233 research outputs found
Spectral features of matrix-sequences, GLT, symbol, and application in preconditioning Krylov methods, image deblurring, and multigrid algorithms.
The final purpose of any scientific discipline can be regarded as the solution of real-world problems. With this aim, a mathematical modeling of the considered phenomenon is often compulsory. Closed-form solutions of the arising functional equations are usually not available and numerical discretization techniques are required. In this setting, the discretization of an infinite-dimensional linear equation via some linear approximation method, leads to a sequence of linear systems of increasing dimension whose coefficient matrices could inherit a structure from the continuous problem. For instance, the numerical approximation by local methods of constant or nonconstant coefficients systems of Partial Differential Equations (PDEs) over multidimensional domains, gives rise to multilevel block Toeplitz or to Generalized Locally Toeplitz (GLT) sequences, respectively. In the context of structured matrices, the convergence properties of iterative methods, like multigrid or preconditioned Krylov techniques, are strictly related to the notion of symbol, a function whose role relies in describing the asymptotical distribution of the spectrum.
This thesis can be seen as a byproduct of the combined use of powerful tools like symbol, spectral distribution, and GLT, when dealing with the numerical solution of structured linear systems. We approach such an issue both from a theoretical and practical viewpoint. On the one hand, we enlarge some known spectral distribution tools by proving the eigenvalue distribution of matrix-sequences obtained as combination of some algebraic operations on multilevel block Toeplitz matrices. On the other hand, we take advantage of the obtained results for designing efficient preconditioning techniques. Moreover, we focus on the numerical solution of structured linear systems coming from the following applications: image deblurring, fractional diffusion equations, and coupled PDEs. A spectral analysis of the arising structured sequences allows us either to study the convergence and predict the behavior of preconditioned Krylov and multigrid methods applied to the coefficient matrices, or to design effective preconditioners and multigrid solvers for the associated linear systems
Spectral features of matrix-sequences, GLT, symbol, and application in preconditioning Krylov methods, image deblurring, and multigrid algorithms.
The final purpose of any scientific discipline can be regarded as the solution of real-world problems. With this aim, a mathematical modeling of the considered phenomenon is often compulsory. Closed-form solutions of the arising functional equations are usually not available and numerical discretization techniques are required. In this setting, the discretization of an infinite-dimensional linear equation via some linear approximation method, leads to a sequence of linear systems of increasing dimension whose coefficient matrices could inherit a structure from the continuous problem. For instance, the numerical approximation by local methods of constant or nonconstant coefficients systems of Partial Differential Equations (PDEs) over multidimensional domains, gives rise to multilevel block Toeplitz or to Generalized Locally Toeplitz (GLT) sequences, respectively. In the context of structured matrices, the convergence properties of iterative methods, like multigrid or preconditioned Krylov techniques, are strictly related to the notion of symbol, a function whose role relies in describing the asymptotical distribution of the spectrum.
