O ptimistic M irror D escent in S addle -P oint P roblems : G oing the E xtra (-G radient ) M ile
Anonymous authors
Paper under double-blind review
A bstract
Owing to their connection with generative adversarial networks (GANs), saddlepoint problems have recently attracted considerable interest in machine learning and beyond.
By necessity, most theoretical guarantees revolve around convexconcave (or even linear) problems; however, making theoretical inroads towards e ﬃ cient GAN training depends crucially on moving beyond this classic framework.
To make piecemeal progress along these lines, we analyze the behavior of mirror descent (MD) in a class of non-monotone problems whose solutions coincide with those of a naturally associated variational inequality – a property which we call coherence .
We ﬁrst show that ordinary, “vanilla” MD converges under a strict version of this condition, but not otherwise; in particular, it may fail to converge even in bilinear models with a unique solution.
We then show that this deﬁciency is mitigated by optimism: by taking an “extra-gradient” step, optimistic mirror descent (OMD) converges in all coherent problems.
Our analysis generalizes and extends the results of Daskalakis et al.
[2018] for optimistic gradient descent (OGD) in bilinear problems, and makes concrete headway for provable convergence beyond convex-concave games.
We also provide stochastic analogues of these results, and we validate our analysis by numerical experiments in a wide array of GAN models (including Gaussian mixture models, and the CelebA and CIFAR-10 datasets).
I ntroduction
The surge of recent breakthroughs in deep learning has sparked signiﬁcant interest in solving optimization problems that are universally considered hard.
Accordingly, the need for an e ﬀ ective theory has two di ﬀ erent sides: ﬁrst, a deeper understanding would help demystify the reasons behind the success and / or failures of di ﬀ erent training algorithms; second, theoretical advances can inspire e ﬀ ective algorithmic tweaks leading to concrete performance gains.
For instance, using tools from the theory of dynamical systems, Lee et al.
[2016, 2017] and Panageas & Piliouras [2017] showed that a wide variety of ﬁrst-order methods (including gradient descent and mirror descent) almost always avoid saddle points.
More generally, the optimization and machine learning communities alike have dedicated signiﬁcant e ﬀ ort in understanding non-convex landscapes by searching for properties which could be leveraged for e ﬃ cient training.
As an example, the “strict saddle” property was shown to hold in a wide range of salient objective functions ranging from low-rank matrix factorization
[Bhojanapalli et al., 2016, Ge et al., 2017] and dictionary learning [Sun et al., 2017b,a], to principal component analysis [Ge et al., 2015], phase retrieval [Sun et al., 2016], and many other models.
On the other hand, adversarial deep learning is nowhere near as well understood, especially in the case of generative adversarial networks (GANs)
[Goodfellow et al., 2014].
Despite an immense amount of recent scrutiny, our theoretical understanding cannot boast similar breakthroughs as in “single-agent” deep learning.
Because of this, a considerable corpus of work has been devoted to exploring and enhancing the stability of GANs, including techniques as diverse as the use of
Wasserstein metrics [Arjovsky et al., 2017], critic gradient penalties [Gulrajani et al., 2017], feature matching, minibatch discrimination, etc.
[Radford et al., 2016, Salimans et al., 2016].
A key observation in all this is that ﬁrst-order methods may fail to converge even in bilinear zero-sum games like Matching Pennies [Papadimitriou & Piliouras, 2016, Daskalakis et al., 2018, Mescheder et al., 2018, Mertikopoulos et al., 2018, Bailey & Piliouras, 2018].
This is a critical failure of descent methods, but one which Daskalakis et al.
[2018] showed can be overcome through “ optimism ”, interpreted in this context as an “extra-gradient” step that pushes the training process further along the incumbent gradient – as a result, optimistic gradient descent (OGD) succeeds in cases where vanilla gradient descent (GD) fails (speciﬁcally, unconstrained bilinear saddle-point problems).
A common theme in the above is that, to obtain a principled methodology for training GANs, it is beneﬁcial to ﬁrst establish improvements in a more restricted setting, and then test whether these gains carry over to more demanding learning environments.
Following these theoretical breadcrumbs, we focus on a class of non-monotone problems whose solutions are related to those of a naturally associated variational inequality, a property which we call coherence .
Then, hoping to overcome the shortcomings of ordinary descent methods by exploiting the problem’s geometry, we examine the convergence of MD in coherent problems.
On the positive side, we show that if a problem is strictly coherent (a condition satisﬁed by all strictly convex-concave problems), MD converges almost surely, even in stochastic problems (Theorem 3.1).
However, under null coherence (the “saturated” opposite to strict coherence), MD spirals outwards from the problem’s solutions and may cycle in perpetuity.
The null coherence property covers all bilinear models, so this result generalizes and extends the analysis of Daskalakis et al.
[2018] and Bailey & Piliouras [2018] for gradient descent and follow-the-regularized-leader (FTRL) respectively.
Thus, in and by themselves, gradient / mirror descent methods do not su ﬃ ce for training convoluted, adversarial deep learning models.
To mitigate this deﬁciency, we introduce an extra-gradient step which looks ahead and takes an “optimistic” step along a “future” gradient.
Tracing its roots to the mirror-prox algorithm of Nemirovski [2004] and earlier work by Korpelevich [1976], this technique is often referred to in the machine learning literature as optimistic mirror descent (OMD)
[Rakhlin & Sridharan, 2013].
In the context of GAN training, Gidel et al.
[2018] further introduced a “gradient reuse” mechanism to minimize the computational overhead of back-propagation and proved convergence in stochastic convex-concave problems; similar ideas involve the use of damping in one of the players [Yadav et al., 2018].
However, beyond convex-concave problems, averaging o ﬀ ers no tangible beneﬁts because there is no way to relate the value of the ergodic average to the value of the iterates.
As a result, moving closer to GAN training requires changing both the algorithm’s output as well as the accompanying analysis.
Our ﬁrst result is that the last iterate of OMD converges in all coherent problems, including nullcoherent ones .
As a special case, this generalizes and extends the results of Noor et al.
[2011] for
OGD in pseudo-monotone problems, and also settles in the a ﬃ rmative an open question of Daskalakis et al.
[2018] concerning the convergence of OGD in nonlinear problems.
Going beyond deterministic problems, we also show that OMD converges with probability 1 even in stochastic saddle-point problems that are strictly coherent.
These results suggest that a cheap extra-gradient add-on can lead to signiﬁcant performance gains when applied to state-of-the-art methods (such as Adam).
We validate this prediction for a wide array of standard GAN models in Section 5.
P roblem setup and preliminaries
Saddle-point problems.
Consider a saddle-point problem of the general form
[Equation]
where each feasible region X i , i = 1 , 2, is a compact convex subset of a ﬁnite-dimensional normed space V i ≡ (cid:146)
d i , and f : X ≡ X 1 × X 2 → (cid:146) denotes the problem’s value function.
From a gametheoretic standpoint, (SP) can be seen as a zero-sum game between two optimizing agents (or players ):
Player 1 (the minimizer ) seeks to incur the least possible loss, while Player 2 (the maximizer ) seeks to obtain the highest possible reward – both determined by f ( x 1 , x 2 ).
To solve (SP), we will focus on incremental processes that exploit the individual loss / reward gradients of f (assumed throughout to be at least C 1 -smooth).
