RMS PROP CAN CONVERGE WITH PROPER HYPERPARAMETER
Anonymous authors
Paper under double-blind review
A BSTRACT
Despite the existence of divergence examples, RMSprop remains one of the most popular algorithms in machine learning.
Towards closing the gap between theory and practice, we prove that RMSprop can converge with proper choice of hyperparameters under certain conditions.
More speciﬁcally, we prove that when the hyper-parameter β 2 is large enough, the random shufﬂing version of RMSprop converges to a bounded region in general, and converges to critical points in the interpolation regime.
It is worth mentioning that our results do not depend on “bounded gradient" assumption, which is often the key assumption utilized by existing theoretical work for Adam.
Removing this assumption allows us to establish a phase transition from divergence to non-divergence for RMSProp.
Finally, based on our theory, we conjecture that in practice there is a critical threshold th , such that RMSprop generates reasonably good results only if β 2 ≥ th .
We provide empirical evidence for such a phase transition in our numerical experiments.
I NTRODUCTION
RMSprop (Tielman & Hinton, 2012) remains one of the most popular algorithms for machine learning applications.
As a non-momentum version of a more general algorithm Adam, RMSprop’s good empirical performance has been well acknowledged by practitioners in generative adversarial networks (GANs) (Seward et al., 2020; Yazıcı et al., 2019; Karnewar & Wang, 2020; JolicoeurMartineau, 2019), reinforcement learning (Mnih et al., 2016), etc.
In spite of its prevalence, however,
Reddi et al.
(2018) discovered that RMSprop (as well as the more general version Adam) can diverge even for simple convex functions.
To ﬁx the algorithm, the authors proposed a new variant called
AMSGrad, which is guaranteed to converge under certain conditions.
Since then, it has been an active area of research to design provably convergent variants of RMSprop.
These variants include AdaFom (Chen et al., 2019), Adabound (Luo et al., 2019), Nostalgic Adam (Huang et al., 2019), Yogi (Zaheer et al., 2018), and many more.
Despite the variants, the vanilla RMSprop indeed works well in practice, and after proper hyper-parameter tuning, the non-convergence issue has not been commonly observed.
Why is there a large gap between theory and practice?
Is this because the real-world problems are likely to be “nice”, or is it because the theoretical analysis of
RMSprop does not match how it is used in practice?
With the above questions in mind, we revisited the counter-example of Reddi et al.
(2018), and found an interesting phenomenon.
The counter-example of Reddi et al.
(2018) is the following:
[Equation]
where x ∈ [ − 1 , 1] .
They proved the divergence under the condition β 2 ≤ min { C − 4 C − 2 , 1 − (cid:0) 9 2 C (cid:1) 2 } , where β 2 is the second order momentum coefﬁcient in Algorithm 1 (the algorithm is presented later).
For instance, when C = 10 , then the algorithm diverges if β 2 < 0 .
Reddi et al.
(2018) mentioned that “this explains why large β 2 is advisable while using Adam algorithm”, but they did not explain whether large β 2 leads to convergence in their example.
We run simulation for problem (1) with different β 2 and found there is always a threshold of β 2 above which RMSprop can converge, see
Figure 1.
For instance, when C = 10 , the transition point of β 2 is roughly 0.63: the algorithm will converge if β 2 > 0 .
64 but diverges
if β 2 < 0 .
In general, there is a curve of divergence to convergence, and such a curve slopes upward, which means the is closer to 1 if C becomes larger.
Based on observation, we make the
Conjecture: RMSprop converges if β 2 is large enough.
Before further discussion, we make the following assumption.
Assumption 1.1.
f ( x ) =
(cid:80)
[Equation]
j =
f
j ( x )
[Equation]
and
[Equation]
We divide optimzation problems into 2 classes: realizable problems where D 0 = 0 and nonrealizable problems where D 0 > 0 .
When D 0 = 0 , the assumption becomes (cid:80)
[Equation]
j
[Equation]
x )
[Equation] [Equation]
D 1 (cid:107)∇ f ( x ) (cid:107) 22 , which is called “strong growth condition” (SGC) (Vaswani et al., 2019).
It requires the norm of the stochastic gradient to be proportional to the batch gradient norm.
When (cid:107)∇ f ( x ) (cid:107) = 0 , under SGC we have (cid:107)∇ f j ( x ) (cid:107) =
for all j .
For linear regression problems,
SGC holds if the linear model can ﬁt all data.
