Please use this identifier to cite or link to this item:
Title: Infeasible potential reduction algorithms for semidefinite programming
Authors: Zhao, X.-Y.
Toh, K.-C. 
Keywords: Infeasible potential reduction algorithms
Phase I
Phase II
Semidefinite programming
Issue Date: Oct-2012
Citation: Zhao, X.-Y.,Toh, K.-C. (2012-10). Infeasible potential reduction algorithms for semidefinite programming. Pacific Journal of Optimization 8 (4) : 725-753. ScholarBank@NUS Repository.
Abstract: We design infeasible potential reduction algorithms for primal semidefinite programming (SDP) problems that simultaneously seek feasibility and optimality. The algorithms are based on those in [Anstre- icher, Math. Prog. 52 (1991), pp.429-439] and [Todd, Math. Prog. 59 (1993), pp.133-150] for linear programming. Because a dual algorithm is expected to be computationally advantageous for large sparse problems, we also propose a dual infeasible potential reduction algorithm for dual SDP problems. We analyze the convergence of the algorithms, and implement them to compare their relative performance. © 2012 Yokohama Publishers.
Source Title: Pacific Journal of Optimization
ISSN: 13489151
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Oct 12, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.