Results 251 to 260 of about 65,642 (305)

Conjugate gradient method

WIREs Computational Statistics, 2001
AbstractThe conjugate gradient (CG) method for optimization and equation solving is described, along with three principal families of algorithms derived from it. In each case, a foundational CG algorithm is formulated mathematically and followed by a brief discussion of refinements and variants within its family.
Saul I. Gass, Carl M. Harris
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Deconvolution by the conjugate gradient method

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Since it is practically difficult to generate and propagate an impulse, often a system is excited by a narrow time domain pulse. The output is recorded and then a numerical deconvolution is often done to extract the impulse response of the object.
Tapan K. Sarkar, Fung I. Tseng, Soheil A. Dianat, Bruce Z. Hollmann   +3 more
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Complex conjugate gradient methods

Numerical Algorithms, 1993
The paper is concerned with the solution of linear systems with non- singular complex matrices. A unified framework is presented from which various conjugate gradient-like methods for solving the above described systems are derived. The considered methods include both well-known methods and some new variants of these methods.
Pascal Joly, Gérard Meurant
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Duality in conjugate gradient methods

Numerical Algorithms, 1999
The authors present the reverse algorithms of \textit{Cs. J. Hegedüs} [Comput. Math. Appl. 21, No. 1, 71-85 (1991; Zbl 0727.65023)] in a new perspective and show how they are related to the more conventional algorithms if the latter is regarded as solving problems involving the original preconditioning matrices.
Broyden C. G., Foschi P.
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The Conjugate Gradient Method

2020
The conjugate gradient method was published by Hestenes and Stiefel in 1952, as a direct method for solving linear systems. Today its main use is as an iterative method for solving large sparse linear systems. On a test problem we show that it performs as well as the SOR method with optimal acceleration parameter, and we do not have to estimate any ...
Tom Lyche, Georg Muntingh, Øyvind Ryan
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On Restart Procedures for the Conjugate Gradient Method

Numerical Algorithms, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Hong Dai, Li-Zhi Liao, Duan Li 0002
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Block-conjugate-gradient method

Physical Review D, 1989
It is shown that by using the block-conjugate-gradient method several, say {ital s}, columns of the inverse Kogut-Susskind fermion matrix can be found simultaneously, in less time than it would take to run the standard conjugate-gradient algorithm {ital s} times. The method improves in efficiency relative to the standard conjugate-gradient algorithm as
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On the truncated conjugate gradient method

Mathematical Programming, 2000
Trust region algorithms for the unconstrained optimization problem \(\min_{x\in\mathbb{R}^n} f(x)\), where the function \(f(x)\) is continuously differentiable, often need to solve the following subproblem \[ \min_{d\in\mathbb{R}^n} g^Td+ d^TBd/2\quad\text{subject to }\|d\|\leq \Delta, \] where \(\Delta> 0\) is a trust region bound, \(g\in \mathbb{R}^n\
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The conjugate gradient method

Numerische Mathematik, 1963
The CG-algorithm is an iterative method to solve linear systems $$Ax + b = 0$$ (1) where A is a symmetric and positive definite coefficient matrix of order n. The method has been described first by Stiefel and Hesteness [1, 2] and additional information is contained in [3] and [4]. The notations used here coincide partially with those used in
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