標題: Space-decomposition minimization method for large-scale minimization problems
作者: Liu, CS
Tseng, CH
機械工程學系
Department of Mechanical Engineering
關鍵字: unconstrained minimization;decomposition method;direct-search method;large-scale problem
公開日期: 1-四月-1999
摘要: This paper introduces a set of new algorithms, called the Space-Decomposition Minimization (SDM) algorithms, that decomposes the minimization problem into subproblems. If the decomposed-space subproblems are not coupled to each other, they can be solved independently with any convergent algorithm; otherwise, iterative algorithms presented in this paper can be used. Furthermore, if the design space is further decomposed into one-dimensional decomposed spaces, the solution can be found directly using one-dimensional search methods. A hybrid algorithm that yields the benefits of the SDM algorithm and the conjugate gradient method is also given. An example that demonstrates application of SDM algorithm to the learning of a single-layer perceptron neural network is presented, and five large-scale numerical problems are used to test the SDM algorithms. The results obtained are compared with results from the conjugate gradient method. (C) 1999 Elsevier Science Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/S0898-1221(99)00088-7
http://hdl.handle.net/11536/31435
ISSN: 0898-1221
DOI: 10.1016/S0898-1221(99)00088-7
期刊: COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume: 37
Issue: 7
起始頁: 73
結束頁: 88
顯示於類別:期刊論文


文件中的檔案:

  1. 000079482100007.pdf