Simple and Optimal Methods for Stochastic Variational Inequalities, I : Opera…

In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE up…

Simple and Optimal Methods for Stochastic Variational Inequalities, II : Mark…

The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evaluation problem in …

Conditional Gradient Methods for Convex Optimization with General Affine and …

Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In …

Efficient Algorithms for Distributionally Robust Stochastic Optimization with…

Recently, there has been a growing interest in distributionally robust optimization (DRO) as a principled approach to data-driven decision making. In this paper, we consider a distributionally robu…

Conditional Gradient Sliding for Convex Optimization

Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimiza…

Algorithms for optimizing the placement of stationary monitors

Otimal Primal-Dual Methods For A Class Of Saddle Point Problems