Krylov subspace methods
Comparison of Fixed Point Methods and Krylov Subspace Methods Solving Convection Diffusion Equations
... The paper first introduces two-dimensional convection-diffusion equation with boundary value condition, later uses the finite difference method to discretize the equation and analyzes positive definite, diagonally ...
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Krylov subspace methods for time modeling of photonic crystals
... and Krylov simulations of a few different scenarios and for a number of grid ...that Krylov subspace methods are way more efficient than FDTD if a high accuracy is ...integration. ...
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Parallel Implementation of Hybrid Direct-Iterative Algorithm for Multibody Dynamics via Krylov Subspace Methods on IBM 1350 Cluster
... PPCG methods work very well for solution of a large sparse, symmetric, and positive definite system of linear ...other Krylov subspace methods such as Generalized Minimal Residual (GMRES), ...
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Reduced Rank Adaptive Filtering Using Krylov Subspace
... considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear ...
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New variants of the global Krylov type methods for linear systems with multiple right-hand sides arising in elliptic PDEs
... iterative methods, which are the generalizations of the classical Krylov subspace methods [18], have been proposed in recent ...block methods. The first development and analysis of the ...
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Krylov Subspace Solvers and Preconditioners
... This Krylov subspace method can only be used if the coefficient matrix is symmetric and positive ...discuss Krylov subspace methods for an increasing class of ...iterative ...
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Krylov subspace time domain computations of monochromatic sources for multi frequency optical response
... different Krylov subspace methods to efficiently solve Maxwell’s equa- tions in a scattering medium for monochromatic sources to calculate a multi-frequency ...these methods are shown to be ...
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Krylov Subspace Spectral Methods for the Time-Dependent Schr¨odinger Equation with Non-Smooth Potentials
... KSS methods, by employing dif- ferent approximations of the solution operator for each Fourier component of the solution, achieve higher-order accuracy in time than other Krylov subspace ...
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Krylov deferred correction methods for differential equations with algebraic constraints
... the Krylov iterative ...the Krylov subspace methods may be extremely large and hence the resulting numerical approach is ...iterative methods have shown great promise but are still ...
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A Multigrid Block Krylov Subspace Spectral Method for Variable-Coefficient Elliptic PDE
... KSS methods, by employing different approximations of the solution op- erator for each Fourier coefficient of the solution, achieve higher-order accuracy in time than other Krylov subspace ...
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Improved Krylov-FSP Method for Solving the Chemical Master Equation
... The Krylov tolerance is set at Tol := 10 −8 for all three ...the Krylov dimension can vary dynamically from 10 to 100, except for the MAPK example where the maximum dimension is reduced to 30 due to memory ...
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Subspace system identification
... called subspace methods. Subspace methods basically originate in a good combination between system theory, geometry and numerical linear ...that subspace methods calculate a good ...
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Semi-implicit Krylov deferred correction algorithms, applications, and parallelization
... SDC methods are applied to very stiff ODEs, order reduction is observed, and the SDC methods become divergent for many DAE systems independent of the time step-size ...parareal methods to improve the ...
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Orderly Subspace Clustering
... orderly relationship as a novel supervision into subspace clustering for semi-supervised learning. Due to the natural availability of the orderly relationship, OSC is practical for real applications. With the ...
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Krylov subspaces from bilinear representations of nonlinear systems
... Thus, for nonlinear systems, some compromises have to be made if Krylov approaches are to be utilised. For this contribution, the compromise comprises either a restriction on the type of nonlinear system under ...
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Machine Learning Methods Using Class-specific Subspace Kernel Representations for Large-Scale Applications
... Given training data, kernel approximation techniques are typically needed for reduc- ing the computational complexity. This approximation can be interpreted using a subspace model in the RKHS (c.f. Eq. (3.8) and ...
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BLOCK JACOBI-TYPE METHODS FOR LOG-LIKELIHOOD BASED LINEAR INDEPENDENT SUBSPACE ANALYSIS
... MICA methods based on joint block diagonalisation has been developed in [4, 5] by performing a standard Jacobi-type method as in [6] followed by cer- tain permutations on the columns of the demixing matrix, to ...
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Noisy Sparse Subspace Clustering
... 1). Subspace clustering, arguably the most crucial step to understand such data, refers to the task of clustering the data into their original subspaces and uncovering the underlying structure of the ...
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Robust online subspace learning
... the methods presented in chapter 2 require the complete video a ...kernel methods allow for a mapping of features into a high- dimensional feature ...
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Undercomplete Blind Subspace Deconvolution
... kernel-ICA methods of Bach and Jordan (2002) are extended to the ISA ...original methods estimate pair-wise indepen- dence between 1-dimensional random ...
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