More speciﬁcally, for the problem min x (cid:107) A x (cid:107) 2 = (cid:80) n j =1 (cid:0) a Tj x (cid:1) 2 where A is an n by n matrix and a Tj is the j -th row vector of A , SGC holds with
D 1 ≤ λ max (cid:0)(cid:80) ni
=1 a i a Ti a i a Ti (cid:1) /λ min (cid:0) A T A (cid:1) (Raj & Bach, 2020).
SGC can be viewed as a simple condition that models overparameterized neural network problems which can interpolate all data points (Vaswani et al., 2019).
For this reason, in this work we use the terminology “realizable problems” to refer to the problems that satisfy SGC.
1. M AIN C ONTRIBUTIONS
To resolve the conjecture, we delve into RMSprop’s convergence issues and obtain a series of theoretical and empirical results.
Our contributions are summarized below:
• We ﬁnd RMSprop’s convergence contingent on the choice of β 2 .
For general optimization problems, there are two types of hyper-parameters: problem-dependent hyper-parameters such as step size in GD, and universal constant such as momentum coefﬁcient in heavy ball method 1 .
Our result reveals that β 2 is closer to the ﬁrst type.
• We prove that RMSprop converges to stationary point for realizable problems (interpolation regime) and to some bounded region for non-realizable problems.
Combining with the divergence example of RMSprop, this indicates that there is a phase transition from divergence to convergence dependent on β 2 ; note that “convergence” is in a weak sense of converging to a bounded region for non-realizable case, and in a strong sense of converging to stationary points for realizable case.
• To our best knowledge, we are the ﬁrst to prove the convergence of RMSprop and some of Adam without any form of assumption about the boundedness of the gradient norm.
This is important for conveying our message of transition: with added assumptions on bounded gradients, then the gradients cannot diverge, while the counter-example shows that the gradient can diverge.
P RELIMINARIES
We consider a ﬁnite-sum problem:
[Equation]
In neural network training, f j usually represents the loss contributed by the j -th sample batch.
We present randomly shufﬂed Adam in Algorithm 1.
RMSprop is the special case of Adam with β 1 = 0 .
In this work, we mainly focus on RMSprop; nevertheless, we will present a result on Adam with small β 1 which is a special Adam that is close to RMSprop.
Algorithm
Initialize m 1 , − 1 = 1 1 − β 1 ∇ f ( x 0 ) and v 1 , − 1 = 1 1 − β 2 max j {∇ f j ( x 0 ) ◦ ∇ f j ( x 0 ) } .
for k = 1 → ∞ do
Sample { τ k, 0 , τ k, 1 , · · · , τ k,n − 1 } as a random permutation of { 0 , 1 , 2 , · · · , n − 1 } for i =
→ n − 1 do m k
[Equation]
i =
β
1 m k,i − 1 + (1 − β 1 ) ∇ f τ k,i v k,
− k,i τ k,i n √
,i l,k,i
In Algorithm 1, x denotes the optimization variable, m denotes the ﬁrst order momentum and v denotes the second order momentum.
Speciﬁcally, we denote x k,i , m k,i , v k,i ∈
R d as the value of x, m, v at the k -th outer loop and i -th inner loop, respectively.
We denote ∇ f j as the gradient of f j and let ◦ be the component-wise multiplication.
The division of two vectors is component-wise as well.
Moreover, we denote η t as the step-size and β 1 , β 2 as the hyper-parameters in the algorithm.
When n = 1 , we obtain full batch Adam.
We replaced the bias correction step in (Kingma & Ba, 2015) with a special initialization on m 1 , −and v 1 , − 1 .
This initialization can also correct the bias, but have cleaner results.
Since the effect of initialization or bias correction becomes more and more negligible as the training progresses,
RMSprop with zero initialization or our initialization will have the same asymptotic behavior.
We put our results for the original version of RMSprop in the appendix.
As for hyper-parameters, we choose η t = η 1 √ t and ﬁx β 2 to be a constant that is independent of the iteration count.
We allow (cid:15) to be an arbitrary non-negative constant; in particular, our result holds even for (cid:15)
= 0 .
The constant (cid:15) is added in practice for numerical stability, and (cid:15) is typically chosen to be 10 − 6 or even 10 − 8 .
It is much smaller than √ v k,i (which is roughly the size of gradient norm).
2. R ELATED W ORK
As discussed earlier, one line of research focuses on mutants of RMSprop and Adam that can be proved to converge.
