Partitioning inverse Monte Carlo iterative algorithm for finding the three smallest eigenpairs of generalized eigenvalue problem.
Vajargah, Behrouz Fathi, Mehrdoust, Farshid (2011)
Advances in Numerical Analysis
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Displaying similar documents to “Quasi-Monte Carlo Methods for some Linear Algebra Problems. Convergence and Complexity”
Partitioning inverse Monte Carlo iterative algorithm for finding the three smallest eigenpairs of generalized eigenvalue problem.
Inversion of square matrices in processors with limited calculation abillities
Krzysztof Janiszowski (2003)
International Journal of Applied Mathematics and Computer Science
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An iterative inversion algorithm for a class of square matrices is derived and tested. The inverted matrix can be defined over both real and complex fields. This algorithm is based only on the operations of addition and multiplication. The numerics of the algorithm can cope with a short number representation and therefore can be very useful in the case of processors with limited possibilities, like different neuro-computers and accelerator cards. The quality of inversion can be traced...
On the fundamental matrix of finite state Markov chains, its eigensystem and its relation to hitting times.
Takacs, Christiane (2006)
Mathematica Pannonica
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Directed forests with application to algorithms related to Markov chains
Piotr Pokarowski (1999)
Applicationes Mathematicae
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This paper is devoted to computational problems related to Markov chains (MC) on a finite state space. We present formulas and bounds for characteristics of MCs using directed forest expansions given by the Matrix Tree Theorem. These results are applied to analysis of direct methods for solving systems of linear equations, aggregation algorithms for nearly completely decomposable MCs and the Markov chain Monte Carlo procedures.
Aggregation/disaggregation iterative methods applied to Leontev systems and Markov chains
Ivo Marek, Petr Mayer (2002)
Applications of Mathematics
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The paper surveys some recent results on iterative aggregation/disaggregation methods (IAD) for computing stationary probability vectors of stochastic matrices and solutions of Leontev linear systems. A particular attention is paid to fast IAD methods.
Using successive approximations for improving the convergence of GMRES method
Jan Zítko (1998)
Applications of Mathematics
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In this paper, our attention is concentrated on the GMRES method for the solution of the system of linear algebraic equations with a nonsymmetric matrix. We perform pre-iterations before starting GMRES and put for the initial approximation in GMRES. We derive an upper estimate for the norm of the error vector in dependence on the th powers of eigenvalues of the matrix . Further we study under what eigenvalues lay-out this upper estimate is the best one. The estimate shows and...