An outer-approximation algorithm for maximum-entropy sampling

We apply the well-known outer-approximation algorithm (OA) of convex mixed-integer nonlinear optimization to the maximum-entropy sampling problem (MESP), using convex relaxations for MESP from the literature. We discuss possible methodologies to accelerate the convergence of OA, by combining the use...

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Vydané v:Discrete Applied Mathematics Ročník 347; s. 271 - 284
Hlavní autori: Fampa, Marcia, Lee, Jon
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.04.2024
Predmet:
Disjunctive cuts
Local search
Maximum-entropy sampling
Mixed-integer nonlinear optimization
No-good
Outer approximation
Regularization
Second-order approximation
Local search
No-good
Maximum-entropy sampling
Second-order approximation
Outer approximation
Disjunctive cuts
Regularization
Mixed-integer nonlinear optimization
ISSN:0166-218X, 1872-6771
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Abstract We apply the well-known outer-approximation algorithm (OA) of convex mixed-integer nonlinear optimization to the maximum-entropy sampling problem (MESP), using convex relaxations for MESP from the literature. We discuss possible methodologies to accelerate the convergence of OA, by combining the use of the different relaxations and by selecting additional linearization points using a local-search procedure, disjunctive cuts, a regularization method, and a second-order approximation of the objective of the MESP. We discuss our findings through numerical experiments with a benchmark test problem.
AbstractList We apply the well-known outer-approximation algorithm (OA) of convex mixed-integer nonlinear optimization to the maximum-entropy sampling problem (MESP), using convex relaxations for MESP from the literature. We discuss possible methodologies to accelerate the convergence of OA, by combining the use of the different relaxations and by selecting additional linearization points using a local-search procedure, disjunctive cuts, a regularization method, and a second-order approximation of the objective of the MESP. We discuss our findings through numerical experiments with a benchmark test problem.
Author Lee, Jon
Fampa, Marcia
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Keywords Local search
No-good
Maximum-entropy sampling
Second-order approximation
Outer approximation
Disjunctive cuts
Regularization
Mixed-integer nonlinear optimization
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Snippet We apply the well-known outer-approximation algorithm (OA) of convex mixed-integer nonlinear optimization to the maximum-entropy sampling problem (MESP), using...
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elsevier
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StartPage 271
SubjectTerms Disjunctive cuts
Local search
Maximum-entropy sampling
Mixed-integer nonlinear optimization
No-good
Outer approximation
Regularization
Second-order approximation
Title An outer-approximation algorithm for maximum-entropy sampling
URI https://dx.doi.org/10.1016/j.dam.2024.01.002
Volume 347
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