A Greedy Framework for First-Order Optimization

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1 A Greedy Framework for First-Order Optimization Jacob Steinhardt Department of Computer Science Stanford University Stanford, CA Jonathan Huggins Department of EECS Massachusetts Institute of Technology Cambridge, MA 039 Introduction. Recent work has shown many connections between conditional gradient and other first-order optimization methods, such as herding [3] and subgradient descent []. By considering a type of proximal conditional method, which we call boosted mirror descent (BMD), we are able to unify all of these algorithms into a single framework, which can be interpreted as taking successive arg-mins of a sequence of surrogate functions. Using a standard online learning analysis based on Bregman divergences, we are able to demonstrate an O(/T ) convergence rate for all algorithms in this class. Setup. More concretely, pose that we are given a function L : U Θ R defined by L(u, ) = h(u) + u, R() () and wish to find the saddle point L def = min max L(u, ). () u We should think of u as the primal variables and as the dual variables; we will assume throughout that h and R are both convex. We will also abuse notation and define L(u) def = max L(u, ); we can equivalently write L(u) as L(u) = h(u) + R (u), (3) where R is the Fenchel conjugate of R. Note that L(u) is a convex function. Moreover, since R R is a one-to-one mapping, for any convex function L and any decomposition of L into convex functions h and R, we get a corresponding two-argument function L(u, ). Primal algorithm. Given the function L(u, ), we define the following optimization procedure, which will generate a sequence of points (u, ), (u, ),... converging to a saddle point of L. First, take a sequence of weights α, α,..., and for notational convenience define s= û t = α su s s= α s s= and ˆt = α s s s= α. s Then the primal boosted mirror descent (PBMD) algorithm is the following iterative procedure:. u arg min u h(u). t arg max Θ, u t R() = R (u t ) 3. u t+ arg min u h(u) + ˆ t, u = h ( ˆ t ) As long as h is strongly convex, for the proper choice of α t we obtain the bound (see Corollary ): L(û T, ) L + O(/T ). (4) Θ As an example, pose that we are given a γ-strongly convex function f: that is, f(x) = γ x + f 0 (x), where f 0 is convex. Then we let h(x) = γ x, R (x) = f 0 (x), and obtain the updates: Both authors contributed equally to this work.

2 . u = 0. t = f 0 (u t ) 3. u t+ = γ ˆ t = s= αs f0(us) γ s= αs We therefore obtain a variant on subgradient descent where u t+ is a weighted average of the first t subgradients (times a step size /γ). Note that these are the subgradients of f 0, which are related to the subgradients of f by f 0 (x) = f(x) γx. Dual algorithm. We can also consider the dual form of our mirror descent algorithm (dual boosted mirror descent, or DBMD):. arg min R(). u t arg min u h(u) + t, u = h ( t ) 3. t+ arg max Θ, û t R() = R (û t ) Convergence now hinges upon strong convexity of R rather than h, and we obtain the same O(/T ) convergence rate (see Corollary 4): L(û T, ) L + O(/T ). (5) Θ { 0 : u K An important special case is h(u) =, where K is some compact set. Also let : u K R = f, where f is an arbitrary strongly convex function. Then we are minimizing f over the compact set K, and we obtain the following updates, which are equivalent to (standard) conditional gradient or Frank-Wolfe:. = f(0). u t arg min u K t, u 3. t+ = f(û t ) Our notation is slightly different from previous presentations in that we use linear weights (α t ) instead of geometric weights (often denoted ρ t, as in []). However, under the mapping α t = ρ t / t s= ( ρ s), we obtain an algorithm equivalent to the usual formulation. Optimality. The solutions generated by conditional gradient have an attractive sparsity property: the solution after the t-th iteration is a convex combination of t extreme points of the optimized space. In [4] it is shown that conditional gradient is asymptotically optimal amongst algorithms with sparse solutions. This suggests that the O(/T ) convergence rate obtained in this paper cannot be improved without further assumptions. For further discussion of the properties of conditional gradient, see Jaggi [4]. Primal vs. dual algorithms. It is worth taking a moment to contrast the primal and dual algorithms. Note that both algorithms have a primal convergence guarantee (i.e. both guarantee that û T is close to optimal). The sense in which the latter algorithm is a dual algorithm is that it hinges on strong convexity of the dual, as opposed to strong convexity of the primal. If we care about dual convergence instead of primal convergence, the only change we need to make is to take ˆ T at the end of the algorithm instead of û T. (This can be seeing by defining the loss function L (, u) def = L(u, ), which inverts the role of u and but yields the same updates as above.) Discussion. Our framework is intriguing in that it involves a purely greedy minimization of surrogate loss functions (alternating between the primal and dual), and yet is powerful enough to capture both (a variant of) subgradient descent and conditional gradient descent, as well as a host of other first-order methods. An example of a more complex first-order method captured by our framework is the low-rank SDP solver introduced by Arora, Hazan, and Kale []. Briefly, the AHK algorithm seeks to minimize

