Bayesian approach to global optimization and application to multiobjective and constrained problems

DOI10.1007/BF00940509zbMath0746.90063MaRDI QIDQ1176841

L. J. Mockus, Jonas Mockus

Publication date: 25 June 1992

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

zbMATH Keywords

global optimizationasymptotic behaviormultiobjective optimizationBayesian approachinteractive proceduredensity of observations

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Management decision making, including multiple objectives (90B50) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

Related Items

Application of Bayesian approach to numerical methods of global and stochastic optimization, Stochastic resource allocation using a predictor-based heuristic for optimization via simulation, Query efficient posterior estimation in scientific experiments via Bayesian active learning

Cites Work

• Optimization by Simulated Annealing
• Hit-and-run algorithms for the identification of nonredundant linear inequalities
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