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Conjugate Gradients for Kernel Machines
CoRR, 2019 Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets require to resort to approximations.
Bartels, S., Hennig, P.
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A NEW THREE -TERM CONJUGATE GRADIENT ALGORITHM FOR SOLVING MINIMIZATION PROBLEMS
Science Journal of University of Zakho, 2023 The method of optimization is used to determine the most precise value for certain functions within a certain domain; it is mostly studied and employed in the fields of mathematics, computer science, and physics.
Dilovan H. Omar +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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Two new spectral conjugate gradient algorithms based on Hestenes–Stiefel
Journal of Algorithms & Computational Technology, 2017 The spectral conjugate gradient algorithm, which is a variant of conjugate gradient method, is one of the effective methods for solving unconstrained optimization problems.
Guofang Wang +4 more
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Iteration-fusing conjugate gradient [PDF]
Proceedings of the International Conference on Supercomputing, 2017 Peer ...
Zhuang, Sicong, Casas, Marc
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Differentiating the Method of Conjugate Gradients [PDF]
SIAM Journal on Matrix Analysis and Applications, 2014 The method of conjugate gradients (CG) is widely used for the iterative solution of large sparse systems of equations $Ax=b$, where $A\in\Re^{n\times n}$ is symmetric positive definite. Let $x_k$ denote the $k$th iterate of CG. This is a nonlinear differentiable function of $b$. In this paper we obtain expressions for $J_k$, the Jacobian matrix of $x_k$
Serge Gratton +3 more
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Reducing Impulse Noise in Images Using an Improved Formula Conjugate Gradient Method
Zanco Journal of Pure and Applied SciencesThe conjugate formula's significance is frequently emphasised by conjugate gradient approaches. In this paper, a novel conjugate coefficient for the conjugate gradient technique is introduced using a quadratic model and conjugacy condition.
Basim A. Hassan, Yousif Ali Mohammed
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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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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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