On a new stochastic global optimization algorithm based on censored observations

DOI10.1007/BF01096532zbMath0791.90054OpenAlexW2094720190MaRDI QIDQ1315438

Fabio Schoen

Publication date: 10 March 1994

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01096532

zbMATH Keywords

global optimizationcensored observationssequential stopping rulesnonparametric modelsclustering techniquesmultistart algorithm

Mathematics Subject Classification ID

Nonlinear programming (90C30) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

Uses Software

• tn

Cites Work

• Bayesian methods in global optimization
• Stochastic techniques for global optimization: A survey of recent advances
• Bayesian nonparametric estimation based on censored data
• Optimal and sub-optimal stopping rules for the multistart algorithm in global optimization
• Bayesian testing of nonparametric hypotheses and its application to global optimization
• A wide class of test functions for global optimization
• Bayesian stopping rules for multistart global optimization methods
• Newton-Type Minimization via the Lanczos Method
• Stochastic global optimization methods part I: Clustering methods
• Stochastic global optimization methods part II: Multi level methods
• Sequential stopping rules for the multistart algorithm in global optimisation
• Stopping eules for the multistart method when different local minima have different function values

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