Results 31 to 40 of about 84,313 (308)
Two modified hybrid conjugate gradient methods based on a hybrid secant equation
Mathematical Modelling and Analysis, 2013 Taking advantage of the attractive features of Hestenes–Stiefel and Dai–Yuan conjugate gradient methods, we suggest two globally convergent hybridizations of these methods following Andrei's approach of hybridizing the conjugate gradient parameters ...
Saman Babaie-Kafaki, Nezam Mahdavi-Amiri
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SIAM Journal on Scientific Computing, 2000 The author discusses the conjugate gradient method for the numerical solution of a linear large sparse system, using preconditioning technique. To maintain the optimal convergence properties of the method, the author considers a variant of the conjugate gradient method (called flexible conjugate gradient) that performs an explicit orthogonalization of ...
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The rate of convergence of Conjugate Gradients
Numerische Mathematik, 1986 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
SLUIS, A. van der, Vorst, H.A. van der
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Two efficient modifications of AZPRP conjugate gradient method with sufficient descent property
Journal of Inequalities and Applications, 2022 The conjugate gradient method can be applied in many fields, such as neural networks, image restoration, machine learning, deep learning, and many others.
Zabidin Salleh +2 more
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Conjugate gradient Mojette reconstruction [PDF]
SPIE Proceedings, 2005 Iterative methods are now recognized as powerful tools to solve inverse problems such as tomographic reconstruction. In this paper, the main goal is to present a new reconstruction algorithm made from two components. An iterative algorithm, namely the Conjugate Gradient (CG) method, is used to solve the tomographic problem in the least square (LS ...
Servières, Myriam +3 more
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Algorithm for Scaling Variables in Minimization Methods
AlgorithmsEliminating poor scaling of variables of minimized functions is a pressing issue in solving high-dimensional minimization problems where it is impossible to use methods that change the metric of the space with full-scale metric matrices.
Elena Tovbis +2 more
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Two New Conjugate Gradient Methods for Unconstrained Optimization
Complexity, 2020 The conjugate gradient method is very effective in solving large-scale unconstrained optimal problems. In this paper, on the basis of the conjugate parameter of the conjugate descent (CD) method and the second inequality in the strong Wolfe line search ...
Meixing Liu, Guodong Ma, Jianghua Yin
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Nonlinear Conjugate Gradient Methods with Wolfe Type Line Search
Abstract and Applied Analysis, 2013 Nonlinear conjugate gradient method is one of the useful methods for unconstrained optimization problems. In this paper, we consider three kinds of nonlinear conjugate gradient methods with Wolfe type line search for unstrained optimization problems ...
Yuan-Yuan Chen, Shou-Qiang Du
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A New Modified Conjugate Gradient for Nonlinear Minimization Problems
Science Journal of University of Zakho, 2022 The conjugate gradient is a highly effective technique to solve the unconstrained nonlinear minimization problems and it is one of the most well-known methods. It has a lot of applications.
Hussein Ageel Khatab, Salah G. Sharef
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A new modified conjugate gradient method under the strong Wolfe line search for solving unconstrained optimization problems [PDF]
, 2022 Conjugate gradient (CG) method is well-known due to efficiency to solve the problems of unconstrained optimization because of its convergence properties and low computation cost.
June, L.W., Ishak, M. I., Marjugi, S.M.
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