In [18], livieris and pintelas proposed a modified perry’s conjugate gradient method. in order to guarantee that the proposed method generates descent directions, livieris and pintelas [18] exploit the idea of the spectral type modified fletcher–reeves method [9], [10] to the well known perry’s conjugate gradient method [19]. under. A perry type derivative free algorithm for solving systems of nonlinear equations based on the well known bfgs quasi newton method with a modified perry's parameter is proposed and successfully applied to solve signal recovery problem.
Combined with the hyperplane projection method, we extend modified perry’s conjugate gradient method [18] to solve large scale constrained nonlinear equations (1.1). in [18], livieris and pintelas proposed a modified perry’s conjugate gradient method. in order to guarantee that the . global convergence analysis. lemma 3.1. Numerical results show that the new conjugate gradient method is more effective and competitive when compared to other of standard conjugates gradient methods including: cghestenes and stiefel (h s) method, cg perry method cgdai and yuan (d y)method. it is known that the conjugate gradient method is still a popular method for many researchers who are focused in solving the large scale. In this paper, we propose a derivative free method for solving large scale nonlinear monotone equations. it combines the modified perry's conjugate gradient method (i.e. livieris, p. pintelas, globally convergent modified perrys conjugate gradient. At this point, we present our proposed modified perry’s conjugate gradient algorithm (mp cg). algorithm 2.1mp cg. step 1: initiate x 0 ∈ r n and 0 < σ 1 < σ 2 < 1; set k = 0. step 2: if ‖ g k ‖ = 0, then terminate; otherwise go to the next step. step 3: compute the descent direction d k by eq. (2.5). step 4:.
In this paper, we propose a derivative free method for solving large scale nonlinear monotone equations. it combines the modified perry's conjugate gradient method (i.e. livieris, p. pintelas, globally convergent modified perrys conjugate gradient. At this point, we present our proposed modified perry’s conjugate gradient algorithm (mp cg). algorithm 2.1mp cg. step 1: initiate x 0 ∈ r n and 0 < σ 1 < σ 2 < 1; set k = 0. step 2: if ‖ g k ‖ = 0, then terminate; otherwise go to the next step. step 3: compute the descent direction d k by eq. (2.5). step 4:. In this work, we propose a new conjugate gradient method which consists of a modification of perry’s method and ensures sufficient descent independent of the accuracy of the line search. an important property of our proposed method is that it achieves a high order accuracy in approximating the second order curvature information of the objective function by utilizing a new modified secant. This article presents some efficient training algorithms, based on conjugate gradient optimization methods. in addition to the existing conjugate gradient training algorithms, we introduce perry's conjugate gradient method as a training algorithm [a. perry, a modified conjugate gradient algorithm, operations research 26 (1978) 26 43].
In this work, we propose a new conjugate gradient method which consists of a modification of perry’s method and ensures sufficient descent independent of the accuracy of the line search. an important property of our proposed method is that it achieves a high order accuracy in approximating the second order curvature information of the objective function by utilizing a new modified secant. This article presents some efficient training algorithms, based on conjugate gradient optimization methods. in addition to the existing conjugate gradient training algorithms, we introduce perry's conjugate gradient method as a training algorithm [a. perry, a modified conjugate gradient algorithm, operations research 26 (1978) 26 43].
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