3 m j= (Tr(AT j X) b j) subject to the constraints X 0 and Tr(X) ρ. We can then define { 0 : X 0 and Tr(X) ρ h(x) = (6) : else and R (X) = m j= (Tr(AT j X) b j ). (7) Note that this decomposition is actually a special case of the conditional gradient decomposition above, and so we obtain the updates m X t+ argmin X 0,T r(x) ρ [Tr( ˆX ] j t ) b j Tr( j X), (8) j= whose solution turns out to be ρvv T, where v is the top singular vector of the matrix m j= [Tr( ˆX ] j t ) b j A j. This example serves both to illustrate the flexibility of our framework and to highlight the interesting fact that the Arora-Hazan-Kale SDP algorithm is a special case of conditional gradient (to get the original formulation in [], we need to replace the function x with x + log x +, where x + = max(x, 0)). It is worth noting that the analysis in [] appears to be tighter in their special case than the analysis we give here. We plan to investigate this phenomenon in future work. Related work. A generalized Frank-Wolfe algorithm has also been proposed in [5], which considers first-order convex surrogate functions more generally. Algorithm 3 of [5] turns out to be equivalent to a line-search formulation of the algorithm we propose in this paper. However, the perspective is quite different. While both algorithms share the intuition of trying to construct a proximal Frank-Wolfe algorithm, the general framework of [5] studies convex upper bounds, rather than the convex lower bounds of this paper (although for the proximal gradient surrogates that they suggest using, their Frank-Wolfe algorithm does implicitly use lower bounds). Another difference is that their analysis focuses on the Lipschitz properties of the objective function, whereas we use the more general notion of strong convexity with respect to an arbitrary norm. We believe that this geometric perspective is useful for tailoring the proximal function to specific applications, as demonstrated in our discussion of q-herding below. Finally, the saddle point perspective on Frank-Wolfe is not present in [5], although they do discuss the general idea of saddle-point methods in relation to convex surrogates. q-herding. In addition to unifying several existing methods, our framework allows us to extend herding to an algorithm we call q-herding. Herding is an algorithm for constructing pseudosamples that match a specified collection of moments from a distribution; it was originally introduced by Welling [6] and was shown to be a special case of conditional gradient by Bach et al. [3]. Let M X be the probability simplex over X. Herding can be cast as trying to minimize E µ [φ(x)] φ over µ M X, for a given φ : X R d and φ R d. As shown in [3], the herding updates are equivalent to DBMD with h(µ) 0 and R() = T φ +. The implicit assumption in the herding algorithm is that φ(x) is bounded. We are able to construct a more general algorithm that only requires φ(x) p to be bounded for some p. This q-herding algorithm works by taking R() = T φ + q q q, where p + q =. In this case our convergence results imply that E µ [φ(x)] φ p p decays at a rate of O(/T ) (proof to appear in an extended version of this paper). Convergence results. We end by stating our formal convergence results. Proofs are given in the Appendix. For the primal algorithm (PBMD) we have: Theorem. Suppose that h is strongly convex with respect to a norm and let r =. Then, for any u, L(û, ) L(u, ) + r αt+a t A. (9) t+ This is actually a variant of their algorithm, which we present for ease of exposition. t= 3

4 Corollary. Under the hypotheses of Theorem, for α t = we have and for α t = t we have L(û, ) Similarly, for the dual algorithm (DBMD) we have: L(u, ) + r (log(t ) + ). (0) T L(û, ) L(u, ) + 8r T. () Theorem 3. Suppose that R is strongly convex with respect to a norm and let r = u u. Then, for any u, L(û, ) L(u, ) + r Corollary 4. Under the hypotheses of Theorem 3, for α t = we have and for α t = t we have t= L(û, ) L(u, ) + r (log(t ) + ) T αt+a t A. () t+ (3) L(û, ) L(u, ) + 8r T (4) Thus, a step size of α t = t yields the claimed O(/T ) convergence rate. Acknowledgements. Thanks to Cameron Freer, Simon Lacoste-Julien, Martin Jaggi, Percy Liang, Arun Chaganty, and Sida Wang for helpful discussions. Thanks also to the anonymous reviewer for suggesting improvements to the paper. JS was ported by the Hertz Foundation. JHH was ported by the U.S. Government under FA9550--C-008 and awarded by the Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 3 CFR 68a. References [] Sanjeev Arora, Elad Hazan, and Satyen Kale. Fast algorithms for approximate semidefinite programming using the multiplicative weights update method. In FOCS, pages , 005. [] Francis Bach. Duality between subgradient and conditional gradient methods. arxiv.org, November 0. [3] Francis Bach, Simon Lacoste-Julien, and Guillaume Obozinski. On the Equivalence between Herding and Conditional Gradient Algorithms. In ICML. INRIA Paris - Rocquencourt, LIENS, March 0. [4] Martin Jaggi. Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization. In ICML, pages , 03. [5] Julien Mairal. Optimization with First-Order Surrogate Functions. In ICML, 03. [6] Max Welling. Herding dynamical weights to learn. In ICML,

