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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Veröffentlicht in:Discrete Applied Mathematics Jg. 347; S. 271 - 284
Hauptverfasser: Fampa, Marcia, Lee, Jon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.04.2024
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
ISSN:0166-218X
1872-6771
DOI:10.1016/j.dam.2024.01.002