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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Parallel conjugate gradient method
Lietuvos Matematikos Rinkinys, 2003 We investigate a parallel version of the preconditioned conjugate gradientmethod. A scalability analysis is done for a finite difference schemewhich approximates the 3D elliptic problem.
Raimondas Čiegis, Galina Šilko
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A New Parameterized Conjugate Gradient Method based on Generalized Perry Conjugate Gradient Method
Tikrit Journal of Pure Science, 2023 A New Parameterized Conjugate Gradient Method based on Generalized Perry Conjugate Gradient Method is proposed to be based on Perry's idea, the descent condition and the global convergent is proven under Wolfe condition.
Khalil K. Abbo, Nazar K. Hussein
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A Three-Term Conjugate Gradient Method with Sufficient Descent Property for Unconstrained Optimization [PDF]
SIAM Journal on Optimization, 2011 Conjugate gradient methods are widely used for solving large-scale unconstrained optimization problems, because they do not need the storage of matrices.
Hager W. W. +4 more
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A spectral conjugate gradient method for solving large-scale unconstrained optimization
Computers and Mathematics With Applications, 2019 This paper establishes a spectral conjugate gradient method for solving unconstrained optimization problems, where the conjugate parameter and the spectral parameter satisfy a restrictive relationship.
Jin-kui Liu, Yuming Feng, Limin Zou
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A Bayesian Conjugate Gradient Method [PDF]
Bayesian Analysis, 2018 A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is employed.
J. Cockayne, C. Oates, M. Girolami
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Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold [PDF]
International Conference on Learning Representations, 2023 The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lower than that of second-order methods.
Jun Chen +6 more
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A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression [PDF]
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 ...
R'emi Chan--Renous-Legoubin, C. Royer
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New modification of the hestenes-stiefel with strong wolfe line search [PDF]
, 2021 . The method of the nonlinear conjugate gradient is widely used in solving large-scale unconstrained optimization since been proven in solving optimization problems without using large memory storage.
Basri, Srimazzura +2 more
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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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