Results 211 to 220 of about 73,090 (262)
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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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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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Superlinear Convergence of Conjugate Gradients
SIAM Journal on Numerical Analysis, 2001The main goal of this paper is to illustrate that some recent results obtained in the logarithmic potential theory can be used for better understanding the phenomenon in the numerical analysis known as superlinear convergence. The authors give a theoretical explanation for superlinear convergence behaviour observed while solving large systems of linear
Bernhard Beckermann +1 more
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The Kernel Conjugate Gradient Algorithms
IEEE Transactions on Signal Processing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ming Zhang 0010 +3 more
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Pseudoinversus and conjugate gradients
Communications of the ACM, 1975This paper is devoted to the study of connections between pseudoinverses of matrices and conjugate gradients and conjugate direction routines.
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Deconvolution by the conjugate gradient method
ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005Since 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 +3 more
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Orderings for Conjugate Gradient Preconditionings
SIAM Journal on Optimization, 1991The paper treats several ordering principles for the solution of Poisson- type elliptic boundary value problems with preconditioned conjugate gradient methods (PCG). As preconditioners SSOR and incomplete Cholesky (IC) are considered. For the solution of the relevant linear systems, which mostly are tridiagonal, on vector or parallel computers ...
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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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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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A Generalized Conjugate Gradient Algorithm
Journal of Optimization Theory and Applications, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
SanmatĂas, S., Vercher, E.
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On the Conjugate Gradient Matched Filter
IEEE Transactions on Signal Processing, 2012The conjugate gradient (CG) algorithm is an efficient method for the calculation of the weight vector of the matched filter (MF). As an iterative algorithm, it produces a series of approximations to the MF weight vector, each of which can be used to filter the test signal and form a test statistic.
Chaoshu Jiang +2 more
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