CARTopt: a random search method for nonsmooth unconstrained optimization

B. Robertson, C. Price () and M. Reale

Computational Optimization and Applications, 2013, vol. 56, issue 2, 315 pages

Abstract: A random search algorithm for unconstrained local nonsmooth optimization is described. The algorithm forms a partition on $\mathbb{R}^{n}$ 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. Copyright Springer Science+Business Media New York 2013

Keywords: Nonsmooth optimization; CART; Partitioning random search; Numerical results (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-013-9560-9

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