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

Volume

3

fYear

1995

fDate

21-23 Jun 1995

Firstpage

1568

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;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, Proceedings of the 1995

Conference_Location

Seattle, WA

Print_ISBN

0-7803-2445-5

Type

conf

DOI

10.1109/ACC.1995.529771

Filename

529771