|edoc-Server der Humboldt-Universität zu Berlin|
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|
Stochastic Programming E-Print Series |
|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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|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  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  addresses implementation issues for the theoretical algorithm of this paper.|
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