Results 21 to 30 of about 434,100 (202)
AI‐enhanced iterative solvers for accelerating the solution of large‐scale parametrized systems [PDF]
International Journal for Numerical Methods in Engineering, 2022 Recent advances in the field of machine learning open a new era in high performance computing for challenging computational science and engineering applications.
Stefanos Nikolopoulos +3 more
semanticscholar +1 more source
Quantization in Control Systems and Forward Error Analysis of Iterative Numerical Algorithms [PDF]
, 2010 The use of control theory to study iterative algorithms, which can be considered as dynamical systems, opens many opportunities to find new tools for analysis of algorithms.
Constantinides, GA +2 more
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Analysis of Monte Carlo accelerated iterative methods for sparse linear systems
Numerical Linear Algebra with Applications, 2017 We consider hybrid deterministic‐stochastic iterative algorithms for the solution of large, sparse linear systems. Starting from a convergent splitting of the coefficient matrix, we analyze various types of Monte Carlo acceleration schemes applied to the
M. Benzi +4 more
semanticscholar +1 more source
A comparison of numerical splitting-based methods for Markovian dependability and performability models [PDF]
, 1998 Iterative numerical methods are an important ingredient for the solution of continuous time Markov dependability models of fault-tolerant systems. In this paper we make a numerical comparison of several splitting-based iterative methods. We consider the
Carrasco, Juan A., Suñé, Víctor
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Preconditioned fully implicit PDE solvers for monument conservation [PDF]
, 2010 Mathematical models for the description, in a quantitative way, of the damages induced on the monuments by the action of specific pollutants are often systems of nonlinear, possibly degenerate, parabolic equations. Although some the asymptotic properties
Berger A. E. +3 more
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A convergence analysis of SOR iterative methods for linear systems with weak H-matrices
Open Mathematics, 2016 It is well known that SOR iterative methods are convergent for linear systems, whose coefficient matrices are strictly or irreducibly diagonally dominant matrices and strong H-matrices (whose comparison matrices are nonsingular M-matrices).
Zhang Cheng-yi +2 more
doaj +1 more source
Two Iterative Methods for Solving Linear Interval Systems
Applied Computational Intelligence and Soft Computing, 2018 Conjugate gradient is an iterative method that solves a linear system Ax=b, where A is a positive definite matrix. We present this new iterative method for solving linear interval systems Ãx̃=b̃, where à is a diagonally dominant interval matrix, as ...
Esmaeil Siahlooei +1 more
doaj +1 more source
Preconditioning and convergence in the right norm [PDF]
, 2007 The convergence of numerical approximations to the solutions of differential equations is a key aspect of Numerical Analysis and Scientific Computing.
Wathen, A. J.
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Self-stabilizing Numerical Iterative Computation [PDF]
, 2008 Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, {\em etc.}, can be formulated as a problem of solving a linear system
D.P. Bertsekas +6 more
core +3 more sources
, 2020 The following document presents a possible solution and a brief stability analysis for a nonlinear system, which is obtained by studying the possibility of building a hybrid solar receiver; It is necessary to mention that the solution of the ...
Brambila-Paz, F. +4 more
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