|edoc-Server der Humboldt-Universität zu Berlin|
Sanjay Mehrotra, Northwestern University, Evanston|
M. Gokhan Ozevin, Northwestern University, Evanston
|Title:||Convergence Analysis of a Weighted Barrier Decomposition Algorithm for Two Stage Stochastic Programming|
|Date of Acceptance:||05.07.2008|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10090107)|
|Keywords (eng):||two stage stochastic programming, linear-quadratic programming, Bender's decomposition, lagre scale optimization, nondifferentiable convex optimization|
|Appeared in:||Technical Report 07 (2007)|
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|Mehrotra and Ozevin  computationally found that a weighted primal barrier decomposition algorithm signiﬁcantly outperforms the barrier decomposition proposed and analyzed in [11; 6; 8]. This paper provides a theoretical foundation for the weighted barrier decomposition algorithm (WBDA) in . Although the worst case analysis of the WBDA achieves a ﬁrst-stage iteration complexity bound that is worse than the bound shown for the decomposition algorithms of  and [6; 8], under a probabilistic assumption we show that the worst case iteration complexity of WBDA is independent of the number of scenarios in the problem. The probabilistic assumption uses a novel concept of self-concordant random variables.|
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