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Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization.
- Source :
- Journal of Global Optimization; Jan2022, Vol. 82 Issue 1, p21-50, 30p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09255001
- Volume :
- 82
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Global Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 154568003
- Full Text :
- https://doi.org/10.1007/s10898-021-01045-8