A new modified scaled conjugate gradient method for large-scale unconstrained optimization with non-convex objective function |
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Authors: | Zahra Khoshgam |
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Affiliation: | Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, Semnan, Iran |
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Abstract: | In this paper, according to the fifth-order Taylor expansion of the objective function and the modified secant equation suggested by Li and Fukushima, a new modified secant equation is presented. Also, a new modification of the scaled memoryless BFGS preconditioned conjugate gradient algorithm is suggested which is the idea to compute the scaling parameter based on a two-point approximation of our new modified secant equation. A remarkable feature of the proposed method is that it possesses a globally convergent even without convexity assumption on the objective function. Numerical results show that the proposed new modification of scaled conjugate gradient is efficient. |
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Keywords: | secant equation conjugate gradient method memoryless BFGS method large scale unconstrained optimization problems |
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