Empirical analysis of different hybridization strategies for solving systems of nonlinear equations

Author:

Singh Bishwesvar Pratap1,Mäkelä Marko M.1,Nikulin Yury1

Affiliation:

1. University of Turku , Department of Mathematics and Statistics , 20014 Vesilinnantie 5, Turku , Finland

Abstract

Abstract This paper considers various hybrid methods for solving a system of nonlinear equations. Extensive numerical experiments confirm the research hypothesis that one particular form of hybridization of the conjugate gradient algorithm with Newton’s method outperforms other hybrid strategies considered in the paper.

Publisher

Walter de Gruyter GmbH

Reference16 articles.

1. Andrei, N. (2008) An Unconstrained Optimization Test Functions Collection, Advanced Modeling and Optimization, 10, 147–161.

2. Atkinson, K. E. (1978) An Introduction to Numerical Analysis. Nonlinear Systems of Equations. John Wiley & Sons, Canada.

3. Broyden, C. G. (1970) “The convergence of a class of double-rank minimization algorithms”, Journal of the Institute of Mathematics and Its Applications, 6: 76–90, doi:10.1093/imamat/6.1.76

4. Buckley, A. (1978) A combined conjugate-gradient quasi-Newton minimization algorithm. Mathematical Programming, 15, 200–210.

5. Burden, R. L. and Faires, J.D. (2010) Numerical Analysis, 9th Edition. Brooks Cole.

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