Recent applications that arise in machine learning have surged significant interest in solving
min-max saddle point games. This problem has been extensively studied in the convex …

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 …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Inverse problems with Poisson data arise in many photonic imaging modalities in medicine,
engineering and astronomy. The design of regularization methods and estimators for such …

Learning approximations to smooth target functions of many variables from finite sets of
pointwise samples is an important task in scientific computing and its many applications in …

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 …

This article introduces a new class of fast algorithms to approximate variational problems
involving unbalanced optimal transport. While classical optimal transport considers only …

The paper presents a fully adaptive algorithm for monotone variational inequalities. In each
iteration the method uses two previous iterates for an approximation of the local Lipschitz …

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