We propose stochastic variance reduced algorithms for solving convex-concave saddle
point problems, monotone variational inequalities, and monotone inclusions. Our framework …

We present a unified framework based on primal-dual stochastic mirror descent for
approximately solving infinite-horizon Markov decision processes (MDPs) given a …

We give a classical algorithm for linear regression analogous to the quantum matrix
inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters' 09] for low-rank …

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 …

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 give a quantum algorithm for computing an $\epsilon $-approximate Nash equilibrium of
a zero-sum game in a $ m\times n $ payoff matrix with bounded entries. Given a standard …

We show that standard extragradient methods (ie mirror prox and dual extrapolation)
recover optimal accelerated rates for first-order minimization of smooth convex functions. To …

In this paper we study the lower complexity bounds for finite-sum optimization problems,
where the objective is the average of $ n $ individual component functions. We consider a …

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex
losses. In particular, we consider group-structured and bounded $ f $-divergence uncertainty …

Discrepancy theory has provided powerful tools for producing higher-quality objects which
“beat the union bound” in fundamental settings throughout combinatorics and computer …