JOURNAL ARTICLE

A modified Polak–Ribière–Polyak conjugate gradient algorithm for unconstrained optimization

Neculai Andrei

Year: 2010 Journal:   Optimization Vol: 60 (12)Pages: 1457-1471   Publisher: Taylor & Francis

Abstract

A modified Polak–Ribière–Polyak conjugate gradient algorithm which satisfies both the sufficient descent condition and the conjugacy condition is presented. These properties are independent of the line search. The algorithms use the standard Wolfe line search. Under standard assumptions, we show the global convergence of the algorithm. Numerical comparisons with conjugate gradient algorithms using a set of 750 unconstrained optimization problems, some of them from the CUTE library, show that this computational scheme outperforms the known Polak–Ribière–Polyak algorithm, as well as some other unconstrained optimization algorithms.

Keywords:
Line search Conjugate gradient method Mathematics Gradient descent Convergence (economics) Nonlinear conjugate gradient method Algorithm Conjugacy class Mathematical optimization Set (abstract data type) Conjugate residual method Gradient method Line (geometry) Computer science Combinatorics Artificial intelligence Artificial neural network

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46
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1.93
FWCI (Field Weighted Citation Impact)
29
Refs
0.83
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Citation History

Topics

Advanced Optimization Algorithms Research
Physical Sciences →  Mathematics →  Numerical Analysis
Optimization and Variational Analysis
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Sparse and Compressive Sensing Techniques
Physical Sciences →  Engineering →  Computational Mechanics
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