minimax problems – Optimization Online

An (s^r)hBcResolution ODE Framework for Understanding Discrete-Time Algorithms and Applications to the Linear Convergence of Minimax Problems

Published: 2021/01/02

There has been a long history of using ordinary differential equations (ODEs) to understand the dynamic of discrete-time algorithms (DTAs). Surprisingly, there are still two fundamental and unanswered questions: (i) it is unclear how to obtain a \emph{suitable} ODE from a given DTA, and (ii) it is unclear the connection between the convergence of a … Read more

Stochastic Approximation approach to Stochastic Programming

Published: 2007/09/26, Updated: 2007/10/03

Convex and Nonsmooth Optimization, Stochastic Programming complexity, minimax problems, mirror descent algorithms, monte carlo sampling, saddle point problem, sample average approximation method, stochastic approximation, stochastic programming

In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic difficulty of solving such stochastic optimization problems is that the involved multidimensional integrals (expectations) cannot be computed with high accuracy. The aim of this paper is to compare two computational approaches based on Monte … Read more