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Isogeometric analysis (IGA) represents a recently developed technology in computational mechanics that offers the possibility of integrating methods for analysis and Computer Aided Design (CAD) into a single, unified process. The…

Numerical Analysis · Computer Science 2016-02-01 Vinh Phu Nguyen , Stéphane P. A. Bordas , Timon Rabczuk

Parallel-in-time methods, such as multigrid reduction-in-time (MGRIT) and Parareal, provide an attractive option for increasing concurrency when simulating time-dependent PDEs in modern high-performance computing environments. While these…

Numerical Analysis · Mathematics 2021-03-04 Hans De Sterck , Robert D. Falgout , Stephanie Friedhoff , Oliver A. Krzysik , Scott P. MacLachlan

We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over…

Numerical Analysis · Mathematics 2023-04-05 Tomas Teijeiro , Jamie M. Taylor , Ali Hashemian , David Pardo

Large-scale numerical simulations often come at the expense of daunting computations. High-Performance Computing has enhanced the process, but adapting legacy codes to leverage parallel GPU computations remains challenging. Meanwhile,…

Efficient numerical solvers for sparse linear systems are crucial in science and engineering. One of the fastest methods for solving large-scale sparse linear systems is algebraic multigrid (AMG). The main challenge in the construction of…

Machine Learning · Computer Science 2020-09-25 Ilay Luz , Meirav Galun , Haggai Maron , Ronen Basri , Irad Yavneh

In this article we present a new multigrid preconditioner for the linear systems arising in the semismooth Newton method solution of certain control-constrained, quadratic distributed optimal control problems. Using a piecewise constant…

Numerical Analysis · Mathematics 2016-02-16 Andrei Draganescu , Jyoti Saraswat

Algebraic multigrid (AMG) is one of the fastest numerical methods for solving large sparse linear systems. For SPD matrices, convergence of AMG is well motivated in the $A$-norm, and AMG has proven to be an effective solver for many…

Numerical Analysis · Mathematics 2019-09-10 Ben S. Southworth , Thomas A. Manteuffel

Algebraic multigrid (AMG) methods derive their optimal efficiency from the interplay between a relaxation process and a corresponding coarse grid correction. In many standard formulations, relaxation and coarse-graining are analyzed and…

Numerical Analysis · Mathematics 2026-03-30 Rayan Moussa , Karsten Kahl

Discretizations of infinite-dimensional variational inequalities lead to linear and nonlinear complementarity problems with many degrees of freedom. To solve these problems in a parallel computing environment, we propose two active-set…

Optimization and Control · Mathematics 2007-05-23 Steven J. Benson , Todd S. Munson

In this work we exploit agglomeration based $h$-multigrid preconditioners to speed-up the iterative solution of discontinuous Galerkin discretizations of the Stokes and Navier-Stokes equations. As a distinctive feature $h$-coarsened mesh…

Computational Physics · Physics 2017-09-13 Lorenzo Botti , Alessandro Colombo , Francesco Bassi

We present a novel augmented Lagrangian (AL) preconditioner for the solution of linear systems arising from finite element discretizations of elliptic interface problems with jump coefficients. The method is based on the Fictitious Domain…

Numerical Analysis · Mathematics 2026-03-16 Michele Benzi , Marco Feder , Luca Heltai , Federica Mugnaioni

Despite hundreds of papers on preconditioned linear systems of equations, there remains a significant lack of comprehensive performance benchmarks comparing various preconditioners for solving symmetric positive definite (SPD) systems. In…

Numerical Analysis · Mathematics 2025-05-28 Marc A. Tunnell , David F. Gleich

In this paper we consider a new version of the dual-primal isogeometric tearing and interconnecting (IETI-DP) method for solving large-scale linear systems of algebraic equations arising from discontinuous Galerkin (dG) isogeometric…

Numerical Analysis · Mathematics 2017-04-05 Christoph Hofer , Ulrich Langer

In this work, we propose a class of novel preconditioned Krylov subspace methods for solving an optimal control problem of parabolic equations. Namely, we develop a family of block $\omega$-circulant based preconditioners for the…

Numerical Analysis · Mathematics 2024-06-04 Po Yin Fung , Sean Hon

In this work we focus on the preconditioning of a Galerkin space-time isogeometric discretization of the heat equation. Exploiting the tensor product structure of the basis functions in the parametric domain, we propose a preconditioner…

Numerical Analysis · Mathematics 2020-11-03 Gabriele Loli , Monica Montardini , Giancarlo Sangalli , Mattia Tani

We introduce a novel quadrature strategy for Isogeometric Analysis (IgA) boundary element discretizations, specifically tailored to collocation methods. Thanks to the dimensionality reduction and the natural handling of unbounded domains,…

Numerical Analysis · Mathematics 2025-11-25 Cesare Bracco , Francesco Patrizi , Alessandra Sestini

The first step towards applying isogeometric analysis techniques to solve PDE problems on a given domain consists in generating an analysis-suitable mapping operator between parametric and physical domains with one or several patches from…

Numerical Analysis · Mathematics 2019-10-29 Jochen Hinz , Matthias Möller , Cornelis Vuik

We construct solvers for an isogeometric multi-patch discretization, where the patches are coupled via a discontinuous Galerkin approach, which allows the consideration of discretizations that do not match on the interfaces. We solve the…

Numerical Analysis · Mathematics 2022-06-20 Monica Montardini , Giancarlo Sangalli , Rainer Schneckenleitner , Stefan Takacs , Mattia Tani

Methods for solving hyperbolic systems typically depend on unknown ordering (e.g., Gauss-Seidel, or sweep/wavefront/marching methods) to achieve good convergence. For many discretisations, mesh types or decompositions these methods do not…

Numerical Analysis · Mathematics 2025-11-19 S. Dargaville , R. P. Smedley-Stevenson , P. N. Smith , C. C. Pain

Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative numerical techniques are becoming popular to scale to larger datasets, relying on the conjugate gradient…

Machine Learning · Computer Science 2022-06-22 Jonathan Wenger , Geoff Pleiss , Philipp Hennig , John P. Cunningham , Jacob R. Gardner
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