-

 

 

 




Optimization Online





 

Implementation of a block-decomposition algorithm for solving large-scale conic semidefinite programming problems

Renato D. C. Monteiro (monteiro***at***isye.gatech.edu)
Camilo Ortiz (camiort***at***gatech.edu)
Benar B. F. Svaiter (benar***at***impa.br)

Abstract: In this paper, we consider block-decomposition first-order methods for solving large-scale conic semidefinite programming problems. Several ingredients are introduced to speed-up the method in its pure form such as: an aggressive choice of stepsize for performing the extragradient step; use of scaled inner products in the primal and dual spaces; dynamic update of the scaled inner product in the primal space for properly balancing the primal and dual relative residuals; and proper choices of the initial primal and dual iterates, as well as the initial parameter for the primal scaled inner product. Finally, we present computational results showing that our method outperforms the two most competitive codes for large-scale conic semidefinite programs, namely: the boundary point method introduced by Povh et al. and the Newton-CG augmented Lagrangian method by Zhao et al.

Keywords: Complexity, Proximal, Extragradient, Block-decomposition, Convex optimization, Conic optimization, Semidefinite programing

Category 1: Convex and Nonsmooth Optimization

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Linear, Cone and Semidefinite Programming

Citation: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA, May, 2011

Download: [PDF]

Entry Submitted: 05/13/2011
Entry Accepted: 05/13/2011
Entry Last Modified: 09/12/2013

Modify/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
Mathematical Optimization Society