Since the individual gradients of f will play a key role in our analysis, we will encode them in a single vector as
[Equation]
and, following standard conventions, we will treat g ( x ) as an element of Y ≡ V ∗ , the dual of the ambient space V ≡ V 1 × V 2 , assumed to be endowed with the product norm (cid:107) x (cid:107)
2 = (cid:107) x 1 (cid:107)
[Equation]
+ (cid:107)
[Equation]
Variational inequalities and coherence.
Most of the literature on saddle-point problems has focused on the monotone case, i.e., when f is convex-concave .
In such problems, it is well known that solutions of (SP) can be characterized equivalently as solutions of the associated (Minty) variational inequality:
[Equation]
Importantly, this equivalence extends well beyond the realm of monotone problems: it trivially includes all bilinear problems ( f ( x 1 , x 2 ) =
x (cid:62) 1 Mx 2 ), pseudo-monotone objectives as in Noor et al.
[2011], etc.
For a concrete example which is not even pseudo-monotone, consider the problem
[Equation]
The only saddle-point of f is x ∗ =
(0 , 0): it is easy to check that x ∗ is also the unique solution of the corresponding problem (VI), despite the fact that f is not pseudo-monotone .
This shows that the equivalence between (SP) and (VI) encompasses a wide range of phenomena that are innately incompatible with convexity / monotonicity, even in the lowest possible dimension; for an in-depth discussion of the links between (SP) and (VI), we refer the reader to Facchinei & Pang [2003].
Motivated by all this, we introduce below the following notion of coherence between (SP) and (VI):
Deﬁnition 2.1.
We say that (SP) is coherent if:
There exists a solution p of (SP) that satisﬁes (VI).
Every solution x ∗ of (SP) satisﬁes (VI) locally, i.e., for all x su ﬃ ciently close to x ∗ .
If (VI) holds as a strict inequality whenever x is not a solution thereof, (SP) will be called strictly coherent ; by contrast, if (VI) holds as an equality for all x ∈ X , we will say that (SP) is null-coherent .
The notion of coherence will play a central part in our considerations, so a few remarks are in order.
First, to the best of our knowledge, its ﬁrst antecedent is a gradient condition examined by Bottou [1998] in the context of nonlinear programming; we borrow the term “coherence” from the more recent paper of Zhou et al.
[2017b].
We should also note here that the set of solutions of a coherent problem might fail to be convex: for instance, if Player 1 controls ( x , y ), and the objective function is f ( x , y )
= x 2 y 2 (i.e., Playerhas no impact in the game), the set of solutions is the non-convex set X ∗ = { ( x , y ) :
x = 0 or y = 0 } .
Moreover, regarding the distinction between coherence and strict coherence, we show in Appendix A that (SP) is strictly coherent when f is strictly convex-concave.
At the other end of the spectrum, typical examples of problems that are null-coherent are bilinear objectives with an interior solution: for instance, f ( x 1 , x 2 ) =
x 1 x 2 with x 1 , x 2 ∈ [ − 1 , 1] has (cid:104) g
( x ) , x (cid:105) =
x 1 x 2 − x 2 x 1 = 0 for all x 1 , x 2 ∈ [ − 1 , 1], so it is null-coherent.
Finally, neither strict, nor null coherence imply a unique solution to (SP), a property which is particularly relevant for GANs (the ﬁrst example above is strictly coherent, but does not admit a unique solution).
Require:
-strongly convex regularizer h :
X → (cid:146) , step-size sequence γ n > 0 1: dom h # initialization do 3:
# gradient
# new
M irror descent
The method.
Motivated by its proliﬁc success in convex programming, our starting point will be the well-known mirror descent (MD) method of Nemirovski & Yudin [1983], suitably adapted to our saddle-point context.
Several variants of the method exist, ranging from dual averaging [Nesterov, 2009] to follow-the-regularized-leader; for a survey, see Hazan [2012] and Bubeck [2015].
The basic idea of mirror descent is to generate a new state variable x + from some starting state x by taking a “mirror step” along a gradient-like vector y .
To do this, let h : X → (cid:146) be a continuous and
K -strongly convex distance-generating function (DGF) on X , i.e.,
[Equation]
for all x , x (cid:48) ∈ X and all t ∈ [0 , 1].
In terms of smoothness (and in a slight abuse of notation), we also assume that the subdi ﬀ erential of h admits a continuous selection , i.e., a continuous function ∇ h : dom
∂ h → Y such that ∇ h ( x ) ∈
∂ h ( x ) for all x ∈ dom ∂ h .
Then, following Bregman [1967], h generates a pseudo-distance on X via the relation
[Equation]
This pseudo-distance is known as the Bregman divergence .
As we show in Appendix B, we have
D ( p , x ) ≥ 12 K (cid:107) x − p (cid:107) 2 , so the convergence of a sequence X n to some target point p can be veriﬁed by showing that D ( p , X n ) → 0.
On the other hand, D ( p , x ) typically fais to be symmetric and / or satisfy the triangle inequality, so it is not a true distance function per se.
Moreover, the level sets of D ( p , x ) may fail to form a neighborhood basis of p , so the convergence of X n to p does not necessarily imply
that D ( p , X n ) → 0; we provide an example of this behavior in Appendix B. For technical reasons, it will be convenient to assume that such phenomena do not occur, i.e., that D ( p , X n ) → 0 whenever
X n → p .
This mild regularity condition is known in the literature as “Bregman reciprocity” [Chen & Teboulle, 1993, Alvarez et al., 2004, Mertikopoulos & Zhou, 2018], and it will be our standing assumption in what follows (note also that it holds trivially for both Examples 3.1 and 3.2 below).
Now, as with standard Euclidean distances, the Bregman divergence generates an associated proxmapping deﬁned as
[Equation]
In analogy with the Euclidean case (discussed below), the prox-mapping (3.3) produces a feasible point x + = P x ( y ) by starting from x ∈ dom ∂ h and taking a step along a dual (gradient-like) vector y ∈ Y .
In this way, we obtain the mirror descent (MD) algorithm
[Equation]
where γ n is a variable step-size sequence and ˆ g n is the calculated value of the gradient vector g ( X n ) at the n -th stage of the algorithm (for a pseudocode implementation, see Section 3).
For concreteness, two widely used examples of prox-mappings are as follows:
Example 3.1 (Euclidean projections) .
When X is endowed with the L 2 norm (cid:107)·(cid:107) 2 , the archetypal prox-function is the (square of the) norm itself, i.e., h ( x ) =
1 2 (cid:107) x (cid:107) 22 .
In that case, D ( p , x ) = 1 2 (cid:107) x − p (cid:107)and the induced prox-mapping is
[Equation]
with Π ( x ) =
arg min x (cid:48)
∈ X (cid:107) x (cid:48) − x (cid:107) 2 denoting the ordinary Euclidean projection onto X .
Example 3.2 (Entropic regularization) .
When X is a d -dimensional simplex, a widely used DGF is the (negative) Gibbs–Shannon entropy h ( x )
= (cid:80) dj
= 1 x j log x j .
