|edoc-Server der Humboldt-Universität zu Berlin|
Michael Chen, Northwestern University, Evanston|
Sanjay Mehrotra, Northwestern University, Evanston
|Title:||Self-concordant Tree and Decomposition Based Interior Point Methods for Stochastic Convex Optimization Problem|
|Date of Acceptance:||08.07.2007|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10078826)|
|Submitted in:||Mathematical Programming (A)|
|Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.||Endnote Bibtex|
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|We consider barrier problems associated with two and multistage stochastic convex optimization problems. We show that the barrier recourse functions at any stage form a self- concordant family with respect to the barrier parameter. We also show that the complexity value of the ﬁrst stage problem increases additively with the number of stages and scenarios. We use these results to propose a prototype primal interior point decomposition algorithm for the two-stage and multistage stochastic convex optimization problems admitting self-concordant barriers.|
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