252 research outputs found
Multilevel Approach For Signal Restoration Problems With Toeplitz Matrices
We present a multilevel method for discrete ill-posed problems arising from the discretization of Fredholm integral equations of the first kind. In this method, we use the Haar wavelet transform to define restriction and prolongation operators within a multigrid-type iteration. The choice of the Haar wavelet operator has the advantage of preserving matrix structure, such as Toeplitz, between grids, which can be exploited to obtain faster solvers on each level where an edge-preserving Tikhonov regularization is applied. Finally, we present results that indicate the promise of this approach for restoration of signals and images with edges
Accelerating Cosmic Microwave Background map-making procedure through preconditioning
Estimation of the sky signal from sequences of time ordered data is one of
the key steps in Cosmic Microwave Background (CMB) data analysis, commonly
referred to as the map-making problem. Some of the most popular and general
methods proposed for this problem involve solving generalised least squares
(GLS) equations with non-diagonal noise weights given by a block-diagonal
matrix with Toeplitz blocks. In this work we study new map-making solvers
potentially suitable for applications to the largest anticipated data sets.
They are based on iterative conjugate gradient (CG) approaches enhanced with
novel, parallel, two-level preconditioners. We apply the proposed solvers to
examples of simulated non-polarised and polarised CMB observations, and a set
of idealised scanning strategies with sky coverage ranging from nearly a full
sky down to small sky patches. We discuss in detail their implementation for
massively parallel computational platforms and their performance for a broad
range of parameters characterising the simulated data sets. We find that our
best new solver can outperform carefully-optimised standard solvers used today
by a factor of as much as 5 in terms of the convergence rate and a factor of up
to in terms of the time to solution, and to do so without significantly
increasing the memory consumption and the volume of inter-processor
communication. The performance of the new algorithms is also found to be more
stable and robust, and less dependent on specific characteristics of the
analysed data set. We therefore conclude that the proposed approaches are well
suited to address successfully challenges posed by new and forthcoming CMB data
sets.Comment: 19 pages // Final version submitted to A&
Accelerating Cosmic Microwave Background map-making procedure through preconditioning
Estimation of the sky signal from sequences of time ordered data is one of
the key steps in Cosmic Microwave Background (CMB) data analysis, commonly
referred to as the map-making problem. Some of the most popular and general
methods proposed for this problem involve solving generalised least squares
(GLS) equations with non-diagonal noise weights given by a block-diagonal
matrix with Toeplitz blocks. In this work we study new map-making solvers
potentially suitable for applications to the largest anticipated data sets.
They are based on iterative conjugate gradient (CG) approaches enhanced with
novel, parallel, two-level preconditioners. We apply the proposed solvers to
examples of simulated non-polarised and polarised CMB observations, and a set
of idealised scanning strategies with sky coverage ranging from nearly a full
sky down to small sky patches. We discuss in detail their implementation for
massively parallel computational platforms and their performance for a broad
range of parameters characterising the simulated data sets. We find that our
best new solver can outperform carefully-optimised standard solvers used today
by a factor of as much as 5 in terms of the convergence rate and a factor of up
to in terms of the time to solution, and to do so without significantly
increasing the memory consumption and the volume of inter-processor
communication. The performance of the new algorithms is also found to be more
stable and robust, and less dependent on specific characteristics of the
analysed data set. We therefore conclude that the proposed approaches are well
suited to address successfully challenges posed by new and forthcoming CMB data
sets.Comment: 19 pages // Final version submitted to A&
Preconditioned fully implicit PDE solvers for monument conservation
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 of the solutions are known, for a short window of
time, one needs a numerical approximation scheme in order to have a
quantitative forecast at any time of interest. In this paper a fully implicit
numerical method is proposed, analyzed and numerically tested for parabolic
equations of porous media type and on a systems of two PDEs that models the
sulfation of marble in monuments. Due to the nonlinear nature of the underlying
mathematical model, the use of a fixed point scheme is required and every step
implies the solution of large, locally structured, linear systems. A special
effort is devoted to the spectral analysis of the relevant matrices and to the
design of appropriate iterative or multi-iterative solvers, with special
attention to preconditioned Krylov methods and to multigrid procedures.
Numerical experiments for the validation of the analysis complement this
contribution.Comment: 26 pages, 13 figure
An Efficient Iteration Method for Toeplitz-Plus-Band Triangular Systems Generated from Fractional Ordinary Differential Equation
It is time consuming to numerically solve fractional differential equations. The fractional ordinary differential equations may produce Toeplitz-plus-band triangular systems. An efficient iteration method for Toeplitz-plus-band triangular systems is presented with OMlogM computational complexity and OM memory complexity in this paper, compared with the regular solution with OM2 computational complexity and OM2 memory complexity. M is the discrete grid points. Some methods such as matrix splitting, FFT, compress memory storage and adjustable matrix bandwidth are used in the presented solution. The experimental results show that the presented method compares well with the exact solution and is 4.25 times faster than the regular solution
A fast normal splitting preconditioner for attractive coupled nonlinear Schr\"odinger equations with fractional Laplacian
A linearly implicit conservative difference scheme is applied to discretize
the attractive coupled nonlinear Schr\"odinger equations with fractional
Laplacian. Complex symmetric linear systems can be obtained, and the system
matrices are indefinite and Toeplitz-plus-diagonal. Neither efficient
preconditioned iteration method nor fast direct method is available to deal
with these systems. In this paper, we propose a novel matrix splitting
iteration method based on a normal splitting of an equivalent real block form
of the complex linear systems. This new iteration method converges
unconditionally, and the quasi-optimal iteration parameter is deducted. The
corresponding new preconditioner is obtained naturally, which can be
constructed easily and implemented efficiently by fast Fourier transform.
Theoretical analysis indicates that the eigenvalues of the preconditioned
system matrix are tightly clustered. Numerical experiments show that the new
preconditioner can significantly accelerate the convergence rate of the Krylov
subspace iteration methods. Specifically, the convergence behavior of the
related preconditioned GMRES iteration method is spacial mesh-size-independent,
and almost fractional order insensitive. Moreover, the linearly implicit
conservative difference scheme in conjunction with the preconditioned GMRES
iteration method conserves the discrete mass and energy in terms of a given
precision
V-cycle optimal convergence for DCT-III matrices
The paper analyzes a two-grid and a multigrid method for matrices belonging
to the DCT-III algebra and generated by a polynomial symbol. The aim is to
prove that the convergence rate of the considered multigrid method (V-cycle) is
constant independent of the size of the given matrix. Numerical examples from
differential and integral equations are considered to illustrate the claimed
convergence properties.Comment: 19 page
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