The CoMirror algorithm with random constraint sampling for convex semi-infinite programming

Bo Wei

doi:10.1007/s10479-020-03766-7

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The CoMirror algorithm with random constraint sampling for convex semi-infinite programming

2020-09-02

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Bo Wei

William B. Haskell *

Sixiang Zhao

DOI：10.1007/s10479-020-03766-7

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Abstract

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The CoMirror algorithm, by Beck et al. (Oper Res Lett 38(6):493-498, 2010), is designed to solve convex optimization problems with one functional constraint. At each iteration, it performs a mirror-descent update using either the subgradient of the objective function or the subgradient of the constraint function, depending on whether or not the constraint violation is below some tolerance. In this paper, we combine the CoMirror algorithm with inexact cut generation to create the SIP-CoM algorithm for solving semi-infinite programming (SIP) problems. First, we provide general error bounds for SIP-CoM. Then, we propose two specific random constraint sampling schemes to approximately solve the cut generation problem for generic SIP. When the objective and constraint functions are generally convex, randomized SIP-CoM achieves an O(1/root N) convergence rate in expectation (in terms of the optimality gap and SIP constraint violation). When the objective and constraint functions are all strongly convex, this rate can be improved to O(1/root N).

Keywords:

Semi-infinite programming

Random constraint sampling

Corporative stochastic approximation

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