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SPEPS Preprint

Author(s): Sanjay Mehrotra, Northwestern University, Evanston
M. Gokhan Ozevin, Northwestern University, Evanston
Title: Decomposition-based interior point methods for two-stage stochastic convex quadratic programs with recourse
Date of Acceptance: 20.04.2004
Submission Date: 21.01.2004
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10059502)
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Abstract (eng):
Zhao [28] recently 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 Benders decomposition based linearly convergent interior point algorithms. An analysis of such an algorithm is given in this paper. [28] G. Zhao: A log-barrier method with Benders decomposition for solving two-stage stochastic linear programs, Mathematical Programming Ser. A 90, (2001) 507-536.
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