Bayesian and High-Dimensional Global Optimization

DOI10.1007/978-3-030-64712-4zbMath1473.90134OpenAlexW3134032036MaRDI QIDQ5146731

Antanas Žilinskas, Anatoly A. Zhigljavsky

Publication date: 26 January 2021

Published in: SpringerBriefs in Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-64712-4

zbMATH Keywords

global optimizationstochastic programmingheuristicsapproximation methodsBayesian global optimizationglobal random search

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Research exposition (monographs, survey articles) pertaining to operations research and mathematical programming (90-02)

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Efficient quantisation and weak covering of high dimensional cubes ⋮ A hybrid of Bayesian-based global search with Hooke–Jeeves local refinement for multi-objective optimization problems ⋮ Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms ⋮ Numerical methods using two different approximations of space-filling curves for black-box global optimization ⋮ On asymptotic convergence rate of random search

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