f i ( x [Equation] , 0 ) − ∂ l f i ( 
Revised: Since | ∂ l f i ( x k, 0 ) − ∂ l f i ( x

Original: [Equation]
Revised: [Equation]

Original: | ≤
Revised: | ≤

Original: Q k −+ · · · +
Revised: Q k −+ · · · +

Original: Q k − j , the event 994 (cid:110) max i | ∂ l f i ( x k − j, 0 ) | ≥ (cid:80) kp = k − j Q p (cid:111) implies the event { max i | ∂ l f i ( x k, 0 )
Revised: Q k − j , the event 993 (cid:110) max i | ∂ l f i ( x k − j, 0 ) | ≥ (cid:80) kp = k − j Q p (cid:111) implies the event { max i | ∂ l f i ( x k, 0 )

Original: | ≥ Q k } , so theproof is completed.
Revised: | ≥ Q k } , so theproof is completed.

Original: Now we are ready to bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .
Revised: Now we are ready to bound E (cid:104)(cid:80) l large ( b 1 ) (cid:105) .

Original: Combining Step 1, 2 and 3 together, we prove thefollowing Lemma G.4.
Revised: Combining Step 1, 2 and 3 together, we prove thefollowing Lemma G.4.

Original: Lemma G.4.
Revised: Lemma G.4.

Original: Under Assumption 2.1, consider β 1 < √ β 2 , when k is large such that: k ≥ 4 ;β ( k − 1)
Revised: Under Assumption 2.1, consider β 1 < √ β 2 , when k is large such that: k ≥ 4 ;β ( k − 1)

Original: n≤ β n 1 √ k − 1 , we have the following result:
Revised: n≤ β n 1 √ k − 1 , we have the following result:

Original: [Equation]
Revised: [Equation]

Original: where δ 1 = (1 − β 2 )4 nρ 2 β n+ (cid:18) 1 √ β n 2 −(cid:19) , which goes to 0 when β 2 goes to 1.
Revised: where δ 1 = (1 − β 2 )4 nρ 2 β n+ (cid:18) 1 √ β n 2 −(cid:19) , which goes to 0 when β 2 goes to 1.

Original: Proof can be seen in Appendix G.4.
Revised: Proof can be seen in Appendix G.4.

Original: Now we bound the error terms: E (cid:104)(cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:105) .
Revised: Now we bound the error terms: E (cid:104)(cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:105) .

Original: Since they are multiplied by δ 1 , these two terms vanish when β 2 → 1 .
Revised: Since they are multiplied by δ 1 , these two terms vanish when β 2 → 1 .

Original: Bounding E (cid:104)(cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:105) .
Revised: Bounding E (cid:104)(cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:105) .

Original: We bound these two terms in the following Lemma G.5.
Revised: We bound these two terms in the following Lemma G.5.

Original: Lemma G.5.
Revised: Lemma G.5.

Original: Given all the history up to x k, 0 , we denote α as the index of the coordinate withthe greatest gradient: α = arg max l =1 , 2 , ··· ,d | ∂ l f ( x k, 0 ) | .
Revised: Given all the history up to x k, 0 , we denote α as the index of the coordinate withthe greatest gradient: α = arg max l =1 , 2 , ··· ,d | ∂ l f ( x k, 0 ) | .

Original: Under Assumption 2.1 and 2.2, considerβ 1 < √ β 2 , when k is large such that: k ≥ 4 ; β ( k − 1) n
Revised: Under Assumption 2.1 and 2.2, considerβ 1 < √ β 2 , when k is large such that: k ≥ 4 ; β ( k − 1) n

Original: [Equation]
Revised: [Equation]

Original: k − 1 , we have the following results:
Revised: k − 1 , we have the following results:

Original: [Equation]
Revised: [Equation]

Original: l [Equation] where δ 1 is defined in (16) , D 0 is defined in Assumption 2.2.
Revised: where δ 1 is defined in (16) , D 0 is defined in Assumption 2.2.

Original: Detailed proof of Lemma G.5 is shown in Appendix G.8.
Revised: Detailed proof of Lemma G.5 is shown in Appendix G.8.

