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

DOI10.1016/j.orl.2016.11.004zbMath1409.90113OpenAlexW2560154405MaRDI QIDQ1727947

Giorgio Sartor, Giacomo Nannicini, Claudia D'Ambrosio

Publication date: 21 February 2019

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2016.11.004

zbMATH Keywords

mixed-integer programmingderivative-free optimizationexperimental designmodel fitting

Mathematics Subject Classification ID

Mixed integer programming (90C11) Derivative-free methods and methods using generalized derivatives (90C56) Factorial statistical designs (62K15)

Related Items (2)

RBFOpt: an open-source library for black-box optimization with costly function evaluations ⋮ Derivative-free optimization methods

Uses Software

• MLMSRBF
• EGO

Cites Work

• Exploratory designs for computational experiments
• How to select a small set of diverse solutions to mixed integer programming problems
• An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization
• Multivariate adaptive regression splines
• Efficient global optimization of expensive black-box functions
• Latin hypercube sampling with inequality constraints
• A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• A generalized discrepancy and quadrature error bound
• A Class of Hard Small 0-1 Programs
• Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments
• Generating Multiple Solutions for Mixed Integer Programming Problems
• A radial basis function method for global optimization
• Benchmarking optimization software with performance profiles.

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