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The Multi‐Point Approximation Method in a Parallel Computing Environment
Author(s) -
Keulen Fred Van,
Toropov Vassili V.
Publication year - 1999
Publication title -
zamm ‐ journal of applied mathematics and mechanics / zeitschrift für angewandte mathematik und mechanik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 51
eISSN - 1521-4001
pISSN - 0044-2267
DOI - 10.1002/zamm.19990791318
Subject(s) - robustness (evolution) , computer science , mathematical optimization , function approximation , point (geometry) , function (biology) , algorithm , mathematics , artificial intelligence , artificial neural network , biochemistry , chemistry , geometry , evolutionary biology , biology , gene
The Multi‐point Approximation Method (MAM) replaces an optimization problem by a sequence of approximate ones. The corresponding approximate response functions are sample and often explicit in terms of the design variables. Each step of the Optimization process involves several implicit function evaluations before identification of the approximation functions by weighted least‐squares fitting takes place. The present paper addresses alternative planning of implicit function evaluations in order to enhance both robustness and efficiency in parallel computer environments, especially heterogeneous networks.