MILP models for the selection of a small set of well-distributed points

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• 作者：Claudia D'Ambrosio^a ; ^{dambrosio@lix.polytechnique.fr} ; Giacomo Nannicini^b ; ^c ; ^{nannicini@sutd.edu.sg} ; Giorgio Sartor^c ; ^{giorgio_sartor@mymail.sutd.edu.sg}
• 关键词：Model fitting ; Mixed-integer programming ; Derivative-free optimization ; Experimental design
• 刊名：Operations Research Letters
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：45
• 期：1
• 页码：46-52
• 全文大小：865 K
• 卷排序：45

文摘

Motivated by the problem of fitting a surrogate model to a set of feasible points in the context of constrained derivative-free optimization, we consider the problem of selecting a small set of points with good space-filling and orthogonality properties from a larger set of feasible points. We propose four mixed-integer linear programming models for this task and we show that the corresponding optimization problems are NP-hard. Numerical experiments show that our models consistently yield well-distributed points that, on average, help reducing the variance of model fitting errors.