Nonlinear constrained optimizer and parallel processing for golden block line search

Duc T. Nguyen; Wilson H. Tang; Yeou-Koung Tung; Hakizumwami B. Runesha

Nonlinear constrained optimizer and parallel processing for golden block line search

Duc T. Nguyen, Wilson H. Tang, Yeou-Koung Tung, Hakizumwami B. Runesha

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研究成果: Conference article › 同行評審

2 引文斯高帕斯（Scopus）

摘要

Generalized exponential penalty functions are constructed for the multiplier methods in solving nonlinear programming problems. The non-smooth extreme constraint Gext is replaced by a single smooth constraint Gs by using the generalized exponential function (base a>1). The well-known K.S. function is found to be a special case of our proposed formulation. Parallel processing for Golden block line search algorithm is then summarized, which can also be integrated into our formulation. Both small and large-scale nonlinear programming problems (up to 2000 variables and 2000 nonlinear constraints) have been solved to validate the proposed algorithms.

原文	English
頁（從 - 到）	469-477
頁數	9
期刊	Computer Assisted Mechanics and Engineering Sciences
卷	6
發行號	3
出版狀態	Published - 1 十二月 1999
事件	8th International Conference on Numerical Mathematics and Computational Mechanics (NMCM98) - Miskolc, Hung 持續時間: 24 八月 1998 → 27 八月 1998

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引用此

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AB - Generalized exponential penalty functions are constructed for the multiplier methods in solving nonlinear programming problems. The non-smooth extreme constraint Gext is replaced by a single smooth constraint Gs by using the generalized exponential function (base a>1). The well-known K.S. function is found to be a special case of our proposed formulation. Parallel processing for Golden block line search algorithm is then summarized, which can also be integrated into our formulation. Both small and large-scale nonlinear programming problems (up to 2000 variables and 2000 nonlinear constraints) have been solved to validate the proposed algorithms.

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