A new smoothing modified three-term conjugate gradient method for l1 $l_{1}$-norm minimization problem [PDF]
Journal of Inequalities and Applications, 2018 We consider a kind of nonsmooth optimization problems with l1 $l_{1}$-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems.
Shouqiang Du, Miao Chen
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Dai-Kou type conjugate gradient methods with a line search only using gradient [PDF]
Journal of Inequalities and Applications, 2017 In this paper, the Dai-Kou type conjugate gradient methods are developed to solve the optimality condition of an unconstrained optimization, they only utilize gradient information and have broader application scope.
Yuanyuan Huang, Changhe Liu
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A hybrid conjugate gradient algorithm for constrained monotone equations with application in compressive sensing [PDF]
Heliyon, 2020 Combining the projection method of Solodov and Svaiter with the Liu-Storey and Fletcher Reeves conjugate gradient algorithm of Djordjević for unconstrained minimization problems, a hybrid conjugate gradient algorithm is proposed and extended to solve ...
Abdulkarim Hassan Ibrahim +4 more
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Performance Gains in Conjugate Gradient Computation with Linearly Connected GPU Multiprocessors [PDF]
, 2014 Conjugate gradient is an important iterative method used for solving least squares problems. It is compute-bound and generally involves only simple matrix computations.
Lin, Tsung-Han, Tarsa, Stephen, Kung, H.
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A feed forward neural network approach for matrix computations [PDF]
, 2001 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN)
Al-Mudhaf, Ali F
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A Combined Conjugate Gradient Quasi-Newton Method with Modification BFGS Formula
International Journal of Analysis and Applications, 2023 The conjugate gradient and Quasi-Newton methods have advantages and drawbacks, as although quasi-Newton algorithm has more rapid convergence than conjugate gradient, they require more storage compared to conjugate gradient algorithms.
Mardeen Sh. Taher, Salah G. Shareef
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Improvement of conjugate gradient methods for removing impulse noise images [PDF]
, 2023 Optimization problems occur in most disciplines like engineering, physics, mathematics, economics, administration, commerce, social sciences, and even politics.
Ahmed A. Abdullah, Ali +3 more
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Comparison Between Steepest Descent Method and Conjugate Gradient Method by Using Matlab
Journal of Studies in Science and Engineering, 2021 The Steepest descent method and the Conjugate gradient method to minimize nonlinear functions have been studied in this work. Algorithms are presented and implemented in Matlab software for both methods.
Dana Taha Mohammed Salih +1 more
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A New Spectral Conjugate Gradient method for solving unconstrained Optimization problems [PDF]
مجلة ابحاث كلية التربية الاساسية, 2022 The spectral conjugate gradient methods are fascinating, and it has been shown that they are useful for strictly convex quadratic reduction when used properly.
أسامة محمد طاهر ویس +2 more
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EURO Journal on Computational Optimization, 2022 Nonlinear conjugate gradients are among the most popular techniques for solving continuous optimization problems. Although these schemes have long been studied from a global convergence standpoint, their worst-case complexity properties have yet to be ...
Rémi Chan–Renous-Legoubin +1 more
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