Minimization of Unconstrained Nonpolynomial Large-Scale Optimization Problems Using Conjugate Gradient Method Via Exact Line Search
American Journal of Mechanical and Materials Engineering
Volume 1, Issue 1, March 2017, Pages: 10-14
Received: Feb. 28, 2017; Accepted: Mar. 22, 2017; Published: Apr. 7, 2017
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Adam Ajimoti, Department of Mathematics, University of Ilorin, Ilorin, Nigeria
Onah David Ogwumu, Department of Mathematics, Federal University Wukari, Wukari, Nigeria
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The nonlinear conjugate gradient method is an effective iterative method for solving large-scale optimization problems using the iterative scheme x(k+1) = x(k) + αkd(k) where: x(k+1) is the new iterative point, x(k) is the current iterative point, αk is the step-size and d(k) is the descent direction. In this research work, we employed the technique of exact line search to compute the step-size in the iterative scheme mentioned above. The line search technique gave good results when applied to some non-polynomial unconstrained optimization problems.
Iterative Point, Non Polynomial, Unconstrained Optimization, Conjugate Gradient Method, Descent Direction, Exact Line Search, Iterative Scheme
To cite this article
Adam Ajimoti, Onah David Ogwumu, Minimization of Unconstrained Nonpolynomial Large-Scale Optimization Problems Using Conjugate Gradient Method Via Exact Line Search, American Journal of Mechanical and Materials Engineering. Vol. 1, No. 1, 2017, pp. 10-14. doi: 10.11648/j.ajmme.20170101.13
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