other · 2004

Unconstrained Optimization

Cited by 0Abstract and details only

Abstract

A random search algorithm for unconstrained local nonsmooth optimization is described.The algorithm forms a partition on R n using classification and regression trees (CART) from statistical pattern recognition.The CART partition defines desirable subsets where the objective function f is relatively low, based on previous sampling, from which further samples are drawn directly.Alternating between partition and sampling phases provides an effective method for nonsmooth optimization.The sequence of iterates {z k } is shown to converge to an essential local minimizer of f with probability one under mild conditions.Numerical results are presented to show that the method is effective and competitive in practice.

Cite this paper

APA 7

(2004). Unconstrained Optimization. https://doi.org/10.1002/0471650412.ch8

MLA 9

“Unconstrained Optimization.” 2004, pp. 341–68, https://doi.org/10.1002/0471650412.ch8.

Chicago

2004. “Unconstrained Optimization.” https://doi.org/10.1002/0471650412.ch8.

Harvard

(2004) ‘Unconstrained Optimization’. Available at: https://doi.org/10.1002/0471650412.ch8.

BibTeX

@misc{anon2004unconstrained,
  title = {{Unconstrained Optimization}},
  pages = {341--368},
  year = {2004},
  doi = {10.1002/0471650412.ch8}
}

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