Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization

The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-base...

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Published in:	Journal of global optimization Vol. 82; no. 1; pp. 21 - 50
Main Authors:	Zhai, Jianyuan, Boukouvala, Fani
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2022 Springer Springer Nature B.V
Subjects:	Algorithms Complexity Computer Science Computer simulation Computer-generated environments Decision analysis Decision making First principles Mathematical optimization Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Optimization techniques Real Functions Simulation Spatial data Branch-and-bound Simulation-optimization Convex underestimators Global optimization Black-box optimization
ISSN:	0925-5001, 1573-2916
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Abstract	The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.
AbstractList	The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.
Audience	Academic
Author	Boukouvala, Fani Zhai, Jianyuan
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SubjectTerms	Algorithms Complexity Computer Science Computer simulation Computer-generated environments Decision analysis Decision making First principles Mathematical optimization Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Optimization techniques Real Functions Simulation Spatial data
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Title	Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
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