13 research outputs found
Solving rank structured Sylvester and Lyapunov equations
We consider the problem of efficiently solving Sylvester and Lyapunov
equations of medium and large scale, in case of rank-structured data, i.e.,
when the coefficient matrices and the right-hand side have low-rank
off-diagonal blocks. This comprises problems with banded data, recently studied
by Haber and Verhaegen in "Sparse solution of the Lyapunov equation for
large-scale interconnected systems", Automatica, 2016, and by Palitta and
Simoncini in "Numerical methods for large-scale Lyapunov equations with
symmetric banded data", SISC, 2018, which often arise in the discretization of
elliptic PDEs.
We show that, under suitable assumptions, the quasiseparable structure is
guaranteed to be numerically present in the solution, and explicit novel
estimates of the numerical rank of the off-diagonal blocks are provided.
Efficient solution schemes that rely on the technology of hierarchical
matrices are described, and several numerical experiments confirm the
applicability and efficiency of the approaches. We develop a MATLAB toolbox
that allows easy replication of the experiments and a ready-to-use interface
for the solvers. The performances of the different approaches are compared, and
we show that the new methods described are efficient on several classes of
relevant problems
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
Rational Krylov for Stieltjes matrix functions: convergence and pole selection
Evaluating the action of a matrix function on a vector, that is , is an ubiquitous task in applications. When is large, one
usually relies on Krylov projection methods. In this paper, we provide
effective choices for the poles of the rational Krylov method for approximating
when is either Cauchy-Stieltjes or Laplace-Stieltjes (or, which is
equivalent, completely monotonic) and is a positive definite
matrix. Relying on the same tools used to analyze the generic situation, we
then focus on the case , and
obtained vectorizing a low-rank matrix; this finds application, for instance,
in solving fractional diffusion equation on two-dimensional tensor grids. We
see how to leverage tensorized Krylov subspaces to exploit the Kronecker
structure and we introduce an error analysis for the numerical approximation of
. Pole selection strategies with explicit convergence bounds are given also
in this case
Rational Krylov and ADI iteration for infinite size quasi-Toeplitz matrix equations
We consider a class of linear matrix equations involving semi-infinite
matrices which have a quasi-Toeplitz structure. These equations arise in
different settings, mostly connected with PDEs or the study of Markov chains
such as random walks on bidimensional lattices. We present the theory
justifying the existence in an appropriate Banach algebra which is
computationally treatable, and we propose several methods for their solutions.
We show how to adapt the ADI iteration to this particular infinite dimensional
setting, and how to construct rational Krylov methods. Convergence theory is
discussed, and numerical experiments validate the proposed approaches
An Efficient, Memory-Saving Approach for the Loewner Framework
The Loewner framework is one of the most successful data-driven model order reduction techniques. If N is the cardinality of a given data set, the so-called Loewner and shifted Loewner matrices [Formula: see text] and [Formula: see text] can be defined by solely relying on information encoded in the considered data set and they play a crucial role in the computation of the sought rational model approximation.In particular, the singular value decomposition of a linear combination of [Formula: see text] and [Formula: see text] provides the tools needed to construct accurate models which fulfill important approximation properties with respect to the original data set. However, for highly-sampled data sets, the dense nature of [Formula: see text] and [Formula: see text] leads to numerical difficulties, namely the failure to allocate these matrices in certain memory-limited environments or excessive computational costs. Even though they do not possess any sparsity pattern, the Loewner and shifted Loewner matrices are extremely structured and, in this paper, we show how to fully exploit their Cauchy-like structure to reduce the cost of computing accurate rational models while avoiding the explicit allocation of [Formula: see text] and [Formula: see text] . In particular, the use of the hierarchically semiseparable format allows us to remarkably lower both the computational cost and the memory requirements of the Loewner framework obtaining a novel scheme whose costs scale with [Formula: see text]
Structured condition number for multiple right-hand side linear systems with parameterized quasiseparable coefficient matrix
In this paper, we consider the structured perturbation analysis for multiple
right-hand side linear systems with parameterized coefficient matrix.
Especially, we present the explicit expressions for structured condition
numbers for multiple right-hand sides linear systems with {1;1}-quasiseparable
coefficient matrix in the quasiseparable and the Givens-vector representations.
In addition, the comparisons of these two condition numbers between themselves,
and with respect to unstructured condition number are investigated. Moreover,
the effective structured condition number for multiple right-hand sides linear
systems with {1;1}-quasiseparable coefficient matrix is proposed. The
relationships between the effective structured condition number and structured
condition numbers with respect to the quasiseparable and the Givens-vector
representations are also studied. Numerical experiments show that there are
situations in which the effective structured condition number can be much
smaller than the unstructured ones
Hierarchical adaptive low-rank format with applications to discretized partial differential equations
A novel framework for hierarchical low-rank matrices is proposed that combines an adaptive hierarchical partitioning of the matrix with low-rank approximation. One typical application is the approximation of discretized functions on rectangular domains; the flexibility of the format makes it possible to deal with functions that feature singularities in small, localized regions. To deal with time evolution and relocation of singularities, the partitioning can be dynamically adjusted based on features of the underlying data. Our format can be leveraged to efficiently solve linear systems with Kronecker product structure, as they arise from discretized partial differential equations (PDEs). For this purpose, these linear systems are rephrased as linear matrix equations and a recursive solver is derived from low-rank updates of such equations. We demonstrate the effectiveness of our framework for stationary and time-dependent, linear and nonlinear PDEs, including the Burgers' and Allen-Cahn equations
Iterative and doubling algorithms for Riccati-type matrix equations: a comparative introduction
We review a family of algorithms for Lyapunov- and Riccati-type equations
which are all related to each other by the idea of \emph{doubling}: they
construct the iterate of another naturally-arising fixed-point
iteration via a sort of repeated squaring.
The equations we consider are Stein equations , Lyapunov
equations , discrete-time algebraic Riccati equations
, continuous-time algebraic Riccati equations
, palindromic quadratic matrix equations , and
nonlinear matrix equations . We draw comparisons among these
algorithms, highlight the connections between them and to other algorithms such
as subspace iteration, and discuss open issues in their theory.Comment: Review article for GAMM Mitteilunge