A parallel dual-type algorithm for a class of quadratic programming problems and applications

In this paper, we present a parallel dual-type (PDT) algorithm for solving a strictly convex quadratic programming problem with equality and box constraints. The PDT algorithm is suitable for distributed implementation and can be used as a basic optimization module for handling optimization problems...

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Bibliographic Details
Published in:Expert systems with applications Vol. 36; no. 3; pp. 5190 - 5199
Main Authors: Lin, Shieh-Shing, Lin, Ch’i-Hsin, Horng, Shih-Cheng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2009
Subjects:
Dual method
Optimization
Parallel processing
Power-system state estimation
Quadratic programming problems
Parallel processing
Power-system state estimation
Quadratic programming problems
Optimization
Dual method
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:In this paper, we present a parallel dual-type (PDT) algorithm for solving a strictly convex quadratic programming problem with equality and box constraints. The PDT algorithm is suitable for distributed implementation and can be used as a basic optimization module for handling optimization problems of large distributed systems. Besides, combining the proposed algorithm with a successive quadratic programming (SQP) method, we can solve constrained nonlinear programming problems such as power-system state estimation with power-flow balance constraints on no generation and no-load buses. We have demonstrated the computational efficiency of our method, by comparing with the benchmark commercial NCONF and QPROG routines and the state-of-the-art parallel algorithm through the implementation in the sequential version of Sparc workstation and the parallel version of PC network in solving constrained state estimation problems within IEEE 30-bus and IEEE 118-bus systems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2008.06.023