This function is 1-strongly convex with respect to the L 1 norm [Shalev-Shwartz, 2011] and the associated pseudo-distance is the
Kullback–Leibler divergence D KL ( p , x ) =
(cid:80) dj
= 1 p j log( p j / x j ); in turn, this yields the prox-mapping
[Equation]
The update rule x ← P x ( y ) is known in the literature as the multiplicative weights (MW) algorithm
[Arora et al., 2012], and is one of the centerpieces for learning in zero-sum games [Freund & Schapire, 1999, Mertikopoulos et al., 2018, Daskalakis et al., 2018], adversarial bandits [Auer et al., 1995], etc.
Regarding the gradient input sequence ˆ g n of (MD), we assume that it is obtained by querying a ﬁrst-order oracle which outputs an estimate of g ( X n ) when called at X n .
This oracle could be either perfect , returning ˆ g n = g ( X n ) for all n , or imperfect , providing noisy gradient estimations.
By that token, we will make the following standard assumptions for the gradient feedback sequence ˆ g n [Nesterov, 2007, Nemirovski et al., 2009, Juditsky et al., 2011]:
a ) Unbiasedness: (cid:133)
[ˆ g n | F n ] = g ( X n ) .
b ) Finite mean square error: (cid:133)
[ (cid:107) ˆ g n (cid:107) 2 ∗ | F n ] ≤ G 2 for some ﬁnite G ≥ 0 .
[Equation]
In the above, (cid:107) y (cid:107) ∗ ≡ sup {(cid:104)
y, x (cid:105) : x ∈ V , (cid:107) x (cid:107) ≤ 1 } denotes the dual norm on Y while F n represents the history (natural ﬁltration) of the generating sequence X n up to stage n (inclusive).
Since ˆ g n is generated randomly from X n at stage n , it is obviously not F n -measurable, i.e., ˆ g n = g ( X n ) + U n + 1 , where U n is an adapted martingale di ﬀ erence sequence with (cid:133)
[ (cid:107) U n + 1 (cid:107) 2 ∗ | F n ] ≤ σ 2 for some ﬁnite σ ≥ 0.
Clearly, when σ = 0, we recover the exact gradient feedback framework ˆ g n = g ( X n ).
Convergence analysis.
When (SP) is convex-concave, it is customary to take as the output of (MD) the so-called ergodic average
[Equation]
or some other average of the sequence X n where the objective is sampled.
The reason for this is that convexity guarantees – via Jensen’s inequality and gradient monotonicity – that a regret-based analysis of (MD) can lead to explicit rates for the convergence of ¯ X n to the solution set of (SP)
[Nemirovski, 2004, Nesterov, 2007].
However, when the problem is not convex-concave, the standard proof techniques for establishing convergence of the method’s ergodic average no longer apply; instead, we need to examine the convergence properties of the generating sequence X n of (MD) directly.
With all this in mind, our main result for (MD) may be stated is as follows:
Theorem 3.1.
Suppose that (MD) is run with a gradient oracle satisfying (3.6) and a variable step-size sequence γ n such that (cid:80) ∞ n = 1 γ n = ∞ .
Then:
a ) If f is strictly coherent and (cid:80) ∞ n = 1 γ 2 n < ∞ , X n converges ( a.s. ) to a solution of (SP) .
b ) If f is null-coherent, the sequence (cid:133)
[ D ( x ∗ , X n )] is non-decreasing for every solution x ∗ of (SP) .
This result establishes an important dichotomy between strict and null coherence: in strictly coherent problems, X n is attracted to the solution set of (SP) ; in null-coherent problems, X n drifts away and cycles without converging.
In particular, this dichotomy leads to the following immediate corollaries:
Corollary 3.2.
Suppose that f is strictly convex-concave.
Then, with assumptions as above, X n converges ( a.s. ) to the ( necessarily unique ) solution of (SP) .
Corollary 3.3.
Suppose that f is bilinear and admits an interior saddle-point x ∗ ∈
X ◦ .
If X 1 (cid:44) x ∗ and (MD) is run with exact gradient input ( σ = 0) , we have lim n →∞ D ( x ∗ , X n ) > 0 .
Since bilinear models include all ﬁnite two-player, zero-sum games, Corollary 3.3 also encapsulates the non-convergence results of Daskalakis et al.
[2018] and Bailey & Piliouras [2018] for gradient
Require: K
-strongly convex regularizer h :
X → (cid:146) , step-size sequence γ n > 0 1: choose X ∈ dom ∂
h # initialization 2: for n = 1 , 2 ,... do 3: oracle query at X returns g # gradient feedback 4:
set X + ← P X ( − γ n g )
# waiting state 5: oracle query at X + returns g + # gradient feedback 6: set X ← P X ( − γ n g + )
# new state 7: end for 8: return X descent and FTRL respectively (for a more comprehensive formulation, see Proposition C.3 in
Appendix C).
The failure of (MD) to converge in this case is due to the fact that, witout a mitigating mechanism in place, a “blind” ﬁrst-order step could overshoot and lead to an outwards spiral, even with a vanishing step-size.
This phenomenon becomes even more pronounced in GANs where it can lead to mode collapse and / or cycles between di ﬀ erent modes.
The next two sections address precisely these issues.
E xtra - gradient analysis
The method.
In convex-concave problems, taking an average of the algorithm’s generated samples as in (3.7) may resolve cycling phenomena by inducing an auxiliary sequence that gravitates towards the “center of mass” of the driving sequence X n (which orbits interior solutions).
However, this technique cannot be employed in problems that are not convex-concave because the structure of f cannot be leveraged to establish convergence of the ergodic average of the process.
In view of this, we replace averaging with an optimistic “extra-gradient” step which uses the obtained information to amortize the next prox step (possibly by exiting the convex hull of generated states).
The seed of this “extra-gradient” idea dates back to Korpelevich [1976] and Nemirovski [2004], and has since found wide applications in optimization theory and beyond – for a survey, see Bubeck [2015] and references therein.
In a nutshell, given a state x , the extra-gradient method ﬁrst generates an intermediate, “waiting” state ˆ x = P x ( − γg ( x )) by taking a prox step as usual.
However, instead of continuing from ˆ x , the method samples g (ˆ x ) and goes back to the original state x in order to generate a new state x + = P x
( − γg (ˆ x )).
Based on this heuristic, we obtain the optimistic mirror descent (OMD) algorithm
[Equation]
where, in obvious notation, ˆ g n and ˆ g n + 1 / 2 represent gradient oracle queries at the incumbent and intermediate states X n and X n + 1 / 2 respectively.
For a pseudocode implementation, see Algorithm 2; see also Rakhlin & Sridharan [2013] for a variant of the method with a momentum step.
Convergence analysis.
In his original analysis, Nemirovski [2004] considered the ergodic average (3.7) of the algorithm’s iterates and established an O (1 / n ) convergence rate in monotone problems.
However, as we explained above, even though this kind of averaging is helpful in convex-concave problems, it does not provide any tangible beneﬁts beyond this class: in more general problems, X n appears to be the most natural solution candidate.
Our ﬁrst result below justiﬁes this choice in the class of coherent problems:
Theorem 4.1.
Suppose that (SP) is coherent and g is L-Lipschitz continuous.
If (OMD) is run with exact gradient input ( σ = 0) and γ n such that 0 < inf n γ n ≤ sup n γ n < K / L , the sequence X n converges monotonically to a solution x ∗ of (SP) , i.e., D ( x ∗ , X n ) decreases monotonically to 0 .
