A decomposition-based warm-start method for stochastic programming

Abstract

In this paper we propose a warm-start technique for interior point methods applicable to multi-stage stochastic linear programming problems. The main idea is to generate an initial point by decomposing the problem at the second stage and using an approximate solution of the subproblems as a starting point for the complete instance. We analyse this scheme and produce theoretical conditions under which the warm-start iterate is successful. We describe the implementation within the OOPS solver and the results of the numerical tests we performed