Results 21 to 30 of about 84,313 (308)
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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Iteration-fusing conjugate gradient [PDF]
Proceedings of the International Conference on Supercomputing, 2017 Peer ...
Zhuang, Sicong, Casas, Marc
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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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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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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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An imperfect conjugate gradient algorithm [PDF]
, 1982 summary:A new biorthogonalization algorithm is defined which does not depend on the step-size used. The algorithm is suggested so as to minimize the total error after $n$ steps if imperfect steps are used.
Sloboda, Fridrich
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A Multipreconditioned Conjugate Gradient Algorithm [PDF]
SIAM Journal on Matrix Analysis and Applications, 2006 We propose a generalization of the conjugate gradient method that uses multiple preconditioners, combining them automatically in an optimal way. The algorithm may be useful for domain decomposition techniques and other problems in which the need for more than one preconditioner arises naturally.
Robert Bridson, Chen Greif
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Impulse noise removal based on new hybrid conjugate gradient approach [PDF]
, 2016 summary:Image denoising is a fundamental problem in image processing operations. In this paper, we present a two-phase scheme for the impulse noise removal.
Kimiaei, Morteza, Rostami, Majid
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Predict-and-Recompute Conjugate Gradient Variants [PDF]
SIAM Journal on Scientific Computing, 2020 The standard implementation of the conjugate gradient algorithm suffers from communication bottlenecks on parallel architectures, due primarily to the two global reductions required every iteration. In this paper, we study conjugate gradient variants which decrease the runtime per iteration by overlapping global synchronizations, and in the case of ...
Tyler Chen, Erin Carson
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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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