Corollary 4.2.
Suppose that f is bilinear.
If (OMD) is run with assumptions as above, the sequence
X n converges monotonically to a solution of (SP) .
Theorem 4.1 includes as a special case the analysis of Facchinei & Pang [2003, Theorem 12.1.11] for optimistic gradient descent and the more recent results of Noor et al.
[2011] for pseudo-monotone problems.
As in the case of Daskalakis et al.
[2018], Theorem 4.1 shows that optimism (i.e., the
[Figure]
extra-gradient add-on) plays a crucial role in stabilizing (MD): not only does (OMD) converge in problems where (MD) provably fails, but this convergence is, in fact, monotonic.
In other words, at each iteration, (OMD) comes closer to a solution of (SP), whereas (MD) may spiral outwards, ultimately converging to a limit cycle.
This phenomenon can be seen very clearly in Fig.
1, and also in the detailed analysis we provide in Appendix C.
Of course, except for very special cases, the monotonic convergence of X n cannot hold when the gradient input to (OMD) is imperfect: a single “bad” sample of ˆ g n would su ﬃ ce to throw X n o ﬀ -track.
In this case, we have:
Theorem 4.3.
Suppose that (SP) is strictly coherent and (OMD) is run with a gradient oracle satisfying (3.6) and a variable step-size sequence γ n such that (cid:80) ∞ n =
1 γ n = ∞ and (cid:80) ∞ n = 1 γ 2 n < ∞ .
Then, with probability 1 , X n converges to a solution of (SP) .
It is worth noting here that the step-size policy in Theorem 4.3 is di ﬀ erent than that of Theorem 4.1.
This is due to a ) the lack of randomness (which obviates the summability requirement (cid:80) ∞ n = 1 γ 2 n < ∞ in
Theorem 4.1); and b ) the lack of Lipschitz continuity assumption (which, in the case of Theorem 4.
guarantees monotonic decrease at each step, provided the step-size is not too big).
Importantly, the maximum allowable step-size is also controlled by the strong convexity modulus of h , suggesting that the choice of distance-generating function can be ﬁne-tuned further to allow for more aggressive step-size policies – a key beneﬁt of mirror descent methods.
E xperimental results
Gaussian mixture models.
For the experimental validation of our theoretical results, we began by evaluating the extra-gradient add-on in a highly multi-modal mixture of 16 Gaussians arranged in a 4 × 4 grid as in Metz et al.
The generator and discriminator have 6 fully connected layers with 384 neurons and Relu activations (plus an additional layer for data space projection), and the generator generates 2-dimensional vectors.
The output after {4000, 8000, 12000, 16000, 20000} iterations is shown in Fig.
The networks were trained with RMSprop [Tieleman & Hinton, 2012] and Adam [Kingma & Ba, 2014], and the results are compared to the corresponding extra-gradient variant (for an explicit pseudocode representation in the case of Adam, see Daskalakis et al.
[2018] and Appendix E).
Learning rates and hyperparameters were chosen by an inspection of grid search results so as to enable a fair comparison between each method and its look-ahead version.
Overall, the di ﬀ erent optimization strategies without look-ahead exhibit mode collapse or oscillations throughout the training period (we ran all models for at least 20000 iterations in order to evaluate the hopping behavior of the generator).
In all cases, the extra-gradient add-on performs consistently better in learning the multi-modal distribution and greatly reduces occurrences of oscillatory behavior.
Experiments with standard datasets.
In our experiments with Gaussian mixture models (GMMs), the most promising training method was Adam with an extra-gradient step (a concrete pseudocode implementation is provided in Appendix E).
Motivated by this, we trained a Wasserstein-GAN on the CelebA and CIFAR-10 datasets using Adam, both with and without an extra-gradient step.
The architecture employed was a standard DCGAN; hyperparameters and network architecture details may be found in Appendix E. Subsequently, to quantify the gains of the extra-gradient step, we employed the widely used inception score and Fréchet distance metrics, for which we report the results in Fig.
Under both metrics, the extra-gradient add-on provides consistently higher scores after an initial warm-up period (and is considerably more stable).
For visualization purposes, we also present in Fig.
4 an ensemble of samples generated at the end of the training period.
Overall, the generated samples provide accurate feature representation and low distortion (especially in CelebA).
C onclusions
Our results suggest that the implementation of an optimistic, extra-gradient step is a ﬂexible add-on that can be easily attached to a wide variety of GAN training methods (RMSProp, Adam, SGA, etc.), and provides noticeable gains in performance and stability.
From a theoretical standpoint, the dichotomy between strict and null coherence provides a justiﬁcation of why this is so: optimism eliminates cycles and, in so doing, stabilizes the method.
We ﬁnd this property particularly appealing because it paves the way to a local analysis with provable convergence guarantees in multi-modal settings, and beyond zero-sum games; we intend to examine this question in future work.
A C oherent saddle - point problems
We begin our discussion with some basic results on coherence:
Proposition A.1.
If f is convex-concave, (SP) is coherent.
In addition, if f is strictly convex-concave, (SP) is strictly coherent.
Proof.
Let x ∗ be a solution point of (SP).
Since f is convex-concave, ﬁrst-order optimality gives
[Equation]
and
[Equation]
Combining the two, we readily obtain the (Stampacchia) variational inequality
[Equation]
In addition to the above, the fact that f is convex-concave also implies that g ( x ) is monotone in the sense that
[Equation]
for all x , x (cid:48) ∈ X
[Bauschke & Combettes, 2017].
Thus, setting x (cid:48) ← x ∗ in (A.3) and invoking (A.2), we get
[Equation]
i.e., (VI) is satisﬁed.
To establish the converse implication, focus for concreteness on the minimizer, and note that (VI) implies that
[Equation]
Now, if we ﬁx some x 1 ∈ X 1 and consider the function φ ( t ) =
f
( x ∗ 1 + t ( x 1 − x ∗ 1 ) , x ∗ 2 ), the inequality (A.5) yields
[Equation]
for all t ∈ [0 , 1].
This implies that φ is nondecreasing, so f ( x 1 , x ∗ 2 ) =
φ (1) ≥ φ (0) =
f ( x ∗ 1 , x ∗ 2 ).
The maximizing component follows similarly, showing that x ∗ is a solution of (SP) and, in turn, establishing that (SP) is coherent.
For the strict part of the claim, the same line of reasoning shows that if (cid:104) g ( x ) , x − x ∗ (cid:105) = 0 for some x that is not a saddle-point of f , the function φ ( t ) deﬁned above must be constant on [0 , 1], indicating in turn that f cannot be strictly convex-concave, a contradiction.
[Equation]
We proceed to show that the solution set of a coherent saddle-point problem is closed (we will need this regularity result in the convergence analysis of Appendix C):
Lemma A.2.
Let X ∗ denote the solution set of (SP) .
If (SP) is coherent, X ∗ is closed.
Proof.
Let x ∗ n , n = 1 , 2 , . .
, be a sequence of solutions of (SP) converging to some limit point x ∗ ∈ X .
To show that X ∗ is closed, it su ﬃ ces to show that x ∗ ∈ X .
