Results 101 to 110 of about 78,319 (219)

Re-Orthogonalized/Affine GMRES and Orthogonalized Maximal Projection Algorithm for Solving Linear Systems

Algorithms
GMRES is one of the most powerful and popular methods to solve linear systems in the Krylov subspace; we examine it from two viewpoints: to maximize the decreasing length of the residual vector, and to maintain the orthogonality of the consecutive ...
Chein-Shan Liu, Chih-Wen Chang , Chung-Lun Kuo    +2 more
doaj   +1 more source

Equivalence between the combined approximations technique and Krylov subspace methods

, 2002
The objective of this Note is to examine the equivalence between the CA technique and Krylov subspace methods. It is shown that the CA technique is a preconditioned Krylov subspace method.
Nair, Prasanth B.
core   +1 more source

Krylov Subspace Descent for Deep Learning

, 2011
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In our method, we construct on each iteration a Krylov subspace formed by the gradient and an approximation to the Hessian matrix, and then use a subset of the training data ...
Oriol Vinyals, Daniel Povey
openaire   +3 more sources

IDR: A new generation of Krylov subspace methods?

, 2012
The Induced Dimension Reduction (IDR) technique developed by Sonneveld and van Gijzen is a powerful concept resulting in a variety of transpose-free Krylov subspace methods based on short-term recurrences.
Zemke, Jens-Peter M., Rendel, Olaf, Rizvanolli, Anisa   +2 more
core   +1 more source

Generalized Preconditioned MHSS Method for a Class of Complex Symmetric Linear Systems

Abstract and Applied Analysis, 2014
Based on the modified Hermitian and skew-Hermitian splitting (MHSS) and preconditioned MHSS (PMHSS) methods, a generalized preconditioned MHSS (GPMHSS) method for a class of complex symmetric linear systems is presented.
Cui-Xia Li, Yan-Jun Liang, Shi-Liang Wu
doaj   +1 more source

Image Deblurring with Krylov Subspace Methods

, 2011
Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution.
Hansen, Per Christian
core  

KryPy v2.1.1: Krylov subspace methods package for Python

, 2014
<p>KryPy is a Python (versions 2 and 3) module for Krylov subspace methods for the solution of linear algebraic systems. This includes enhanced versions of CG, MINRES and GMRES as well as methods for the efficient solution of sequences of linear ...
Gaul, André, Gaul, André (5251747)
core   +1 more source

Krylov Subspace Methods for Large-Scale Matrix Problems in Control

, 2002
This paper presents a brief but state-of-the-art survey of some of existing Krylov subspace methods for large-scale matrix problems in control.
Biswa Nath Datta
core  

Krylov Subspace Methods and Applications to System and Control Problems

, 2007
In this report the Krylov subspace methods are reviewed and some applications in linear system theory and modern control theory are introduced.
Huipin Zhang
core  

SVD-Krylov–Based Structure-Preserving Techniques for Approximation of a Class of Second-Order Index-1 Descriptor Systems

Journal of Applied Mathematics
This work is aimed at deriving a computationally efficient approach to approximate the second-order Index-1 descriptor systems without exploiting the fundamental structure of the systems, which ensures both the accuracy of the approximation and the ...
Mahtab Uddin, M. Monir Uddin, Md. Abdul Hakim Khan   +2 more
doaj   +1 more source