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A Derivative-free Two Level Random Search Method for Unconstrained OptimizationIntroduction

A Derivative-free Two Level Random Search Method for Unconstrained Optimization: Introduction [The unconstrained optimization problem is presented: the derivative and the derivative-free methods, as well as the optimality conditions are discussed. A short presentation of the two-level random search method for the unconstrained optimization of functions, for which the derivative information is not known, is given. Some open problems associated to this method are also shown. It is emphasized that for derivative-free methods like the one developed in this book, the only thing we can obtain is a point, where the minimizing function value is smaller or equal to the value in its initial point. Nothing can be said about its optimality, but having in view the scarcity of information on the minimizing function, mainly derivative, the result obtained may be of use for practical considerations.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Derivative-free Two Level Random Search Method for Unconstrained OptimizationIntroduction

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References (64)

Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-68516-4
Pages
1 –17
DOI
10.1007/978-3-030-68517-1_1
Publisher site
See Chapter on Publisher Site

Abstract

[The unconstrained optimization problem is presented: the derivative and the derivative-free methods, as well as the optimality conditions are discussed. A short presentation of the two-level random search method for the unconstrained optimization of functions, for which the derivative information is not known, is given. Some open problems associated to this method are also shown. It is emphasized that for derivative-free methods like the one developed in this book, the only thing we can obtain is a point, where the minimizing function value is smaller or equal to the value in its initial point. Nothing can be said about its optimality, but having in view the scarcity of information on the minimizing function, mainly derivative, the result obtained may be of use for practical considerations.]

Published: Apr 1, 2021

Keywords: Unconstrained optimization; Derivative searching methods; Derivative-free searching methods; Optimality conditions

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