Title :
Theoretical foundation of a textured decomposition algorithm
Author :
Huang, Garng M. ; Hsieh, Shih-Chieh
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
A textured decomposition method (TDM) is proposed for large-scale convex optimization problems, in which a problem is reduced to a set of more tractable subproblems by rotatingly fixing some complicating (interaction or coupling) variables. The approach is appealing since mutually independent subproblems can be solved in parallel. Accordingly, the TDM selves a large-scale convex optimization problem by iteratively solving a sequence of concurrent subproblems. Necessary and sufficient conditions to guarantee that the converged solution of the TDM be the optimal solution of the original problem are addressed
Keywords :
constraint theory; convergence of numerical methods; iterative methods; nonlinear programming; concurrent subproblems; iterative method; large-scale convex optimization; mutually independent subproblems; necessary condition; nonlinear programming; sufficient condition; textured decomposition; Decision feedback equalizers; Distributed computing; Large-scale systems; Optimization methods; Power system economics; Power system measurements; Sufficient conditions; Time division multiplexing; Transportation; Vectors;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.529771
