A feasible adaptive refinement algorithm for linear semi-infinite optimization

TL;DR

This paper introduces a new adaptive refinement algorithm for linear semi-infinite programming that guarantees feasible iterates and converges to the true solution, with demonstrated effectiveness through numerical experiments.

Contribution

The paper presents a novel adaptive refinement algorithm ensuring feasible iterates and convergence for LSIP, improving upon existing methods.

Findings

Algorithm produces feasible iterates for LSIP

Convergence to the true solution is proven

Numerical experiments validate effectiveness

Abstract

A numerical method is developed to solve linear semi-infinite programming problem (LSIP) in which the iterates produced by the algorithm are feasible for the original problem. This is achieved by constructing a sequence of standard linear programming problems with respect to the successive discretization of the index set such that the approximate regions are included in the original feasible region. The convergence of the approximate solutions to the solution of the original problem is proved and the associated optimal objective function values of the approximate problems are monotonically decreasing and converge to the optimal value of LSIP. An adaptive refinement procedure is designed to discretize the index set and update the constraints for the approximate problem. Numerical experiments demonstrate the performance of the proposed algorithm.