These works usually modify the update rule of v t .
For instance, AMSGrad (Reddi et al., 2018), AdaFom (Chen et al., 2019) explicitly make v t non-decreasing.
Nostalgic Adam (Huang et al., 2019) and the algorithms analyzed in (Zou et al., 2019) and (Chen et al., 2019) used iteration-dependent β 2 t (and/or β 1 t ) to let v t weigh more on past gradients.
Some works added new modiﬁcations into RMSprop and Adam; for instance, (Zhou et al., 2019) mitigated the bias in update direction by using a different estimate of v t , Dozat (2016)
combined Adam with Nesterov momentum, and Liu et al.
(2019) employed a warm-up technique.
Besides modfying the algorithm, a few attempts have been made to address the non-convergence issues of the orignial versions, but they often have extra assumptions.
A number of works Zaheer et al.
De et al.
; Défossez et al.
(2020) prove the convergence of Adam under extra assumptions.
One representative work along this line, Défossez et al.
(2020), establishes a clean convergence result and also provides some insights on the momentum mechanisms by improving the dependence of the iteration complexity on 1 − β 1 .
However, these works assume (cid:15) to be relatively large compared to √ v k,i .
The issue is that such a choice essentially transforms RMSprop back to
SGD since the effective step size is controlled by (cid:15) , not √ v k,i .
This is in contrary to the spirit of
RMSprop, which is to use adaptive step size to accelerate convergence.
A few other works do not need the assumption of (cid:15) , but they have other assumptions.
De et al.
(2018) analyzed deterministic and stochastic RMSprop, but they utilize a rather unrealistic assumption that the sign of all noisy gradients are the same, i.e., sign ( ∇ f p ( x ))
= sign ( ∇ f q ( x )) for all p, q .
(Chen et al., 2019) described a few quantities based on the iterates, and proved that if they grow in a certain speed as the iterates go, the algorithm converges.
The drawback is that the condition cannot be checked a priori .
Besides the assumptions mentioned above, all the aforementioned works require the gradient to be bounded.
In general, removing boundedness assumptions (of any kind, including bounded gradient, bounded iterates, etc.) is not necessarily easy, thus such results are appreciated even for basic SGD.
For instance, (Bertsekas & Tsitsiklis, 2000) presented a nice discussion of various results on inexact GD that involves various types of bounded assumptions, and claimed “bounded-assumption-free” as one of the main contributions of their work.
Very recently, we noticed another work (Liu et al., 2020) which removed the bounded gradient assumption for SGDM (SGD with momentum) and obtained satisfactory rates.
Nevertheless, we are not aware of an existing result on RMSProp that do not require bounded gradient assumption.
We will explain later why removing this bounded gradient assumption is particularly important for our paper.
T HE raison d’être FOR β
Figure 1 clearly demonstrates the important role of β 2 in the convergence of RMSprop.
Speciﬁcally, a sufﬁciently large β 2 is critical for RMSprop’s convergence.
Indeed, some recent works (Reddi et al., 2018)
(Zhou et al., 2019) have also made similar arguments, but they focused on understanding one part of the phenomenon, that is, small β 2 leads to divergence.
Our goal in this work is to complete the other part of the story by showing that, sufﬁciently large β 2 guarantees convergence.
This result will be provided in Sec. 4.
To understand the role of β 2 , we ﬁrst discuss why RMSprop diverges.
It is known that the stochastic noise due to mini-batch will distort the gradient direction, leading to possible divergence, but in standard SGD, the distortion in multiple iterations offsets since the stochastic gradient is an unbiased estimate of the gradient.
For RMSProp, at a given iteration the scaling constant 1 / √ v in the update direction may cause larger gradient distortion than the standard SGD.
The distortion can be so signiﬁcant that the average updating direction falls outside the dual cone of the true gradient.
To illustrate this, consider the extreme case that β 2 =
(i.e., signSGD) and the special example (1).
When applying signSGD to solve (1), in each epoch which consists of C iterations, one iteration will move x left followed by C − 1 iterations that move x right.
Since all step sizes are the same in one epoch, the accumulated effect of one epoch makes x move in the ascending direction, instead of the descending direction.
Then why does large β 2 help?
Intuitively, a large β 2 can control the distortion on update directions.
In the extreme case that β 2 = 1 , RMSprop reduces to SGD where the distortion of multiple iterations can offset, leading to converge.
