Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming - Université de Lorraine Access content directly
Journal Articles SIAM Journal on Optimization Year : 2022

Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming

Abstract

The paper deals with stochastic difference-of-convex functions (DC) programs, that is, optimization problems whose the cost function is a sum of a lower semicontinuous DC function and the expectation of a stochastic DC function with respect to a probability distribution. This class of nonsmooth and nonconvex stochastic optimization problems plays a central role in many practical applications. Although there are many contributions in the context of convex and/or smooth stochastic optimization, algorithms dealing with nonconvex and nonsmooth programs remain rare. In deterministic optimization literature, the DC Algorithm (DCA) is recognized to be one of the few algorithms to solve effectively nonconvex and nonsmooth optimization problems. The main purpose of this paper is to present some new stochastic DCAs for solving stochastic DC programs. The convergence analysis of the proposed algorithms is carefully studied, and numerical experiments are conducted to justify the algorithms' behaviors.

Dates and versions

hal-03748667 , version 1 (09-08-2022)

Identifiers

Cite

Hoai An Le Thi, van Ngai Huynh, Tao Pham Dinh, Hoang Phuc Hau Luu. Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming. SIAM Journal on Optimization, 2022, 32 (3), pp.2263-2293. ⟨10.1137/20M1385706⟩. ⟨hal-03748667⟩
362 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More