An adaptive stochastic global optimization algorithm for one-dimensional functions

DOI10.1007/BF02096402zbMath0841.90114OpenAlexW2058950129MaRDI QIDQ1904720

Marco Locatelli, Fabio Schoen

Publication date: 24 July 1996

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02096402

zbMATH Keywords

global optimizationsequential stopping rulesone-step look-aheadsample path from a Wiener processstopping criteria for Bayesian algorithms

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Related Items (3)

Univariate global optimization with multiextremal non-differentiable constraints without penalty functions ⋮ Global optimization based on a statistical model and simplicial partitioning. ⋮ Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint

Cites Work

• Statistical decision theory and Bayesian analysis. 2nd ed
• Bayesian methods in global optimization
• Global optimization of univariate Lipschitz functions. I: Survey and properties
• Stochastic techniques for global optimization: A survey of recent advances
• Algorithms for multi-extremal mathematical programming problems employing the set of joint space-filling curves
• Global optimization
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