We suspect that β 2 does not need to be exactly 1 , and a large β 2 is enough to control the distortion.
Our experiment in Figure 1 conﬁrms that, at least for the counter-example of
Reddi et al.
, there is an interval β 2 ∈ [ c, 1] such that RMSprop converges.
What was initially not clear is whether the counter-example of Reddi et al.
(2018) is a very special case or the convergence of large- β 2 -RMSprop holds for all problems.
We found the real situation is somewhat more tricky.
For non-realizable problems, we discovered an example for which RMSprop cannot converge to the minimum for a wide range of β 2 < 1 , but unlike the small- β 2 -case the iterates converge to a small ball around the minimum.
This motivates us to distinguish three convergentsituations: divergence, convergence to a small region, convergence to critical points.
What we can prove for the general problem is (see Theorem 4.3): for small β 2 , RMSprop can diverge; for large β 2 ,
RMSprop must converge to a small region whose size depends on β 2 .
Then why do we observe the convergence to a single point in the experiment for (1)?
We suspect this is because the problem (1) is realizable, and conjectured that the property of “convergence to critical points” holds for all realizable problems.
We indeed prove this conjecture (see Corollary 4.1): large- β 2 -RMSprop converges to critical points if the problem satisﬁes SGC.
We summarize our ﬁndings about the convergence properties of RMSProp in Table 1.
[Table]
to critical points”.
If the gradient norm becomes larger than 10 − 1 and keeps increasing during training, we classify the result as “divergence”.
We classify a result as “converges to small region” if the gradient norm falls below a certain level but not close to 0 .
Note that our results do not conﬂict with Theorem 3 in Reddi et al.
(2018) which claims that “for any constant β 1 and β 2 there exists a divergent example” since here we choose β 2 to be problemdependent, just like one chooses a step size < 2 /L for GD where L is a problem dependent parameter.
Another remark is that though β 2 could be close to 1, RMSprop still retains the ability to adapt v to gradient square norm as long as β 2 < 1 , because new gradient signals are added for each iteration and the impact of previous signals decays exponentially.
It is the adaptive ability that distinguishes
RMSprop from SGD.
Proving the theoretical advantage of RMSProp over SGD (i.e., choosing β 2 <is better than β 2 = 1 ) is a very interesting question; in general, the theoretical advantage of adaptive gradient methods (including RMSProp and AdaGrad) over SGD is a long standing question.
in this work, we focus on the fundamental problem of convergence, instead of the more challanging question of justifying the advantage of RMSProp.
C ONVERGENCE R ESULTS
In this section, we present the formal theoretical results.
We start from the results of full batch
RMSprop/Adam, and then present the results for the stochastic versions.
4. F ULL -B ATCH V ERSION
We ﬁrst consider the full-batch version of RMSProp.
The following theorem shows that if we use all samples to evaluate the gradient, RMSProp with diminishing stepsize converges to critical points, regardless of the choice of β 2 .
Here we consider one popular step size schedule that η t = η 1 √ t .
Theorem 4.1.
(convergence of full-batch RMSprop)
For problem (3) with n = 1 , assume that f is gradient Lipschitz continuous with constant L and lower bounded by f ∗ .
Then, for full-batch
RMSprop (Alg. 1 with β 1 = 0 ) with diminishing step size η t = η 1 √ t and any β 2 ∈ (0 , 1) , we have:
[Equation]
where T > 0 is the total iteration number.
De et al.
(2019) also proves the convergence of full batch RMSprop, but they require the gradient norm to be upper bounded; in contrast, we do not need this assumption, and only require lower-boundedness and L -smoothness of f .
Our result suggests that the convergence property of batch-RMSprop is similar to signSGD, an algorithm that only uses the sign of gradient to calculate its descent direction (Bernstein et al., 2018): in the full-batch setting, signSGD
(which can be called sign GD) has also been proved to converge without bounded gradient assumption.
Below, we also derive an analogous result for full-batch Adam with only one additional constraint β 1 < √ β 2 < 1 , which is often satisﬁed in practice:
Theorem 4.2.
(convergence of full-batch Adam) For optimization problem (3) with n = 1 , assume that f is gradient Lipschitz continuous with constant L and lower bounded by f ∗ .
Then, for full-batch
Adam with diminishing step size η t = η 1 √ t and any β 1 < √ β 2 < 1 , we have: min t ∈ (1 ,T ] (cid:107)∇ f t (cid:107) 1 ≤
O (cid:16) log T √ T (cid:17) .
