September 1, 1995

A Limited Memory Algorithm for Bound Constrained Optimization

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Abstract

An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function. It is shown how to take advantage of the form of the limited memory approximation to implement the algorithm efficiently. The results of numerical tests on a set of large problems are reported.

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Byrd et al. (Fri,) studied this question.

www.synapsesocial.com/papers/69d6b515733a2b54c8aa818a — DOI: https://doi.org/10.1137/0916069

Authors

Richard H. Byrd

Peihuang Lu

Jorge Nocedal

Journals

SIAM Journal on Scientific Computing

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Management Sciences (United States)

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A Limited Memory Algorithm for Bound Constrained Optimization

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Cite this study

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References and Citations

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