On similarities between two models of global optimization: statistical models and radial basis functions

Authors:
Antanas Žilinskas
Affiliations:
Institute of Mathematics and Informatics, Vilnius, Lithuania
Venue:
Journal of Global Optimization
Year:
2010

Citing 8
Cited 2

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Global optimization
A Radial Basis Function Method for Global Optimization

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Radial Basis Functions

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Approximate Implementations of Pure Random Search in the Presence of Noise

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Improved Strategies for Radial basis Function Methods for Global Optimization

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Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)

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An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization

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Laguerre-type exponentials and generalized Appell polynomials

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An adaptive least-squares collocation radial basis function method for the HJB equation

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An adaptive domain decomposition method for the Hamilton---Jacobi---Bellman equation

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Abstract

Construction of global optimization algorithms using statistical models and radial basis function models is discussed. A new method of data smoothing using radial basis function and least squares approach is presented. It is shown that the P-algorithm for global optimization in the presence of noise based on a statistical model coincides with the corresponding radial basis algorithm.