5 Appendix: Convergence Proofs We now prove the convergence results given above. Throughout this section, assume that α,..., α T is a sequence of real numbers and that A t = s= α s. We further require that A t > 0 for all t. Also recall that the Bregman divergence is defined by D f (x x ) def = f(x ) f(x ), x x f(x ). Our proofs hinge on the following key lemma: Lemma 5. Let z,..., z T be vectors and let f(x) be a strictly convex function. Define ẑ t to be t A t s= α sz s. Let x,..., x T be chosen via x t+ = arg min x f(x) + ẑ t, x. Then for any x we have {α t (f(x t ) + z t, x t )} f(x ) + ẑ t, x + A t D f (x t x t+ ). t= Proof. First note that, if x 0 = arg min f(x) + z, x, then f(x 0 ) = z. Now note that so we have α t z t = A t ẑ t A t ẑ t (5) = A t f(x t+ ) + A t f(x t ), (6) {α t (f(x t ) + z t, x t )} (7) t= = = {α t f(x t ) + A t f(x t ) A t f(x t+ ), x t } (8) t= {α t f(x t ) A t f(x t+ ), x t x t+ } (9) t= f(x T + ), x T + (0) = {A t f(x t ) A t f(x t+ ), x t x t+ A t f(x t+ )} () t= + (f(x T + ) f(x T + ), x T + ) = {A t D f (x t x t+ )} () t= + (f(x T + ) f(x T + ), x T + ) = {A t D f (x t x t+ )} + (f(x T + ) + ẑ T, x T + ) (3) t= {A t D f (x t x t+ )} + (f(x ) + ẑ T, x ). (4) t= Dividing both sides by completes the proof. We also note that D f (x t x t+ ) = D f (ẑ t+ z t ), where f (z) = x z, x f(x). This form of the bound will often be more useful to us. Another useful property of Bregman divergences is the following lemma which relates strong convexity of f to strong smoothness of f : Lemma 6. Suppose that D f (x x) x x for some norm and for all x, x. (In this case we say that f is strongly convex with respect to.) Then D f (y y) y y for all y, y. t= 5

6 We require a final porting proposition before proving Theorem. Proposition 7 (Convergence of PBMD). Consider the updates t arg max, u t R() and u t+ arg min u h(u) + ˆ s, u. Then we have L(û T, ) L(u, ) + A t D h (u t u t+ ). (5) Proof. Note that L(u t, t ) = max L(u t, ) by construction. Also note that, if we invoke Lemma 5 with f = h and z t = t then we get the inequality t= α t L(u t, t ) t= t= α t L(u, t ) + Combining these together, we get the string of inequalities ( ) L(û T, ) = L α t u t, as was to be shown. t= α t L(u t, ) t= α t L(u t, t ) t= t= α t L(u, t ) + L(u, ) + Proof of Theorem. By Lemma 6, we have D h (u t u t+ ) = D h (ˆ t+ ˆ t ) A t D h (u t u t+ ). (6) t= A t D h (u t u t+ ) t= A t D h (u t u t+ ), t= ˆ t+ ˆ t = s t α s s s t+ s t α α s s s s t+ α s = α t+ α s s α t+ t+ A t A t+ A t+ s t α t+ α s s + α t+ t+ A t A t+ A t+ r αt+ A. t+ s t It follows that t= A t D h (u t u t+ ) r t= αt+a t A. t+ 6

7 Proof of Corollary. If we let α t =, then A t = t and α t+ At = t A (t+) t+ t. We therefore get r t= α t+a t A t+ r T t r (log(t ) + ). (7) T + t= If we let α t = t, then A t = t(t+) and α t+ At = (t+) t(t+) A (t+) t+ (t+) = t(t+) (t+). We therefore get which completes the proof. r t= α t+a t A t+ 4r T (T + ) t= = 8r T +, (8) The proof of Theorem 3 requires an analagous porting proposition to that of Theorem. Proposition 8 (Convergence of DBMD). Consider the updates u t arg min u h(u) + t, u and t+ arg max, û t R(). Then we have L(û, ) L(u, ) + A t D R ( t t+ ). (9) Proof. If we invoke Lemma 5 with f = R and z t = u t, then for all we get the inequality Re-arranging yields t= t= α t L(u t, t ) α t L(u t, ) + Now, we have the following string of inequalities: ( ) L(û, ) = L α t u t, = t= α t L(u t, ) t= t= t= t= t= t= α t L(u t, ) (30) t= A t D R ( t t+ ). t= α t L(u t, t ) (3) t= A t D R ( t t+ ). (3) t= α t L(u t, t ) + A t D R ( t t+ ) t= α t inf u L(u, t) + α t L(u, t ) + A t D R ( t t+ ) t= A t D R ( t t+ ) t= α t L(u, ) + 7 A t D R ( t t+ ) t=

8 as was to be shown. = L(u, ) + A t D R ( t t+ ), t= Proofs of Theorem 3 and Corollary 4. The proofs are identical to those for PBMD. 8