Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
type: Item
instance of: scholarly article
title: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
zbMATH Open document ID: 1484.90091
author: Jianyuan Zhai
author: Fani Boukouvala
published in: Journal of Global Optimization
publication date: 2022-01-24T00:00:00Z
Mathematics Subject Classification ID: 90C26
Mathematics Subject Classification ID: 90C57
zbMATH DE Number: 7462075
zbMATH Keywords: black-box optimization
zbMATH Keywords: simulation-optimization
zbMATH Keywords: branch-and-bound
zbMATH Keywords: global optimization
zbMATH Keywords: convex underestimators
describes a project that uses: NumPy
describes a project that uses: ENTMOOT
describes a project that uses: SNOBFIT
describes a project that uses: EGO
describes a project that uses: alphaBB
describes a project that uses: Pyomo
describes a project that uses: Scikit
describes a project that uses: pyDOE
describes a project that uses: MultiMin
describes a project that uses: ANTIGONE
describes a project that uses: MCS
describes a project that uses: ARGONAUT
MaRDI profile type: Publication
full work available at URL: s10898-021-01045-8
OpenAlex ID: W4200503073
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DOI: 10.1007/S10898-021-01045-8
description: scientific article; zbMATH DE number 7462075