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2020 | OriginalPaper | Chapter

Multidimensional Global Search Using Numerical Estimations of Minimized Function Derivatives and Adaptive Nested Optimization Scheme

Authors : Victor Gergel, Alexey Goryachikh

Published in: Numerical Computations: Theory and Algorithms

Publisher: Springer International Publishing

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Abstract

This paper proposes a novel approach to the solution of time-consuming multivariate multiextremal optimization problems. This approach is based on integrating the global search method using derivatives of minimized functions and the nested scheme for dimensionality reduction. In contrast with related works novelty is that derivative values are calculated numerically and the dimensionality reduction scheme is generalized for adaptive use of the search information. The obtained global optimization method demonstrates a good performance, which has been confirmed by numerical experiments.

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previous chapter Generalizing Pure and Impure Iterated Prisoner’s Dilemmas to the Case of Infinite and Infinitesimal Quantities

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Title: Multidimensional Global Search Using Numerical Estimations of Minimized Function Derivatives and Adaptive Nested Optimization Scheme
Authors: Victor Gergel
Alexey Goryachikh
Publisher: Springer International Publishing
Book: Numerical Computations: Theory and Algorithms
Print ISBN: 978-3-030-40615-8

Electronic ISBN: 978-3-030-40616-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-40616-5_33

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