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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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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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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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Complex conjugate gradient methods
Numerical Algorithms, 1993The 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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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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Learning by conjugate gradients
1990A learning algorithm (CG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. CG uses second order information from the neural network but requires only O(N) memory usage, where N is the number of minimization variables; 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.
Sanmatías, S., Vercher, E.
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Conjugate Gradient Bundle Adjustment
2010Bundle adjustment for multi-view reconstruction is traditionally done using the Levenberg-Marquardt algorithm with a direct linear solver, which is computationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop.
Martin Byröd, Kalle Åström
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The constrained conjugate gradient algorithm
IEEE Signal Processing Letters, 2000Based on the condition for equivalence between linearly constrained minimum-variance (LCMV) filters and their generalized sidelobe canceler (GSC) implementations, we derive the new constrained conjugate gradient (CCG) algorithm. We discuss the use of orthogonal and nonorthogonal blocking matrices for the GSC structure and how the choice of this matrix ...
J. A. Apolinário Jr. +2 more
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Duality in conjugate gradient methods
Numerical Algorithms, 1999The 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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