Journal Article10.1080/0305215X.2014.895338
Decomposition for large-scale global optimization based on quantified variable correlations uncovered by metamodelling
TL;DR: In this article, a decomposition-optimization strategy is proposed to quantify the variable correlations and a simple optimization scheme is also proposed to systematically solve the decomposed subproblems, instead of solving the original undecomposed problem.
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Abstract: The recently developed Radial Basis Function High-Dimensional Model Representation (RBF-HDMR) yields qualitative information about which variables are correlated. Such qualitative information is only applicable for a few problems that can be completely decomposed. This work develops a strategy to quantify the variable correlations so that decomposition can be fully supported for a wider range of problems. A simple optimization scheme is also proposed to systematically solve the decomposed subproblems, instead of solving the original undecomposed problem. The proposed decomposition–optimization strategy is compared to the direct optimization case without decomposition, for four categories of problems with different decomposability levels. The results show that except for the category of non-decomposable problems in which all variable correlations are strong, the proposed methodology is effective and has similar accuracy to the case of solving the original undecomposed problems, and it finds the optimum wit...
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More test examples for nonlinear programming codes
Klaus Schittkowski
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TL;DR: The purpose of this note is to point out how an interested mathematical programmer could obtain computer programs of more than 120 constrained nonlinear programming problems which have been used in the past to test and compare optimization codes.
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