This thesis can be seen as a byproduct of the combined use of powerful tools like symbol, spectral distribution, and GLT, when dealing with the numerical solution of structured linear systems. We approach such an issue both from a theoretical and practical viewpoint. On the one hand, we enlarge some known spectral distribution tools by proving the eigenvalue distribution of matrix-sequences obtained as combination of some algebraic operations on multilevel block Toeplitz matrices. On the other hand, we take advantage of the obtained results for designing efficient preconditioning techniques. Moreover, we focus on the numerical solution of structured linear systems coming from the following applications: image deblurring, fractional diffusion equations, and coupled PDEs. A spectral analysis of the arising structured sequences allows us either to study the convergence and predict the behavior of preconditioned Krylov and multigrid methods applied to the coefficient matrices, or to design effective preconditioners and multigrid solvers for the associated linear systems
FAST SOLUTION METHODS FOR CONVEX QUADRATIC OPTIMIZATION OF FRACTIONAL DIFFERENTIAL EQUATIONS
In this paper, we present numerical methods suitable for solving convex
quadratic Fractional Differential Equation (FDE) constrained optimization
problems, with box constraints on the state and/or control variables. We
develop an Alternating Direction Method of Multipliers (ADMM) framework, which
uses preconditioned Krylov subspace solvers for the resulting sub-problems. The
latter allows us to tackle a range of Partial Differential Equation (PDE)
optimization problems with box constraints, posed on space-time domains, that
were previously out of the reach of state-of-the-art preconditioners. In
particular, by making use of the powerful Generalized Locally Toeplitz (GLT)
sequences theory, we show that any existing GLT structure present in the
problem matrices is preserved by ADMM, and we propose some preconditioning
methodologies that could be used within the solver, to demonstrate the
generality of the approach. Focusing on convex quadratic programs with
time-dependent 2-dimensional FDE constraints, we derive multilevel circulant
preconditioners, which may be embedded within Krylov subspace methods, for
solving the ADMM sub-problems. Discretized versions of FDEs involve large dense
linear systems. In order to overcome this difficulty, we design a recursive
linear algebra, which is based on the Fast Fourier Transform (FFT). We manage
to keep the storage requirements linear, with respect to the grid size ,
while ensuring an order computational complexity per iteration of
the Krylov solver. We implement the proposed method, and demonstrate its
scalability, generality, and efficiency, through a series of experiments over
different setups of the FDE optimization problem
A note on parallel preconditioning for the all-at-once solution of Riesz fractional diffusion equations
The -step backwards difference formula (BDF) for solving the system of
ODEs can result in a kind of all-at-once linear systems, which are solved via
the parallel-in-time preconditioned Krylov subspace solvers (see McDonald,
Pestana, and Wathen [SIAM J. Sci. Comput., 40(2) (2018): A1012-A1033] and Lin
and Ng [arXiv:2002.01108, 17 pages]. However, these studies ignored that the
-step BDF () is not selfstarting, when they are exploited to solve
time-dependent PDEs. In this note, we focus on the 2-step BDF which is often
superior to the trapezoidal rule for solving the Riesz fractional diffusion
equations, but its resultant all-at-once discretized system is a block
triangular Toeplitz system with a low-rank perturbation. Meanwhile, we first
give an estimation of the condition number of the all-at-once systems and then
adapt the previous work to construct two block circulant (BC) preconditioners.
Both the invertibility of these two BC preconditioners and the eigenvalue
distributions of preconditioned matrices are discussed in details. The
efficient implementation of these BC preconditioners is also presented
especially for handling the computation of dense structured Jacobi matrices.
Finally, numerical experiments involving both the one- and two-dimensional
Riesz fractional diffusion equations are reported to support our theoretical
findings.Comment: 18 pages. 2 figures. 6 Table. Tech. Rep.: Institute of Mathematics,
Southwestern University of Finance and Economics. Revised-1: refine/shorten
the contexts and correct some typos; Revised-2: correct some reference
Recommended from our members
EFFICIENT PRECONDITIONING for TIME FRACTIONAL DIFFUSION INVERSE SOURCE PROBLEMS
On the bilateral preconditioning for an L2-type all-at-once system arising from time-space fractional Bloch-Torrey equations
Time-space fractional Bloch-Torrey equations (TSFBTEs) are developed by some
researchers to investigate the relationship between diffusion and
fractional-order dynamics. In this paper, we first propose a second-order
implicit difference scheme for TSFBTEs by employing the recently proposed
L2-type formula [A. A. Alikhanov, C. Huang, Appl. Math. Comput. (2021) 126545].
Then, we prove the stability and the convergence of the proposed scheme. Based
on such a numerical scheme, an L2-type all-at-once system is derived. In order
to solve this system in a parallel-in-time pattern, a bilateral preconditioning
technique is designed to accelerate the convergence of Krylov subspace solvers
according to the special structure of the coefficient matrix of the system. We
theoretically show that the condition number of the preconditioned matrix is
uniformly bounded by a constant for the time fractional order . Numerical results are reported to show the efficiency of our
method.Comment: 24 pages, 6 tables, 4 figure
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