2,205 research outputs found
Isogeometric preconditioners based on fast solvers for the Sylvester equation
We consider large linear systems arising from the isogeometric discretization
of the Poisson problem on a single-patch domain. The numerical solution of such
systems is considered a challenging task, particularly when the degree of the
splines employed as basis functions is high. We consider a preconditioning
strategy which is based on the solution of a Sylvester-like equation at each
step of an iterative solver. We show that this strategy, which fully exploits
the tensor structure that underlies isogeometric problems, is robust with
respect to both mesh size and spline degree, although it may suffer from the
presence of complicated geometry or coefficients. We consider two popular
solvers for the Sylvester equation, a direct one and an iterative one, and we
discuss in detail their implementation and efficiency for 2D and 3D problems on
single-patch or conforming multi-patch NURBS geometries. Numerical experiments
for problems with different domain geometries are presented, which demonstrate
the potential of this approach
On the ADI method for the Sylvester Equation and the optimal- points
The ADI iteration is closely related to the rational Krylov projection
methods for constructing low rank approximations to the solution of Sylvester
equation. In this paper we show that the ADI and rational Krylov approximations
are in fact equivalent when a special choice of shifts are employed in both
methods. We will call these shifts pseudo H2-optimal shifts. These shifts are
also optimal in the sense that for the Lyapunov equation, they yield a residual
which is orthogonal to the rational Krylov projection subspace. Via several
examples, we show that the pseudo H2-optimal shifts consistently yield nearly
optimal low rank approximations to the solutions of the Lyapunov equations
Low-rank updates and a divide-and-conquer method for linear matrix equations
Linear matrix equations, such as the Sylvester and Lyapunov equations, play
an important role in various applications, including the stability analysis and
dimensionality reduction of linear dynamical control systems and the solution
of partial differential equations. In this work, we present and analyze a new
algorithm, based on tensorized Krylov subspaces, for quickly updating the
solution of such a matrix equation when its coefficients undergo low-rank
changes. We demonstrate how our algorithm can be utilized to accelerate the
Newton method for solving continuous-time algebraic Riccati equations. Our
algorithm also forms the basis of a new divide-and-conquer approach for linear
matrix equations with coefficients that feature hierarchical low-rank
structure, such as HODLR, HSS, and banded matrices. Numerical experiments
demonstrate the advantages of divide-and-conquer over existing approaches, in
terms of computational time and memory consumption
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