Indeed, given that (SP) is coherent, every solution thereof satisﬁes (VI), so we have (cid:104) g ( x ) , x − x ∗ n (cid:105) ≥for all x ∈ X .
With x ∗ n → x ∗ as n → ∞ , it follows that
[Equation]
i.e., x ∗ satisﬁes (VI).
By coherence, this implies that x ∗ is a solution of (SP), as claimed.
[Equation]
B P roperties of the B regman divergence
In this appendix, we provide some auxiliary results and estimates that are used throughout the convergence analysis of Appendix C. Some of the results we present here (or close variants thereof) are not new [see e.g., Nemirovski et al., 2009, Juditsky et al., 2011].
However, the hypotheses used to obtain them vary wildly in the literature, so we provide all the necessary details for completeness.
To begin, recall that the Bregman divergence associated to a K -strongly convex distance-generating function h : X → (cid:146) is deﬁned as
[Equation]
with ∇ h ( x ) denoting a continuous selection of ∂ h ( x ).
The induced prox-mapping is then given by
[Equation]
and is deﬁned for all x ∈ dom ∂ h ,
y ∈ Y (recall here that Y ≡ V ∗ denotes the dual of the ambient vector space V ).
In what follows, we will also make frequent use of the convex conjugate h ∗ :
Y → (cid:146) of h , deﬁned as
[Equation]
By standard results in convex analysis [Rockafellar, 1970, Chap. 26], h ∗ is di ﬀ erentiable on Y and its gradient satisﬁes the identity
[Equation]
For notational convenience, we will also write
[Equation]
and we will refer to Q : Y → X as the mirror map generated by h .
All these notions are related as follows:
Lemma B.1.
Let h be a distance-generating function on X .
Then, for all x ∈ dom ∂ h, y ∈ Y , we have:
[Equation]
Finally, if x = Q ( y ) and p ∈ X , we have
[Equation]
Remark.
By (B.6b), we have ∂ h ( x + ) (cid:44) ∅ , i.e., x + ∈ dom ∂ h .
As a result, the update rule x ← P x ( y ) is well-posed , i.e., it can be iterated in perpetuity.
Proof of Lemma B.1.
For (B.6a), note that x solves (B.3) if and only if y − ∂ h ( x ) (cid:51) 0, i.e., if and only if y ∈ ∂ h ( x ).
Similarly, comparing (B.2) with (B.3), it follows that x + solves (B.2) if and only if ∇ h ( x ) +
y ∈ ∂
h ( x + ), i.e., if and only if x + = Q ( ∇ h ( x ) + y ).
For (B.7), by a simple continuity argument, it su ﬃ ces to show that the inequality holds for interior p ∈
X ◦ .
To establish this, let
[Equation]
Since h is strongly convex and
y ∈ ∂
h ( x ) by (B.6a), it follows that φ ( t ) ≥ 0 with equality if and only if t = 0.
Since ψ ( t ) = (cid:104)∇ h ( x + t ( p − x )) − y, p − x (cid:105) is a continuous selection of subgradients of φ and both φ and ψ are continuous on [0 , 1], it follows that φ is continuously di ﬀ erentiable with φ (cid:48)
=
ψ on [0 , 1].
Hence, with φ convex and φ ( t ) ≥ 0 = φ (0) for all t ∈ [0 , 1], we conclude that φ (cid:48) (0) =
(cid:104)∇ h ( x ) − y, p − x (cid:105) ≥ 0, which proves our assertion.
[Equation]
We continue with some basic bounds on the Bregman divergence before and after a prox step.
The basic ingredient for these bounds is a generalization of the (Euclidean) law of cosines which is known in the literature as the “three-point identity” [Chen & Teboulle, 1993]:
Lemma B.2.
Let h be a distance-generating function on X .
Then, for all p ∈ X and all x , x (cid:48) ∈ dom ∂ h, we have
[Equation]
Proof.
By deﬁnition, we have:
[Equation]
Our claim then follows by adding the last two lines and subtracting the ﬁrst.
[Equation]
With this identity at hand, we have the following series of upper and lower bounds:
Proposition B.3.
Let h be a K-strongly convex distance-generating function on X , ﬁx some p ∈ X , and let x + = P x ( y ) for x ∈ dom ∂ h, y ∈ Y .
We then have:
[Equation]
11c
[Equation]
Proof of (B.11a) .
By the strong convexity of h , we get
[Equation]
so (B.11a) follows by gathering all terms involving h and recalling the deﬁnition of D ( p , x ).
(cid:4)
Proof of (B.11b) and (B.11c) .
By the three-point identity (B.9), we readily obtain
[Equation]
In turn, this gives
[Equation]
where, in the last step, we used (B.7) and the fact that x + = P x ( y ), so ∇ h ( x ) + y ∈
∂ h ( x + ).
The above is just (B.11b), so the ﬁrst part of our proof is complete.
For (B.11c), the bound (B.14) gives
[Equation]
Therefore, by Young’s inequality [Rockafellar, 1970], we get
[Equation]
and hence
[Equation]
with the last step following from Lemma B.1 applied to x in place of p .
[Equation]
The ﬁrst part of Proposition B.3 shows that X n converges to p if D ( p , X n ) → 0.
However, as we mentioned in the main body of the paper, the converse may fail: in particular, we could have lim inf n →∞ D ( p , X n ) > 0 even if X n → p .
To see this, let X be the L 2 ball of (cid:146) d and take h ( x ) =
− (cid:113) 1 − (cid:107) x (cid:107) 22 .
Then, a straightforward calculation gives
[Equation]
whenever (cid:107) p (cid:107) 2 = 1.
The corresponding level sets L c ( p ) =
{ x ∈ (cid:146) d : D ( p , x ) =
c } of D ( p , · ) are given by the equation
[Equation]
which admits p as a solution for all c ≥
(so p belongs to the closure of L c ( p ) even though D ( p , p ) =by deﬁnition).
As a result, under this distance-generating function, it is possible to have X n → p even when lim inf n →∞ D ( p , X n ) > 0 (simply take a sequence X n that converges to p while remaining on the same level set of D ).
As we discussed in the main body of the paper, such pathologies are discarded by the Bregman reciprocity condition
[Equation]
This condition comes into play at the very last part of the proofs of Theorems 3.1 and 4.1; other than that, we will not need it in the rest of our analysis.
Finally, for the analysis of the OMD algorithm, we will need to relate prox steps taken along di ﬀ erent directions:
Proposition B.4.
Let h be a K-strongly convex distance-generating function on X and ﬁx some p ∈ X , x ∈ dom ∂
h.
Then:
a ) For all y 1 , y 2 ∈ Y , we have:
[Equation]
i.e., P x is (1 / K ) -Lipschitz.
b )
In addition, letting x + 1 =
P x ( y 1 ) and x + 2 = P x ( y 2 ) , we have:
[Equation]
Proof.
We begin with the proof of the Lipschitz property of P x .
Indeed, for all p ∈ X , (B.7) gives
[Equation]
and
[Equation]
Therefore
[Equation]
setting p ← x + 2 in (B.23a), p ← x + 1 in (B.23b) and rearranging
[Equation]
we obtain
[Equation]
By the strong convexity of h
[Equation]
we also have
[Equation]
Hence, combining (B.24) and (B.25), we get
[Equation]
26) and our assertion follows.
