632 research outputs found
Preconditioning and convergence in the right norm
The convergence of numerical approximations to the solutions of differential equations is a key aspect of Numerical Analysis and Scientific Computing. Iterative solution methods for the systems of linear(ised) equations which often result are also underpinned by analyses of convergence. In the function space setting, it is widely appreciated that there are appropriate ways in which to assess convergence and it is well-known that different norms are not equivalent. In the finite dimensional linear algebra setting, however, all norms are equivalent and little attention is often payed to the norms used. In this paper, we highlight this consideration in the context of preconditioning for minimum residual methods (MINRES and GMRES/GCR/ORTHOMIN) and argue that even in the linear algebra setting there is a ‘right’ norm in which to consider convergence: stopping an iteration which is rapidly converging in an irrelevant or highly scaled norm at some tolerance level may still give a poor answer
Some Preconditioning Techniques for Saddle Point Problems
Saddle point problems arise frequently in many applications in science and engineering, including constrained optimization, mixed finite element formulations of partial differential equations, circuit analysis, and so forth. Indeed the formulation of most problems with constraints gives rise to saddle point systems. This paper provides a concise overview of iterative approaches for the solution of such systems which are of particular importance in the context of large scale computation. In particular we describe some of the most useful preconditioning techniques for Krylov subspace solvers applied to saddle point problems, including block and constrained preconditioners.\ud
\ud
The work of Michele Benzi was supported in part by the National Science Foundation grant DMS-0511336
On choice of preconditioner for minimum residual methods for nonsymmetric matrices
Existing convergence bounds for Krylov subspace methods such as GMRES for nonsymmetric linear systems give little mathematical guidance for the choice of preconditioner. Here, we establish a desirable mathematical property of a preconditioner which guarantees that convergence of a minimum residual method will essentially depend only on the eigenvalues of the preconditioned system, as is true in the symmetric case. Our theory covers only a subset of nonsymmetric coefficient matrices but computations indicate that it might be more generally applicable
A Bramble-Pasciak conjugate gradient method for discrete Stokes equations with random viscosity
We study the iterative solution of linear systems of equations arising from
stochastic Galerkin finite element discretizations of saddle point problems. We
focus on the Stokes model with random data parametrized by uniformly
distributed random variables and discuss well-posedness of the variational
formulations. We introduce a Bramble-Pasciak conjugate gradient method as a
linear solver. It builds on a non-standard inner product associated with a
block triangular preconditioner. The block triangular structure enables more
sophisticated preconditioners than the block diagonal structure usually applied
in MINRES methods. We show how the existence requirements of a conjugate
gradient method can be met in our setting. We analyze the performance of the
solvers depending on relevant physical and numerical parameters by means of
eigenvalue estimates. For this purpose, we derive bounds for the eigenvalues of
the relevant preconditioned sub-matrices. We illustrate our findings using the
flow in a driven cavity as a numerical test case, where the viscosity is given
by a truncated Karhunen-Lo\`eve expansion of a random field. In this example, a
Bramble-Pasciak conjugate gradient method with block triangular preconditioner
outperforms a MINRES method with block diagonal preconditioner in terms of
iteration numbers.Comment: 19 pages, 1 figure, submitted to SIAM JU
A framework for deflated and augmented Krylov subspace methods
We consider deflation and augmentation techniques for accelerating the
convergence of Krylov subspace methods for the solution of nonsingular linear
algebraic systems. Despite some formal similarity, the two techniques are
conceptually different from preconditioning. Deflation (in the sense the term
is used here) "removes" certain parts from the operator making it singular,
while augmentation adds a subspace to the Krylov subspace (often the one that
is generated by the singular operator); in contrast, preconditioning changes
the spectrum of the operator without making it singular. Deflation and
augmentation have been used in a variety of methods and settings. Typically,
deflation is combined with augmentation to compensate for the singularity of
the operator, but both techniques can be applied separately.
We introduce a framework of Krylov subspace methods that satisfy a Galerkin
condition. It includes the families of orthogonal residual (OR) and minimal
residual (MR) methods. We show that in this framework augmentation can be
achieved either explicitly or, equivalently, implicitly by projecting the
residuals appropriately and correcting the approximate solutions in a final
step. We study conditions for a breakdown of the deflated methods, and we show
several possibilities to avoid such breakdowns for the deflated MINRES method.
Numerical experiments illustrate properties of different variants of deflated
MINRES analyzed in this paper.Comment: 24 pages, 3 figure
A domain decomposing parallel sparse linear system solver
The solution of large sparse linear systems is often the most time-consuming
part of many science and engineering applications. Computational fluid
dynamics, circuit simulation, power network analysis, and material science are
just a few examples of the application areas in which large sparse linear
systems need to be solved effectively. In this paper we introduce a new
parallel hybrid sparse linear system solver for distributed memory
architectures that contains both direct and iterative components. We show that
by using our solver one can alleviate the drawbacks of direct and iterative
solvers, achieving better scalability than with direct solvers and more
robustness than with classical preconditioned iterative solvers. Comparisons to
well-known direct and iterative solvers on a parallel architecture are
provided.Comment: To appear in Journal of Computational and Applied Mathematic
- …