- Large-Scale Semidefinite Programming via Saddle Point Mirror-Prox Algorithm Zhaosong Lu (zhaosongisye.gatech.edu) Arkadi Nemirovski (nemirovsie.technion.ac.il) Renato Monteiro (monteiroisye.gatech.edu) 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: 11/12/2004Entry Accepted: 11/12/2004Entry Last Modified: 11/12/2004Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society and by the Optimization Technology Center.