A penalized nonparametric method for nonlinear constrained optimization based on noisy data

Dias, Ronaldo; Garcia, Nancy L.; Zambom, Adriano Z.

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A penalized nonparametric method for nonlinear constrained optimization based on noisy data

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Author(s):	Dias, Ronaldo ^[1] ; Garcia, Nancy L. ^[1] ; Zambom, Adriano Z. ^[1] Total Authors: 3
Affiliation:	^[1] Univ Estadual Campinas, UNICAMP, Dept Estatist, BR-13081970 Campinas, SP - Brazil Total Affiliations: 1
Document type:	Journal article
Source:	COMPUTATIONAL OPTIMIZATION AND APPLICATIONS; v. 45, n. 3, p. 521-541, APR 2010.
Web of Science Citations:	4
Abstract
The objective of this study is to find a smooth function joining two points A and B with minimum length constrained to avoid fixed subsets. A penalized nonparametric method of finding the best path is proposed. The method is generalized to the situation where stochastic measurement errors are present. In this case, the proposed estimator is consistent, in the sense that as the number of observations increases the stochastic trajectory converges to the deterministic one. Two applications are immediate, searching the optimal path for an autonomous vehicle while avoiding all fixed obstacles between two points and flight planning to avoid threat or turbulence zones. (AU)

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