Decomposition Based Interior Point Methods for Two-Stage Stochastic Convex Quadratic Programs with Recourse
成果类型：
Article
署名作者：
Mehrotra, Sanjay; Ozevin, M. Gokhan
署名单位：
Northwestern University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.1080.0659
发表日期：
2009
页码：
964-974
关键词：
摘要：
Zhao showed that the log barrier associated with the recourse function of two-stage stochastic linear programs behaves as a strongly self-concordant barrier and forms a self-concordant family on the first-stage solutions. In this paper, we show that the recourse function is also strongly self-concordant and forms a self-concordant family for the two-stage stochastic convex quadratic programs with recourse. This allows us to develop Bender's decomposition based linearly convergent interior point algorithms. An analysis of such an algorithm is given in this paper.