Title: Parallel synchronous and asynchronous space-decomposition algorithms for large-scale minimization problems
Authors: Liu, CS
Tseng, CH
機械工程學系
Department of Mechanical Engineering
Keywords: unconstrained minimization;parallel algorithm;parallel training;synchronous algorithm;asynchronous algorithm;decomposition method
Issue Date: 1-Oct-2000
Abstract: Three parallel space-decomposition minimization (PSDM) algorithms, based on the parallel variable transformation (PVT) and the parallel gradient distribution (PGD) algorithms (O.L. Mangasarian, SIMA Journal on Control and Optimization, vol. 33, no. 6, pp. 1916-1925.), are presented for solving convex or nonconvex unconstrained minimization problems. The PSDM algorithms decompose the variable space into subspaces and distribute these decomposed subproblems among parallel processors. It is shown that if all decomposed subproblems are uncoupled of each other, they can be solved independently. Otherwise, the parallel algorithms presented in this paper can be used. Numerical experiments show that these parallel algorithms can save processor time, particularly for medium and large-scale problems. Up to six parallel processors are connected by Ethernet networks to solve four large-scale minimization problems. The results are compared with those obtained by using sequential algorithms run on a single processor. An application of the PSDM algorithms to the training of multilayer Adaptive Linear Neurons (Madaline) and a new parallel architecture for such parallel training are also presented.
URI: http://dx.doi.org/10.1023/A:1008731209637
http://hdl.handle.net/11536/30224
ISSN: 0926-6003
DOI: 10.1023/A:1008731209637
Journal: COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
Volume: 17
Issue: 1
Begin Page: 85
End Page: 107
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