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2013 A Conjugate Gradient Type Method for the Nonnegative Constraints Optimization Problems
Can Li
J. Appl. Math. 2013: 1-6 (2013). DOI: 10.1155/2013/986317

Abstract

We are concerned with the nonnegative constraints optimization problems. It is well known that the conjugate gradient methods are efficient methods for solving large-scale unconstrained optimization problems due to their simplicity and low storage. Combining the modified Polak-Ribière-Polyak method proposed by Zhang, Zhou, and Li with the Zoutendijk feasible direction method, we proposed a conjugate gradient type method for solving the nonnegative constraints optimization problems. If the current iteration is a feasible point, the direction generated by the proposed method is always a feasible descent direction at the current iteration. Under appropriate conditions, we show that the proposed method is globally convergent. We also present some numerical results to show the efficiency of the proposed method.

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Can Li. "A Conjugate Gradient Type Method for the Nonnegative Constraints Optimization Problems." J. Appl. Math. 2013 1 - 6, 2013. https://doi.org/10.1155/2013/986317

Information

Published: 2013
First available in Project Euclid: 14 March 2014

zbMATH: 1266.65101
MathSciNet: MR3045379
Digital Object Identifier: 10.1155/2013/986317

Rights: Copyright © 2013 Hindawi

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