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Simon Dirckx

Publications and source records attributed to Simon Dirckx.

5 recordsLinked to original sources

Applications of QR-based Vector-Valued Rational Approximation

Several applications of the QR-AAA algorithm, a greedy scheme for vector-valued rational approximation, are presented. The focus is on demonstrating the flexibility and practical effectiveness of QR-AAA in a variety of computational settings, including Stokes flow computation, multivariate rational approximation, function extension, the development of novel quadrature methods and near-field approximation in the boundary element method.

math.NA

An overlapping domain decomposition method based on solution-transfer operators

An overlapping domain decomposition method is described for variable-coefficient elliptic boundary value problems on domains that can be decomposed into slabs or shells. The method represents the global solution through its traces on internal interfaces, coupled by local Dirichlet solution transfer operators posed on overlapping double slab domains. The key observation is that these interface maps act between separated interfaces, and as such can be written as smooth-kernel integral operators. The resulting global equilibrium system is Fredholm second kind and, unlike non-overlapping formulations, requires no same-interface Dirichlet-to-Neumann or other interface maps with singular kernels. This makes its off-diagonal blocks highly amenable to hierarchical low-rank compression. The formulation admits complementary continuum and discrete interpretations. At fixed slab width, the method is stable under discretization, sufficiently accurate local solves and compression. At the discrete level, the system can be interpreted as a block Jacobi preconditioned Schur complement system. For compatible SPD discretizations satisfying a standard stable-splitting assumption, the symmetrically scaled interface matrix satisfies an energy-norm condition-number bound that depends on the slab width but is uniform with respect to the local resolution. The formulation is implemented using high-order local solvers and hierarchical compression based on randomized sampling. Numerical experiments report iteration counts, accuracy, and compressibility for 2D and 3D elliptic, nonsymmetric, and oscillatory problems with as many as 28 million degrees of freedom.

math.NA

QR-based Parallel Set-Valued Approximation with Rational Functions

In this article a fast and parallelizable algorithm for rational approximation is presented. The method, called (P)QR-AAA, is a (parallel) set-valued variant of the AAA algorithm for scalar functions. It builds on the set-valued AAA framework introduced by Lietaert, Meerbergen, P{\'e}rez and Vandereycken, accelerating it by using an approximate orthogonal basis obtained from a truncated QR decomposition. We demonstrate both theoretically and numerically this method's accuracy and efficiency. We show how it can be parallelized while maintaining the desired accuracy, with minimal communication cost.

math.NA

On the computation of the SVD of Fourier submatrices

Contiguous submatrices of the Fourier matrix are known to be ill-conditioned. In a recent paper in SIAM Review A. Barnett has provided new bounds on the rate of ill-conditioning of the discrete Fourier submatrices. In this paper we focus on the corresponding singular value decomposition. The singular vectors go by the name of periodic discrete prolate spheroidal sequences (P-DPSS). The singular values exhibit an initial plateau, which depends on the dimensions of the submatrix, after which they decay rapidly. The latter regime is known as the plunge region and it is compatible with the submatrices being ill-conditioned. The discrete prolate sequences have received much less study than their continuous counterparts, prolate spheroidal wave functions, associated with continuous Fourier transforms and widely studied following the work of Slepian in the 1970's. In this paper we collect and expand known results on the stable numerical computation of the singular values and vectors of Fourier submatrices. We illustrate the computations and point out a few applications in which Fourier submatrices arise.

math.NA

Frequency extraction for BEM-matrices arising from the 3D scalar Helmholtz equation

The discretisation of boundary integral equations for the scalar Helmholtz equation leads to large dense linear systems. Efficient boundary element methods (BEM), such as the fast multipole method (FMM) and $\Hmat$ based methods, focus on structured low-rank approximations of subblocks in these systems. It is known that the ranks of these subblocks increase linearly with the wavenumber. We explore a data-sparse representation of BEM-matrices valid for a range of frequencies, based on extracting the known phase of the Green's function. Algebraically, this leads to a Hadamard product of a frequency matrix with an $\Hmat$. We show that the frequency dependency of this $\Hmat$ can be determined using a small number of frequency samples, even for geometrically complex three-dimensional scattering obstacles. We describe an efficient construction of the representation by combining adaptive cross approximation with adaptive rational approximation in the continuous frequency dimension. We show that our data-sparse representation allows to efficiently sample the full BEM-matrix at any given frequency, and as such it may be useful as part of an efficient sweeping routine.

math.NA