A derivative-free algorithm for least-squares minimization

Document Type

Article

Publication Date

12-1-2010

Abstract

We develop a framework for a class of derivative-free algorithms for the least-squares minimization problem. These algorithms are designed to take advantage of the problem structure by building polynomial interpolation models for each function in the least-squares minimization. Under suitable conditions, global convergence of the algorithm is established within a trust region framework. Promising numerical results indicate the algorithm is both efficient and robust. Numerical comparisons are made with standard derivative-free software packages that do not exploit the special structure of the least-squares problem or that use finite differences to approximate the gradients. © 2010 Society for Industrial and Applied Mathematics.

Publication Source (Journal or Book title)

SIAM Journal on Optimization

First Page

3555

Last Page

3576

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