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An adaptive stochastic global optimization algorithm for one-dimensional functions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4757-4286-2openalex-snapshot
Statistical Decision Theory and Bayesian Analysis
inferredcites →https://doi.org/10.1007/978-94-009-0909-0openalex-snapshot
Bayesian Approach to Global Optimization
inferredcites →https://doi.org/10.1007/978-94-011-3436-1openalex-snapshot
Theory of Global Random Search
inferredcites →https://doi.org/10.1007/bf00119932openalex-snapshot
Stochastic techniques for global optimization: A survey of recent advances
inferredcites →https://doi.org/10.1007/bf00120661openalex-snapshot
Bayesian methods in global optimization
inferredcites →https://doi.org/10.1007/bf00122428openalex-snapshot
Algorithms for multi-extremal mathematical programming problems employing the set of joint space-filling curves
inferredcites →https://doi.org/10.1007/bf01581202openalex-snapshot
Global optimization of univariate Lipschitz functions: I. Survey and properties
inferredcites →https://doi.org/10.1007/bfb0006170openalex-snapshot
The Bayesian approach to global optimization
inferredcites →https://doi.org/10.1016/0377-2217(79)90240-6openalex-snapshot
Towards global optimization 2
inferredcites →https://doi.org/10.1016/0378-4754(92)90017-bopenalex-snapshot
Theory of global random search
inferredcites →https://doi.org/10.1016/s0927-0507(89)01010-8openalex-snapshot
Chapter IX Global optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/0-306-47648-7_4openalex-snapshot
One-dimensional Global Optimization Based on Statistical Models
inferred← citeshttps://doi.org/10.1007/0-306-48332-7_491openalex-snapshot
Stochastic Global Optimization: Stopping Rules
inferred← citeshttps://doi.org/10.1007/978-0-387-36721-7_8openalex-snapshot
Univariate Algorithms for Solving Global Optimization Problems with Multiextremal Non-differentiable Constraints
inferred← citeshttps://doi.org/10.1007/978-0-387-74759-0_647openalex-snapshot
Stochastic Global Optimization: Stopping Rules
inferred← citeshttps://doi.org/10.1007/978-3-642-25566-3_13openalex-snapshot
Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion
inferred← citeshttps://doi.org/10.1007/s10589-005-3906-xopenalex-snapshot
Univariate Global Optimization with Multiextremal Non-Differentiable Constraints Without Penalty Functions
inferred← citeshttps://doi.org/10.1016/j.physa.2005.02.028openalex-snapshot
On stochastic global optimization of one-dimensional functions
inferred← citeshttps://doi.org/10.1016/s0898-1221(02)00206-7openalex-snapshot
Global optimization based on a statistical model and simplicial partitioning
inferred← citeshttps://doi.org/10.1023/a:1008294716304openalex-snapshot
Bayesian Algorithms for One-Dimensional Global Optimization
inferred← citeshttps://doi.org/10.1023/a:1011207622164openalex-snapshot
On Convergence of a P-Algorithm Based on a Statistical Model of Continuously Differentiable Functions
inferred← citeshttps://doi.org/10.1023/a:1012391611462openalex-snapshot
Index branch-and-bound algorithm for Lipschitz univariate global optimization with multiextremal constraints
inferred← citeshttps://doi.org/10.1023/a:1022512411995openalex-snapshot
New Interval Analysis Support Functions Using Gradient Information in a Global Minimization Algorithm
inferred← citeshttps://doi.org/10.1051/ro:2006024openalex-snapshot
Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint
inferred← citeshttps://doi.org/10.1080/02331930008844497openalex-snapshot
A statistical model for global optimization by means of select and clone
inferred← citeshttps://doi.org/10.1145/3582099.3582100openalex-snapshot
The case for fully Bayesian optimisation in small-sample trials
inferred← citeshttps://doi.org/10.25439/rmt.27332895openalex-snapshot
New development of the inclusive-cone-based method for linear optimization
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