Can we accelerate the convergence of gradient descent without changing the algorithm—
just by judiciously choosing stepsizes? Surprisingly, we show that the answer is yes. Our …

In this paper we study the convex-concave saddle-point problem $\min_x\max_y f (x)+
y^\top\mathbf {A} xg (y) $, where $ f (x) $ and $ g (y) $ are smooth and convex functions. We …

We design accelerated algorithms with improved rates for several fundamental classes of
optimization problems. Our algorithms all build upon techniques related to the analysis of …

We study the bilinearly coupled minimax problem: $\min_ {x}\max_ {y} f (x)+ y^\top A xh (y) $,
where $ f $ and $ h $ are both strongly convex smooth functions and admit first-order …

This paper is a survey of methods for solving smooth,(strongly) monotone stochastic
variational inequalities. To begin with, we present the deterministic foundation from which …

We develop an algorithmic framework for solving convex optimization problems using no-
regret game dynamics. By converting the problem of minimizing a convex function into an …

We provide Õ (∊–1)-pass semi-streaming algorithms for computing (1–∊)-approximate
maximum cardinality matchings in bipartite graphs. Our most efficient methods are …

We consider strongly-convex-strongly-concave saddle point problems assuming we have
access to unbiased stochastic estimates of the gradients. We propose a stochastic …

In this paper, we propose a general algorithmic framework for the first-order methods in
optimization in a broad sense, including minimization problems, saddle-point problems and …

Box-simplex games are a family of bilinear minimax objectives which encapsulate graph-
structured problems such as maximum flow [She17], optimal transport [JST19], and bipartite …