For the second part of our claim
[Equation]
the bound (B.11b) of Proposition B.3 applied to x + 2 =
P x ( y 2 ) readily gives
[Equation]
) thus proving (B.22a).
To complete our proof, note that (B
[Equation]
11b) with p ← x + 2 gives
[Equation]
or, after rearranging,
[Equation]
We thus obtain
[Equation]
(B
[Equation]
30) where we used Young’s inequality and (B.11a) in the second inequality.
The bound (B.22b) then follows by substituting (B.30) in (B.27).
(cid:4)
C C onvergence analysis of mirror descent
We begin by recalling the deﬁnition of the mirror descent algorithm.
With notation as in the previous section, the algorithm is deﬁned via the recursive scheme
[Equation]
where γ n is a variable step-size sequence and ˆ g n is the calculated value of the gradient vector g ( X n ) at the n -th stage of the algorithm.
As we discussed in the main body of the paper, the gradient input sequence ˆ g n of (MD) is assumed to satisfy the standard oracle assumptions
a ) Unbiasedness: (cid:133)
[ˆ g n | F n ] = g ( X n ) .
b ) Finite mean square: (cid:133)
[ (cid:107) ˆ g n (cid:107) 2 ∗ | F n ] ≤ G 2 for some ﬁnite G ≥ 0 .
where F n represents the history (natural ﬁltration) of the generating sequence X n up to stage n (inclusive).
With this preliminaries at hand, our convergence proof for (MD) under strict coherence will hinge on the following results:
Proposition C.1.
Suppose that (SP) is coherent and (MD) is run with a gradient oracle satisfying (3.6) and a variable step-size γ n such that (cid:80) ∞ n = 1 γ 2 n < ∞ .
If x ∗ ∈ X is a solution of (SP) , the Bregman divergence D ( x ∗ , X n ) converges ( a.s. ) to a random variable D ( x ∗ ) with (cid:133)
[ D ( x ∗ )] < ∞ .
Proposition C.2.
Suppose that (SP) is strictly coherent and (MD) is run with a gradient oracle satisfying (3.6) and a step-size γ n such that (cid:80) ∞ n =
1 γ n = ∞ and (cid:80) ∞ n = 1 γ 2 n < ∞ .
Then, with probability 1 , there exists a ( possibly random ) solution x ∗ of (SP) such that lim inf n →∞ D ( x ∗ , X n ) = 0 .
Proposition C.1 can be seen as a “dichotomy” result: it shows that the Bregman divergence is an asymptotic constant of motion, so (MD) either converges to a saddle-point x ∗ (if D ( x ∗ ) = 0) or to some nonzero level set of the Bregman divergence (with respect to x ∗ ).
In this way, Proposition C. rules out more complicated chaotic or aperiodic behaviors that may arise in general – for instance, as in the analysis of Palaiopanos et al.
[2017] for the long-run behavior of the multiplicative weights algorithm in two-player games.
However, unless this limit value can be somehow predicted (or estimated) in advance, this result cannot be easily applied.
This is the main role of Proposition C.2: it shows that (MD) admits a subsequence converging to a solution of (SP) so, by (B.20), the limit of
D ( x ∗ , X n ) must be zero.
Our ﬁrst step is to prove Proposition C.2.
To do this, we ﬁrst recall the following law of large numbers for L 2 martingales:
Theorem (Hall & Heyde, 1980, Theorem 2.18) .
Let Y n = (cid:80) nk
= 1 ζ k be a martingale and T n a nondecreasing sequence such that lim n →∞ τ n = ∞ .
Then,
[Equation]
on the set (cid:80)
[Equation]
With this in place, we have:
Proof of Proposition C.2.
We begin with the technical observation that the solution set X ∗ of (SP) is closed – and hence, compact (cf.
Lemma A.2 in Appendix A).
Clearly, if X ∗ =
X , there is nothing to show; hence, without loss of generality, we may assume in what follows that X ∗ (cid:44) X .
Assume now ad absurdum that, with positive probability, the sequence X n generated by (MD) admits no limit points in X ∗ .
Conditioning on this event, and given that X ∗ is compact, there exists a (nonempty) compact set C ⊂ X such that C ∩ X ∗ = ∅ and X n ∈ C for all su ﬃ ciently large n .
Moreover, letting p be as in Deﬁnition 2.1, we have (cid:104) g ( x ) , x − p (cid:105) > 0 whenever x ∈ C .
Therefore, by the continuity of g and the compactness of X ∗ and C , there exists some a > 0 such that
[Equation]
To proceed, let D n = D ( p , X n ).
Then, by Proposition B.3, we have
[Equation] [Equation]
where, in the last line, we set U n + 1 = ˆ g n − g ( X n ), ξ n + 1 = −(cid:104)
U n + 1 , X n − p (cid:105) , and we invoked the assumption that (SP) is coherent.
Hence, telescoping (C.3) yields the estimate
[Equation]
Subsequently, letting τ n = (cid:80) nk = 1 γ k and using (C.2), we obtain
[Equation]
By the unbiasedness hypothesis of (3.6) for U n , we have (cid:133) [ ξ n + 1 | F n ] = (cid:104) (cid:133)
[ U n + 1 | F n ] , X n − p (cid:105) =(recall that X n is F n -measurable by construction).
Moreover, since U n is bounded in L 2 and γ n is (cid:96)summable (by assumption), it follows that
[Equation]
Therefore, by the law of large numbers for L 2 martingales stated above [Hall & Heyde, 1980,
Theorem 2.18], we conclude that τ − 1 n (cid:80) nk
=
γ k ξ k + 1 converges to 0 with probability 1.
Finally, for the last term of (C.4), let S n + 1 = (cid:80) nk
= 1 γ 2 k (cid:107) ˆ g k (cid:107) 2 ∗ .
Since ˆ g k is F n -measurable for all k = 1 , 2 , . . .
, n − 1, we have
[Equation]
i.e., S n is a submartingale with respect to F n .
Furthermore, by the law of total expectation, we also have
[Equation]
(C
[Equation]
) so S n is bounded in L 1 .
Hence, by Doob’s submartingale convergence theorem [Hall & Heyde, 1980,
Theorem 2.5], we conclude that S n converges to some (almost surely ﬁnite) random variable S ∞ with (cid:133)
[ S ∞ ] < ∞ , implying in turn that lim n →∞ S n + 1 /τ n =
(a.s.).
Applying all of the above, the estimate (C.4) gives D n + 1 ≤ D 1 − a τ n / 2 for su ﬃ ciently large n , so
D ( p , X n ) → −∞ , a contradiction.
Going back to our original assumption, this shows that, at least one of the limit points of X n must lie in X ∗ (a.s.), as claimed.
[Equation]
We now turn to the proof of Proposition C.1:
Proof of Proposition C.1.
Let x ∗ ∈
X ∗ be a limit point of X n , as guaranteed by Proposition C.2, and let D n = D ( x ∗ , X n ).
Then, by Proposition B.3, we have
[Equation]
and hence, for large enough n :
[Equation]
(C.
