127,616 research outputs found
Preconditioning complex symmetric linear systems
A new polynomial preconditioner for symmetric complex linear systems based on
Hermitian and skew-Hermitian splitting (HSS) for complex symmetric linear
systems is herein presented. It applies to Conjugate Orthogonal Conjugate
Gradient (COCG) or Conjugate Orthogonal Conjugate Residual (COCR) iterative
solvers and does not require any estimation of the spectrum of the coefficient
matrix. An upper bound of the condition number of the preconditioned linear
system is provided. Moreover, to reduce the computational cost, an inexact
variant based on incomplete Cholesky decomposition or orthogonal polynomials is
proposed. Numerical results show that the present preconditioner and its
inexact variant are efficient and robust solvers for this class of linear
systems. A stability analysis of the method completes the description of the
preconditioner.Comment: 26 pages, 4 figures, 4 table
Instabilities and Patterns in Coupled Reaction-Diffusion Layers
We study instabilities and pattern formation in reaction-diffusion layers
that are diffusively coupled. For two-layer systems of identical two-component
reactions, we analyze the stability of homogeneous steady states by exploiting
the block symmetric structure of the linear problem. There are eight possible
primary bifurcation scenarios, including a Turing-Turing bifurcation that
involves two disparate length scales whose ratio may be tuned via the
inter-layer coupling. For systems of -component layers and non-identical
layers, the linear problem's block form allows approximate decomposition into
lower-dimensional linear problems if the coupling is sufficiently weak. As an
example, we apply these results to a two-layer Brusselator system. The
competing length scales engineered within the linear problem are readily
apparent in numerical simulations of the full system. Selecting a :1
length scale ratio produces an unusual steady square pattern.Comment: 13 pages, 5 figures, accepted for publication in Phys. Rev.
On the construction of preconditioners by subspace decomposition
AbstractA preconditioner for the iterative solution of symmetric linear systems which arise in Galerkin's method is obtained by decomposition of the space into orthogonal subspaces. The preconditioner corresponds to a particular bilinear form and is actually a 2-level multigrid method. Applications are presented to the numerical solution of elliptic problems by the finite-element method and to the numerical solution of first-kind Fredholm integral equations by Tikhonov regularization
Symbolic preconditioning techniques for linear systems of partial differential equations
Some algorithmic aspects of systems of PDEs based simulations can be better clarified by means of symbolic computation techniques. This is very important since numerical simulations heavily rely on solving systems of PDEs. For the large-scale problems we deal with in today's standard applications, it is necessary to rely on iterative Krylov methods that are scalable (i.e., weakly dependent on the number of degrees on freedom and number of subdomains) and have limited memory requirements. They are preconditioned by domain decomposition methods, incomplete factorizations and multigrid preconditioners. These techniques are well understood and efficient for scalar symmetric equations (e.g., Laplacian, biLaplacian) and to some extent for non-symmetric equations (e.g., convection-diffusion). But they have poor performances and lack robustness when used for symmetric systems of PDEs, and even more so for non-symmetric complex systems (fluid mechanics, porous media ...). As a general rule, the study of iterative solvers for systems of PDEs as opposed to scalar PDEs is an underdeveloped subject. We aim at building new robust and efficient solvers, such as domain decomposition methods and preconditioners for some linear and well-known systems of PDEs
An efficient null space inexact Newton method for hydraulic simulation of water distribution networks
Null space Newton algorithms are efficient in solving the nonlinear equations
arising in hydraulic analysis of water distribution networks. In this article,
we propose and evaluate an inexact Newton method that relies on partial updates
of the network pipes' frictional headloss computations to solve the linear
systems more efficiently and with numerical reliability. The update set
parameters are studied to propose appropriate values. Different null space
basis generation schemes are analysed to choose methods for sparse and
well-conditioned null space bases resulting in a smaller update set. The Newton
steps are computed in the null space by solving sparse, symmetric positive
definite systems with sparse Cholesky factorizations. By using the constant
structure of the null space system matrices, a single symbolic factorization in
the Cholesky decomposition is used multiple times, reducing the computational
cost of linear solves. The algorithms and analyses are validated using medium
to large-scale water network models.Comment: 15 pages, 9 figures, Preprint extension of Abraham and Stoianov, 2015
(https://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001089), September 2015.
Includes extended exposition, additional case studies and new simulations and
analysi
Nonoverlapping domain decomposition preconditioners for discontinuous Galerkin approximations of Hamilton--Jacobi--Bellman equations
We analyse a class of nonoverlapping domain decomposition preconditioners for
nonsymmetric linear systems arising from discontinuous Galerkin finite element
approximation of fully nonlinear Hamilton--Jacobi--Bellman (HJB) partial
differential equations. These nonsymmetric linear systems are uniformly bounded
and coercive with respect to a related symmetric bilinear form, that is
associated to a matrix . In this work, we construct a
nonoverlapping domain decomposition preconditioner , that is based
on , and we then show that the effectiveness of the preconditioner
for solving the} nonsymmetric problems can be studied in terms of the condition
number . In particular, we establish the
bound , where
and are respectively the coarse and fine mesh sizes, and and
are respectively the coarse and fine mesh polynomial degrees. This represents
the first such result for this class of methods that explicitly accounts for
the dependence of the condition number on ; our analysis is founded upon an
original optimal order approximation result between fine and coarse
discontinuous finite element spaces. Numerical experiments demonstrate the
sharpness of this bound. Although the preconditioners are not robust with
respect to the polynomial degree, our bounds quantify the effect of the coarse
and fine space polynomial degrees. Furthermore, we show computationally that
these methods are effective in practical applications to nonsymmetric, fully
nonlinear HJB equations under -refinement for moderate polynomial degrees
Communication-optimal Parallel and Sequential Cholesky Decomposition
Numerical algorithms have two kinds of costs: arithmetic and communication,
by which we mean either moving data between levels of a memory hierarchy (in
the sequential case) or over a network connecting processors (in the parallel
case). Communication costs often dominate arithmetic costs, so it is of
interest to design algorithms minimizing communication. In this paper we first
extend known lower bounds on the communication cost (both for bandwidth and for
latency) of conventional (O(n^3)) matrix multiplication to Cholesky
factorization, which is used for solving dense symmetric positive definite
linear systems. Second, we compare the costs of various Cholesky decomposition
implementations to these lower bounds and identify the algorithms and data
structures that attain them. In the sequential case, we consider both the
two-level and hierarchical memory models. Combined with prior results in [13,
14, 15], this gives a set of communication-optimal algorithms for O(n^3)
implementations of the three basic factorizations of dense linear algebra: LU
with pivoting, QR and Cholesky. But it goes beyond this prior work on
sequential LU by optimizing communication for any number of levels of memory
hierarchy.Comment: 29 pages, 2 tables, 6 figure
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