Title :
Multi-intersected/recursive textured algorithms for large-scale convex optimization problems
Author :
Huang, Garng M. ; Hsieh, Shih-Chieh
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
In this paper, we extend our textured algorithm to multi-intersected textured models. We also describe the recursive textured decomposition process as a tree, in which we can obtain the overall solution by consolidating the end subsystem solutions. The proposed algorithms, their properties, and the theorems for exact convergence are then addressed. The worst-case time complexity of the algorithm with complete tree structure, in which parents in the same recursion level have the same number of children, is analyzed. Examples are given to demonstrate the use of the algorithms and the trade-off among the number of recursion levels, the number of sequential computing steps, and problem size reduction
Keywords :
computational complexity; convergence of numerical methods; nonlinear programming; trees (mathematics); convergence; large-scale convex optimization; multi-intersected textured models; textured decomposition method; tree structure; worst-case time complexity; Algorithm design and analysis; Convergence; Decision feedback equalizers; Ear; Functional programming; Large-scale systems; Linear programming; Time division multiplexing; Tree data structures; Vectors;
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-2685-7
DOI :
10.1109/CDC.1995.480241