[Equation]
where we used the ansatz that (cid:104) g ( X n ) , X n − x ∗ (cid:105) ≤ 0 for su ﬃ ciently large n (to be proved below), and, as in the proof of Proposition C.2, we set U n + 1 = ˆ g n − g ( X n ), ξ n + 1 = −(cid:104)
U n + 1 , X n − x ∗ (cid:105) .
Thus, conditioning on F n and taking expectations, we get
[Equation]
where we used the oracle assumptions (3.6) and the fact that X n is F n -measurable (by deﬁnition).
Now, letting R n = D n + (2 K ) − 1 G 2 (cid:80) ∞
k = n γ 2 k , the estimate (C.10) gives
[Equation]
i.e., R n is an F n -adapted supermartingale.
Since (cid:80) ∞ n = 1 γ 2 n < ∞ , it follows that
[Equation]
i.e., R n is uniformly bounded in L 1 .
Thus, by Doob’s convergence theorem for supermartingales [Hall & Heyde, 1980, Theorem 2.5], it follows that R n converges (a.s.) to some ﬁnite random variable R ∞ with (cid:133)
[ R ∞ ] < ∞ .
In turn, by inverting the deﬁnition of R n , this shows that D n converges (a.s.) to some random variable D ( x ∗ ) with (cid:133)
[ D ( x ∗ )] < ∞ , as claimed.
It remains to be shown that (cid:104) g ( X n ) , X n − x ∗ (cid:105) ≥ 0 for su ﬃ ciently large n .
By Deﬁnition 2.1, this amounts to showing that, for all large enough n , X n lies in a neighborhood U of x ∗ such that (VI) holds.
Since x ∗ has been chosen so that lim inf D ( x ∗ , X n ) = 0, it follows that, for all ε > 0, there exists some n 0 such that (cid:80) ∞ n = n 0 γ 2 n < ε and X n 0 ∈ U .
Hence, arguing in the same way as in the proof of Theorem 5.2 of Zhou et al.
[2017a], we conclude that (cid:144) ( X n ∈ U for all n ≥ n 0 )
= 1, implying in turn that (cid:104) g ( X n ) , X n − x ∗ (cid:105) ≥ 0 for all n ≥ n 0 .
This proves our last claim and concludes our proof.
(cid:4)
With all this at hand, we are ﬁnally in a position to prove our main result for (MD):
Proof of Theorem 3.1 ( a ) .
Proposition C.2 shows that, with probability 1, there exists a (possibly random) solution x ∗ of (SP) such that lim inf n →∞ (cid:107) X n − x ∗ (cid:107) =
and, hence, lim inf n →∞ D ( x ∗ , X n ) =(by Bregman reciprocity).
Since lim n →∞ D ( x ∗ , X n ) exists with probability 1 (by Proposition C.1), it follows that lim n →∞ D
[Equation]
x
[Equation]
X
[Equation]
i.e., X n converges to x ∗ .
(cid:4)
We proceed with the negative result hinted at in the main body of the paper, namely the failure of (MD) to converge under null coherence:
Proof of Theorem 3.1 ( b ) .
The evolution of the Bregman divergence under (MD) satisﬁes the identity
[Equation]
where, in the last line, we used the null coherence assumption (cid:104) g ( x ) , x − x ∗ (cid:105) = 0 for all x ∈ X .
Since
D ( X n , X n + 1 ) ≥ 0, taking expecations above shows that D ( x ∗ , X n ) is nondecreasing, as claimed.
(cid:4)
With Theorem 3.1 at hand, the proof of Corollary 3.2 is an immediate consequence of the fact that strictly convex-concave problems satisfy strict coherence (Proposition A.1).
As for Corollary 3.3, we provide below a more general result for two-player, zero-sum ﬁnite games.
To state it, let A i
= { 1 , . . .
, A i } , i = 1 , 2, be two ﬁnite sets of pure strategies , and let X i =
∆ ( A i ) denote the set of mixed strategies of player i .
A ﬁnite, two-player zero-sum game is then deﬁned by a matrix M ∈ (cid:146)
A 1 ×
A 2 so that the loss of Player 1 and the reward of Player 2 in the mixed strategy proﬁle x
= ( x 1 , x 2 ) ∈
X are concurrently given by f
[Equation]
Then, writing Γ ≡ Γ ( A 1 , A 2 , M ) for the resulting game, we have:
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Proposition C.3.
Let Γ be a two-player zero-sum game with an interior Nash equilibrium x ∗ .
If
X 1 (cid:44) x ∗ and (MD) is run with exact gradient input ( σ 2 = 0) , we have lim n →∞ D ( x ∗ , X n ) > 0 .
If, in addition, (cid:80) ∞ n = 1 γ 2 n < ∞ , lim n →∞ D ( x ∗ , X n ) is ﬁnite.
Remark.
Note that non-convergence does not require any summability assumptions on γ n .
In words, Proposition C.3 states that (MD) does not converge in ﬁnite zero-sum games with a unique interior equilibrium and exact gradient input: instead, X n cycles at positive Bregman distance from the game’s Nash equilibrium.
Heuristically, the reason for this behavior is that, for small γ → 0, the incremental step V γ ( x ) =
P x ( − γg ( x )) − x of (MD) is essentially tangent to the level set of D ( x ∗ , · ) that passes through x . 4 For ﬁnite γ > 0, things are even worse because V γ ( x ) points noticeably away from x , i.e., towards higher level sets of D .
As a result, the “best-case scenario” for (MD) is to orbit x ∗ (when γ → 0); in practice, for ﬁnite γ , the algorithm takes small outward steps throughout its runtime, eventually converging to some limit cycle farther away from x ∗ .
We make this intuition precise below (for a schematic illustration, see also Fig.
1 above):
Proof of Proposition C.3.
Write v 1 ( x ) =
− Mx 2 and v 2 ( x ) =
x (cid:62) 1 M for the players’ payo ﬀ vectors under the mixed strategy proﬁle x
= ( x 1 , x 2 ).
By construction, we have g ( x ) =
−
( v 1 ( x ) , v 2 ( x )).
Furthermore, since x ∗ is an interior equilibrium of f , elementary game-theoretic considerations show that v 1 ( x ∗ ) and v 2 ( x ∗ ) are both proportional to the constant vector of ones.
We thus get
[Equation]
where, in the last line, we used the fact that x ∗ is interior.
This shows that f satisﬁes null coherence, so our claim follows from Theorem 3.1(b).
For our second claim, arguing as above and using (B.11c), we get
[Equation]
with G = max x 1 ∈ X 1 , x
[Equation]
X
[Equation]
( − Mx 2 , x
[Equation]
1 M )
[Equation]
∗ .
Telescoping this last bound yields
[Equation]
so D ( x ∗ , X n ) is also bounded from above.
Therefore, with D ( x ∗ , X n ) nondecreasing, bounded from above and D ( x ∗ , X 1 ) > 0, it follows that lim n →∞ D ( x ∗ , X n ) > 0, as claimed.
[Equation]
D C onvergence analysis of optimistic mirror descent
We now turn to the optimistic mirror descent (OMD) algorithm, as deﬁned by the recursion
[Equation]
with X 1 initialized arbitrarily in dom ∂ h , and ˆ g n , ˆ g n + 1 / 2 representing gradient oracle queries at the incumbent and intermediate states X n and X n + 1 / 2 respectively.
