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An algorithmic framework, based on the difference of convex functions algorithm (DCA), is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence of $\ell_1$…

Information Theory · Computer Science 2016-11-02 Penghang Yin , Jack Xin

We introduce an immersed high-order discontinuous Galerkin method for solving the compressible Navier-Stokes equations on non-boundary-fitted meshes. The flow equations are discretised with a mixed discontinuous Galerkin formulation and are…

Numerical Analysis · Mathematics 2020-01-08 Hong Xiao , Eky Febrianto , Qiaoling Zhang , Fehmi Cirak

The software package BoSSS serves the discretization of (steady-state or time-dependent) partial differential equations with discontinuous coefficients and/or time-dependent domains by means of an eXtended Discontinuous Galerkin (XDG, resp.…

Numerical Analysis · Mathematics 2020-03-06 Florian Kummer , Martin Smuda , Jens Weber

A wide variety of (fixed-point) iterative methods for the solution of nonlinear equations (in Hilbert spaces) exists. In many cases, such schemes can be interpreted as iterative local linearization methods, which, as will be shown, can be…

Numerical Analysis · Mathematics 2019-10-16 Pascal Heid , Thomas P. Wihler

We present a new analytical and numerical framework for solution of Partial Differential Equations (PDEs) that is based on an exact transformation that moves the boundary constraints into the dynamics of the corresponding governing…

Numerical Analysis · Mathematics 2023-02-14 Yulia T. Peet , Matthew M. Peet

We examine nonlinear Kolmogorov partial differential equations (PDEs). Here the nonlinear part of the PDE comes from its Hamiltonian where one maximizes over all possible drift and diffusion coefficients which fall within a…

Numerical Analysis · Mathematics 2026-04-15 Daniel Bartl , Ariel Neufeld , Kyunghyun Park

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

In this manuscript, we analyze the sparse signal recovery (compressive sensing) problem from the perspective of convex optimization by stochastic proximal gradient descent. This view allows us to significantly simplify the recovery analysis…

Data Structures and Algorithms · Computer Science 2013-04-19 Rong Jin , Tianbao Yang , Shenghuo Zhu

Exact recovery of a sparse solution for an underdetermined system of linear equations implies full search among all possible subsets of the dictionary, which is computationally intractable, while l1 minimization will do the job when a…

Information Theory · Computer Science 2014-12-22 Mohsen Joneidi , Mahdi Barzegar Khalilsarai , Alireza Zaeemzadeh , Nazanin Rahnavard

In this paper we present a cell centered Galerkin (CCG) method applied to miscible displacement problems in heterogeneous porous media. The CCG approach combines concepts from finite volume and discontinuous Galerkin (DG) methods to arrive…

Numerical Analysis · Mathematics 2025-09-19 Maurice S. Fabien

Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent method and coordinate descent method are the most popular…

Machine Learning · Computer Science 2023-11-13 Runxue Bao , Bin Gu , Heng Huang

In electronic structure calculations, various material properties can be obtained by means of computing the total energy of a system as well as derivatives of the total energy w.r.t. atomic positions. The derivatives, also known as…

Computational Physics · Physics 2021-01-07 Robert Cimrman , Matyáš Novák , Radek Kolman , Miroslav Tůma , Jiří Vackář

The paper introduces a very simple and fast computation method for high-dimensional integrals to solve high-dimensional Kolmogorov partial differential equations (PDEs). The new machine learning-based method is obtained by solving a…

Numerical Analysis · Mathematics 2021-02-12 Riu Naito , Toshihiro Yamada

This paper develops and analyzes an interior penalty discontinuous Galerkin (IPDG) method using piecewise linear polynomials for the indefinite time harmonic Maxwell equations with the impedance boundary condition in the three dimensional…

Numerical Analysis · Mathematics 2012-12-11 Xiaobing Feng , Haijun Wu

We present and analyze a discontinuous Galerkin method for the numerical modelling of the non-linear fully-coupled thermo-poroelastic problem. For the spatial discretization, we design a high-order discontinuous Galerkin method on polygonal…

Numerical Analysis · Mathematics 2022-05-27 Paola F. Antonietti , Stefano Bonetti , Michele Botti

The advent of multi-material additive manufacturing and automated composite manufacturing has enabled the design of structures featuring complex curvilinear anisotropy. To take advantage of the new design space, efficient computational…

Applied Physics · Physics 2020-10-05 Kenta Suzuki , Sean E. Phenisee , Marco Salviato

Radial basis functions have become a popular tool for approximation and solution of partial differential equations (PDEs). The recently proposed multilevel sparse interpolation with kernels (MuSIK) algorithm proposed in \cite{Georgoulis}…

Numerical Analysis · Mathematics 2017-10-20 Yangzhang Zhao , Qi Zhang , Jeremy Levesley

We study the convergence of iterative linear solvers for discontinuous Galerkin discretizations of systems of hyperbolic conservation laws with polygonal mesh elements compared with that of traditional triangular elements. We solve the…

Numerical Analysis · Mathematics 2019-11-25 Will Pazner , Per-Olof Persson

A scheme for the solution of fluid-structure interaction (FSI) problems with weakly compressible flows is proposed in this work. A novel hybridizable discontinuous Galerkin (HDG) method is derived for the discretization of the fluid…

We consider the problem of maximizing the variance explained from a data matrix using orthogonal sparse principal components that have a support of fixed cardinality. While most existing methods focus on building principal components (PCs)…

Optimization and Control · Mathematics 2022-10-14 Dimitris Bertsimas , Driss Lahlou Kitane