Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/103419
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
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/103419
ISSN: 13489151
Appears in Collections:Staff Publications

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