Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5128455 | Operations Research Letters | 2017 | 7 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Discrete Mathematics and Combinatorics
Authors
Claudia D'Ambrosio, Giacomo Nannicini, Giorgio Sartor,