The heavy lifting for our analysis is provided by Proposition B.4, which leads to the following crucial lemma:
Lemma D.1.
Suppose that (SP) is coherent and g is L-Lipschitz continuous.
With notation as above and exact gradient input ( σ = 0) , we have
[Equation]
with p as in Deﬁnition 2.1.
Proof.
Substituting x ← X n , y 1 ← − γ n g ( X n ), and y 2 ← − γ n g ( X n + 1 / 2 ) in Proposition B.4, we obtain the estimate:
[Equation]
where we used the fact that g is L -Lipschitz and that p is a solution of (SP) such that (VI) holds for all x ∈ X .
[Equation]
We are now ﬁnally in a position to prove Theorem 4.1 (reproduced below for convenience):
Theorem.
Suppose that (SP) is coherent and g is L-Lipschitz continuous.
If (OMD) is run with exact gradient input and a step-size sequence γ n such that
[Equation]
the sequence X n converges monotonically to a solution x ∗ of (SP) , i.e., D ( x ∗ , X n ) is non-increasing and converges to 0 .
Proof.
Let x ∗ be a solution of (SP) such that (VI) holds for all x ∈ X (that such a solution exists is a consequence of Deﬁnition 2.1).
Then, by the stated assumptions for γ n , Lemma D.1 yields
[Equation]
where α ∈ (0 , 1) is such that γ 2 n < α K / L for all n (that such an α exists is a consequence of the assumption that sup n γ n < K / L ).
This shows that D ( x ∗ , X n ) is non-decreasing for every solution x ∗ of (SP).
Now, telescoping (D.1), we obtain
[Equation]
and hence:
[Equation]
With sup n γ n < K / L , the above estimate readily yields (cid:80) ∞ n = 1 (cid:107) X n + 1 / 2 − X n (cid:107) 2 < ∞ , which in turn implies that (cid:107) X n + 1 / 2 − X n (cid:107) → 0 as n → ∞ .
By the compactness of X , we further infer that X n admits an accumulation point ˆ x , i.e., there exists a subsequence n k such that X n k → ˆ x as k → ∞ .
Since (cid:107) X n k + 1 / 2 − X n k (cid:107) → 0, this also implies that
X n k + 1 / 2 converges to ˆ x as k → ∞ .
Further, by passing to a subsequence if necessary, we may also assume without loss of generality that γ n k converges to some limit value γ > 0.
Then, by the Lipschitz continuity of the prox-mapping (cf.
Proposition B.4), we readily obtain
[Equation]
i.e., ˆ x is a solution of (SP).
Since D (ˆ x , X n ) is nonincreasing and lim inf n →∞ D (ˆ x , X n ) = 0 (by Bregman reciprocity), we conclude that lim inf n →∞ D (ˆ x , X n ) = 0, i.e., X n converges to ˆ x .
[Equation]
Our last result concerns the convergence of (OMD) in strictly coherent problems with a stochastic gradient oracle:
Proof of Theorem 4.3.
Our argument hinges on the inequality
[Equation]
which is obtained from the two-point estimate (B.22b) by substituting x ← x ∗ , x 1 ← X n , y 1 ← ˆ g n , x + 1 ← X n + 1 / 2 = P X n ( − γ n ˆ g n ), y 2 ← ˆ g n + 1 / 2 , and x + 2 ← X n = P X n ( − γ n ˆ g n + 1 / 2 )
Then, working as in the proof of Proposition C.1, we obtain the following estimate for the sequence D n = D ( x ∗ , X n ):
[Equation]
where U + n + 1 = ˆ g n + 1 / 2 − g ( X n + 1 / 2 ) denotes the martingale part of ˆ g n + 1 /
and we have set ξ + n + 1 = (cid:104) U + n + 1 , X n + 1 / 2 − x ∗ (cid:105) .
Since (cid:133) [ (cid:107) g n (cid:107) 2 ∗ | X n , . . .
, X 1 ] and (cid:133)
[ (cid:107) g n + 1 / 2 (cid:107) 2 ∗ |
X n + 1 / 2 , . .
, X 1 ] are both bounded by
G 2 , we get the bound (cid:133)
[ D
[Equation]
D.10)
Following the same steps as in the proof of Proposition C.1, it then follows that D n converges to some limit value D ∞ .
To proceed, telescoping (D.9) also yields
[Equation]
Each term in the above bound can be controlled in the same way as the corresponding terms in (C.4).
Thus, repeating the steps in the proof of Proposition C.2, it follows that there exists a subsequence of X n + 1 / 2 (and hence also of X n ) which converges to x ∗ .
Our claim then follows by combining the two intermediate results above in the same way as in the proof of Theorem 3.1(a); to avoid needless repetition, we omit the details.
[Equation]
E E xperimental results E.1 A dam with extra - gradient step
For most of our experiments, the method that seemed to generate the best results was Adam and its optimistic version [Daskalakis et al., 2018]; for a pseudocode iplementation, see Algorithmbelow.
We also noticed empirically that it was more e ﬃ cient to use two di ﬀ erent sets of moment estimates ( m t , v t ) and ( m (cid:48) t , v (cid:48) t ) for the ﬁrst and the second gradient steps.
We used this algorithm for our experiments with both GMMs and the CelebA / CIFAR-10 datasets.
Compute stochastic gradient: ∇ θ, t
Update biased estimate of 1st momentum: m t = β 1 m t − 1 + (1 − β 1 ) ∇ θ, t
Update biased estimate of 2nd momentum: v t = β 2 v t − 1 + (1 − β 2 ) ∇ 2 θ, t
Compute bias corrected 1st moment: ˆ m t = m t 1 − β t
Compute bias corrected 2nd moment: ˆ v t = v t 1 − β t
Perform: θ (cid:48) t = θ t − 1 − η ˆ m t √ ˆ v t + (cid:15)
Compute stochastic gradient: ∇ θ (cid:48) , t
Update biased estimate of 1st momentum: m (cid:48) t
= β 1 m (cid:48) t − 1 + (1 − β 1 ) ∇ θ (cid:48) , t
Update biased estimate of 2nd momentum: v (cid:48) t
= β 2 v (cid:48)
t − 1 + (1 − β 1 )
[Table]
θ
[Table]
, t
Compute bias
[Table] [Table]
m
[Table]
t =
[Table]
t
Compute bias
[Table]
Return θ t
[Table]
E.2 E xperiments with standards datasets
In this section we present the results of our image experiments using OMD training techniques.
Inception and FID scores obtained by our model during training were reported in Fig.
3: as can be seen there, the extra-gradient add-on improves the performance of GAN training and e ﬃ ciently stabilizes the model; without the extra-gradient step, performance tends to drop noticeably after approximately 100 k steps.
For ease of comparison, we provide below a collection of samples generated by Adam and optimistic
Adam in the CelebA and CIFAR-10 datasets.
Especially in the case of CelebA, the generated samples are consistently more representative and faithful to the target data distribution.
E.2.1 N etwork A rchitecture and hyperparameters
For the reproducibility of our experiments, we provide Table 1 and Table 2 the network architectures and the hyperparameters of the GANs that we used.
The architecture employed is a standard DCGAN architecture with a 5-layer generator with batchnorm, and an 8-layer discriminator.
The generated samples were 32 × 32 × 3 RGB images.
[Table]