Original: Bounding E (cid:110)(cid:80) l large ( a 1 ) + (cid:80) l large ( b 1 ) − (cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:111) .
Revised: Bounding E (cid:110)(cid:80) l large ( a 1 ) + (cid:80) l large ( b 1 ) − (cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:111) .

Original: With the help of Lemma G.4and G.5, we can bound E (cid:110)(cid:80) l large ( a 1 ) + (cid:80) l large ( b 1 ) − (cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:111) .
Revised: With the help of Lemma G.4and G.5, we can bound E (cid:110)(cid:80) l large ( a 1 ) + (cid:80) l large ( b 1 ) − (cid:80) l large { ( a 2 ) + ( b 2 ) } (cid:111) .

Original: The intuition is asfollows:
Revised: The intuition is asfollows:

Original: • ( a 1 ) is in the order of | ∂ l f ( x k, 0 ) | , multiplied by some positive constant;• (cid:80) l large { ( a 2 )
Revised: • ( a 1 ) is in the order of | ∂ l f ( x k, 0 ) | , multiplied by some positive constant;• (cid:80) l large { ( a 2 )

Original: + ( b 2 ) } vanishes when β 2 → 1 .• E (cid:110)(cid:80) l large ( b 1 ) (cid:111) vanishes when β 2 → 1 and k goes to
Revised: + ( b 2 ) } vanishes when β 2 → 1 .• E (cid:110)(cid:80) l large ( b 1 ) (cid:111) vanishes when β 2 → 1 and k goes to

Original: infinity
Revised: infinity

Original: Therefore, E (cid:110)(cid:80) 
Revised: Therefore, E (cid:110)(cid:80) l

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: +
Revised: +

Original: [Equation]
Revised: [Equation]

Original:  l large
Revised: (cid:80) l large

Original: [Equation]
Revised: [Equation]

Original: + ( b
Revised: + ( b

Original: [Equation]
Revised: [Equation]

Original: } (cid:111) is still in the order of | ∂ l f ( x k, 0 ) |(multiplied by a positive constant when β 2 is large).
Revised: } (cid:111) is still in the order of | ∂ l f ( x k, 0 ) |(multiplied by a positive constant when β 2 is large).

Original: More formal results are shown in Lemma G.6.
Revised: More formal results are shown in Lemma G.6.

Original: Lemma G.6.
Revised: Lemma G.6.

Original: Under Assumption 2.1 and 2.2, when the hyperparameters satisfy: i) β 1 < √ β 2 < 1 ,ii) β 2 is large enough such that A ( β 2 ) is small enough to satisfy (33) , where A ( β 2 ) is a non-negativeconstant that approaches 0 when β 2 approaches 1 .
Revised: Under Assumption 2.1 and 2.2, when the hyperparameters satisfy: i) β 1 < √ β 2 < 1 ,ii) β 2 is large enough such that A ( β 2 ) is small enough to satisfy (33) , where A ( β 2 ) is a non-negativeconstant that approaches 0 when β 2 approaches 1 .

Original: More Specifically, A ( β 2 ) needs to satisfy ( ρ 1 , ρ 2and ρ 3 are defined in Appendix F.1).
Revised: More Specifically, A ( β 2 ) needs to satisfy ( ρ 1 , ρ 2and ρ 3 are defined in Appendix F.1).

Original: [Figure]
Revised: [Figure]

Original: [Equation]
Revised: [Equation]

Original: Then
Revised: Then

Original: [Equation]
Revised: [Equation]

Original: we have the following result when k is large enough such that β ( k − 1)
Revised: we have the following result when k is large enough such that β ( k − 1)

Original: n≤ β n 1 √ k − 1 and k ≥ 4 :
Revised: n≤ β n 1 √ k − 1 and k ≥ 4 :

Original: [Equation]
Revised: [Equation]

Original: Proof of Lemma G.6 can be seen in Appendix G.9.
Revised: Proof of Lemma G.6 can be seen in Appendix G.9.

Original: Remark G.7 .
Revised: Remark G.7 .