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SPEPS Preprint

Author(s): Sanjay Mehrotra, Northwestern University, Evanston
M. Gökhan Özevin, Northwestern University, Evanston
Title: Two-stage stochastic semidefinite programming and decomposition based interior point methods – Theory
Date of Acceptance: 11.03.2005
Submission Date: 05.01.2005
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint: pdf (urn:nbn:de:kobv:11-10057201)
Keywords (eng): Stochastic programming, semidefinite programming, Benders decomposition, interior point methods, primal-dual methods
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Abstract (eng):
We introduce two stage stochastic semidefinite programs with recourse and present a Benders decomposition based linearly convergent interior point algorithm to solve them. This extends the results in Zhao [16] wherein it was shown that the logarithmic barrier associated with the recourse function of two-stage stochastic linear programs with recourse behaves as a strongly self-concordant barrier on the first stage solutions. In this paper we develop the necessary theory. A companion paper [8] addresses implementation issues for the theoretical algorithm of this paper.
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