4. S TOCHASTIC V ERSIONS
As mentioned earlier, our simulation shows that RMSprop may not converge to critical points for non-realizable problems (an example is provided in the appendix).
Nevertheless, we can still show randomly shufﬂed large- β 2 -RMSprop converges to a bounded region:
Theorem 4.3.
(large- β 2 RMSprop converge to a region)
For problem (3), assume f is lower-bounded by f ∗ and all ∇ f j is L -Lipschitz continuous.
Furthermore, assume (2) holds, and β 2 satisﬁes
[Equation]
Then, for random-shufﬂe RMSprop with η t = η 1 √ t , we have
[Equation]
Here Q 3 , 3 > 0 is a constant that goes to zero in the limit as β 2 → 1 .
Remark 1.
This result and the result in Reddi et al.
(2018) together distinguish large-
β 2 -RMSprop
and
small- β 2 -RMSprop: the former converges to a bounded region, while the latter can diverge.
Note that there is a gap between the lower bound of β 2 and the upper bound of β 2 in the counter-example.
We do not try to provide tight bounds on the threshold of β 2 , as our main goal is to show a qualitative difference between large- β 2 -RMSprop and small- β 2 -RMSprop.
Remark 2 : Condition (4) in Theorem 4.3 implies that 1 − β 2 ≤ O (cid:0) n − 3 .
5 (cid:1) .
In the appendix we introduce three problem-dependent parameters ρ 1 ∈ [1 , √ n ] , ρ 2 ∈ [0 , n ] , and ρ 3 ∈ [1 , √ n ] in equations(14), (15) and (16), and improve the sufﬁcient condition (4) to 1 − β 2 ≥ O (1 / ( nρ 1 ρ 2 ρ 3 )) .
For the worst case, the bound is O (cid:0) n − 3 .
5 (cid:1) , just like condition (4) in Theorem 4.3.
In actual training process, ρ 1 , ρ 2 , and ρ 3 may not reach their upper bounds, thus the threshold of β 2 can be lower in practice.
The dependence on the number of batches n suggests that as n increases, the required hyper-parameter β 2 should be larger.
This is understandable since more minibatches means larger noise in the stochastic gradient, and thus larger β 2 .
Remark 3.
We point out three possible algorithm behavior: divergence to inﬁnity (or divergence for short), convergence to a bounded region (or non-divergence for short) and convergence to critical points.
We distinguish the three cases, making it easier to explain the qualitative difference of small- β 2 and large- β 2 regime.
For non-realizable cases, the phase transition is from divergence to non-divergence.
Therefore, it is important to discard the bounded-gradient assumption: this assumption eliminates the possibility of divergence of gradients a priori.
To be clear, there are actually two sub-cases of non-divergence: iterates can stay in a bounded but huge region (bad case), or iterates stay in a bounded region dependent on some parameters (good case).
Indeed, the “convergence” of constant-stepsize SGD is in the sense of “converging to a region with size proportional to the noise variance”.
Our result of “converging to bounded region” is also meaningful as the size of the region goes to zero as the noise variance goes to 0 or D 0 goes to 0 (realizable case).
Note that “divergence” can be also interpreted as “not converging to critical points” which is the notion used in Reddi et al.
(2018), instead of “diverging to inﬁnity”.
We use the latter concept of “diverging to inﬁnity” for the term “divergence”, because “not converging to critical points” can include the good case of converging to a small region around critical points (like constant-stepsize
SGD).
In the example of Reddi et al.
(2018), a constrained problem is considered (bound constraint [-1,1]), thus divergence to inﬁnity cannot happen.
We add an example where the iterates and the gradients can diverge to inﬁnity for small β 2 ; see Appendix A.2.
As a corollary of Theorem 4.3, if the optimization problem satisﬁes SGC (i.e. D 0 = 0 ), RMSprop can converge to critical points.
Corollary 4.1.
For optimization problem (3), assume that f is lower-bounded by f ∗ and f i is gradient Lipschitz continuous with constant L for all i .
Furthermore, assume that f j satisﬁes the strong growth condition (cid:80) n − 1 j =0 (cid:107)∇ f j ( x ) (cid:107) 22 ≤ D 1 (cid:107)∇ f ( x ) (cid:107) 22 for all x .
