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Large-Scale Semidefinite Programming via Saddle Point Mirror-Prox Algorithm
Zhaosong Lu (zhaosong Abstract: In this paper, we first develop ``economical'' representations for positive semidefinitness of well-structured sparse symmetric matrix. Using the representations, we then reformulate well-structured large-scale semidefinite problems into smooth convex-concave saddle point problems, which can be solved by a Prox-method with efficiency ${\cal O}(\epsilon^{-1})$ developed in \cite{Nem}. Some numerical implementations for large-scale Lovasz capacity and MAXCUT problems are finally present. Keywords: Semidefinite Programming, Saddle point problem, Mirror-Prox method Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Citation: Technical report, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA, November, 2004. Download: [Postscript][PDF] Entry Submitted: 11/12/2004 Modify/Update this entry | ||
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