Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

Electric distribution grid operations typically rely on both centralized optimization and local
non-optimal control techniques. As an alternative, distribution system operational practices …

We consider nonconvex-concave minimax problems, $\min_ {\mathbf {x}}\max_ {\mathbf
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …

This paper resolves a longstanding open question pertaining to the design of near-optimal
first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

In this paper we consider solving saddle point problems using two variants of Gradient
Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent …

This paper studies first order methods for solving smooth minimax optimization problems
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …

In this work, we study the computational complexity of reducing the squared gradient
magnitude for smooth minimax optimization problems. First, we present algorithms with …

In this paper we study a first-order primal-dual algorithm for non-smooth convex optimization
problems with known saddle-point structure. We prove convergence to a saddle-point with …

Abstract Generative Adversarial Nets (GANs) are very successful at modeling distributions
from given samples, even in the high-dimensional case. However, their formulation is also …