Then, for random shufﬂe
RMSprop with step size η t = η 1 √ t and sufﬁciently large (but constant) β 2 ,
[Equation]
With the above corollary, the numerical result in Figure 1 should not be surprising: problem (1) satisﬁes the strong growth condition, thus there is always a range of β 2 inside which RMSprop converges.
We just need to tune β 2 larger.
We can prove similar convergence results for Adam with small β 1 and large β 2 .
Theorem 4.4.
For optimization problem (3), assume that f is lower-bounded by f ∗ and f j is gradient Lipschitz continuous with constant L for all j .
Furthermore, assume that f j satisﬁes (2) for all x .
Then, for randomly shufﬂed Adam with diminishing step size η t = η 1 √ t and β 1 , β 2 satisfying
T 1 ( β 1 , β 2 )
+ T 2 ( β 2 ) <
1 − 1 √ 2 , we have min t ∈ [1 ,T ] (cid:107)∇
[Equation]
nt
[Equation]
+ O
[Equation]
∀ T ≥ 4 , where Q 3 , 5 is a constant that approaches 0 in the limit T 1 + T 2 → 0 , T 2 is deﬁned in (4) , and Tis deﬁned as T
[Equation]
Remark:
This result shows that controlling β 2 and β 1 together can ensure convergence of Adam.
We conjecture that the same convergence can be proved for a large range of β 1 , but we are not able to prove that for now (which is why we focus on RMSProp in this work) and leave it to future work.
E XPERIMENTS
We conduct experiments of image classiﬁcation and GAN on MNIST and CIFAR-10 to support our theoretical ﬁndings.
The details of GAN experiments are in the appendix, and in this section we focus on image classiﬁcation results.
We visualize the optimization trajectory when training on MNIST for large β 2 = 0 .
8 and β 2 = 0 .in
Figure 2.
We observe different behavior: while the trajectory of β 2 = 0 .
8 moves away from the bottom of the basin, for larger β 2 the trajectory stays in the level set and has decreasing loss values.
In the CIFAR experiments, we used Resnet18.
We choose β 2 = 0 .
99 respectively.
With different batch sizes 8 , 16 , 32 , we run each algorithm for 100 epochs without explicit regularization.
Table 2 shows two phenomenons: ﬁrst, for ﬁxed batch size, there is a transition point of β 2 above which the accuracy suddenly jumps; second, the transition point is closer to 1 as the batch size decreases.
More speciﬁcally, for batch size 8 , the transition point lies in [0 . 95 , 0 . 99] : the average training accuracy is 44 .
53% for β 2 = 0 .
95 , but jumps to 99 .
74% for for β 2 = 0 .
For batch size 16 , the transition point lies in [0 . 9 , 0 .
95] : the average training accuracy is 67 .
27% for β 2 = 0 .
9 , but jumps to 96 .
38% for β 2 = 0 .
For batch size 16 , the transition point lies in [0 . 8 , 0 . 9] .
As batch size increases from 8 to 16 and then 32 , then transition point decreases from 0 .
99 to 0 .
95 and then 0 .
These two phenomenons are consistent with our theory.
The ﬁrst phenomenon can be explained by Theorem 4.3 and Corollary 4.1 which state that for large enough β 2 , RMSProp converges.
The second phenomenon can be explained by Theorem 4.3 as well: as explained in Remark 2, the required β 2 decreases as the number of mini-batches n decreases, i.e., the batch size increases.
[Table]
Next
[Table]
we demonstrate that the convergence speed of SGD is much slower than Adam under the same experiment setting as Table 2.
We compare the average training and test accuracy at the 10 -th epoch.
As Table 3 shows
[Table]
the accuracy of Adam is much higher than SGD at the 10 -th epoch.
[Table]
All codes generating experimental results are available on the anonymous repository https://
anonymous.4open.science/r/f6cfb2f1-093c-4808-86b0-d2af4cd10afb/
C ONCLUSION
In this work, we study the convergence behavior of RMSprop by taking a closer look at the hyperparameters.
Speciﬁcally, for realizable problems, we provide a data-dependent threshold of βabove which we prove the convergence of randomly shufﬂed RMSprop and small β 1 Adam without bounded gradient assumption.
We also show that RMSprop converge into a bounded region under non-realizable settings.
These ﬁndings reveal that there is a critical threshold of β 2 regarding the convergence behavior of RMSprop, and the phase transition is supported by the numerical experiments.
Our results provide basic guidelines for tuning hyper-parameters in practice.