Decomposition Based Interior Point Methods for Two-Stage Stochastic Convex Quadratic Programs with Recourse

Sanjay Mehrotra () and M. Gokhan Ozevin
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Sanjay Mehrotra: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
M. Gokhan Ozevin: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Operations Research, 2009, vol. 57, issue 4, 964-974

Abstract: 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.

Keywords: two-stage stochastic programming; linear-quadratic programming; Bender's decomposition; large-scale optimization; nondifferentiable convex optimization (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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