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We introduce a fast direct solver for variable-coefficient elliptic partial differential equations on surfaces based on the hierarchical Poincar\'e-Steklov method. The method takes as input an unstructured, high-order quadrilateral mesh of…

Numerical Analysis · Mathematics 2022-10-04 Daniel Fortunato

In this paper we consider the computation of approximate solutions for inverse problems in Hilbert spaces. In order to capture the special feature of solutions, non-smooth convex functions are introduced as penalty terms. By exploiting the…

Numerical Analysis · Mathematics 2015-06-18 Qinian Jin , Xiliang Lu

We study the approximation of the spectrum of a second-order elliptic differential operator by the Hybrid High-Order (HHO) method. The HHO method is formulated using cell and face unknowns which are polynomials of some degree $k\geq0$. The…

Numerical Analysis · Mathematics 2018-07-23 Victor Calo , Matteo Cicuttin , Quanling Deng , Alexandre Ern

Regular convergence, together with various other types of convergence, has been studied since the 1970s for the discrete approximations of linear operators. In this paper, we consider the eigenvalue approximation of compact operators whose…

Numerical Analysis · Mathematics 2022-10-20 Bo Gong , Jiguang Sun

In this article a unified approach to iterative soft-thresholding algorithms for the solution of linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate the algorithm in the framework of generalized…

Functional Analysis · Mathematics 2010-10-26 Kristian Bredies , Dirk A. Lorenz

We extend the geometrical inverse approximation approach for solving linear least-squares problems. For that we focus on the minimization of $1-\cos(X(A^TA),I)$, where $A$ is a given rectangular coefficient matrix and $X$ is the approximate…

Numerical Analysis · Mathematics 2019-02-25 Jean-Paul Chehab , Marcos Raydan

Planes are generally used in 3D reconstruction for depth sensors, such as RGB-D cameras and LiDARs. This paper focuses on the problem of estimating the optimal planes and sensor poses to minimize the point-to-plane distance. The resulting…

Computer Vision and Pattern Recognition · Computer Science 2022-11-22 Lipu Zhou

In this paper we deal with the connection of frames with the class of Hilbert Schmidt operators. First we give an easy criteria for operators being in this class using frames. It is the equivalent to the criteria using orthonormal bases.…

Functional Analysis · Mathematics 2008-04-09 Peter Balazs

We consider existence and uniqueness of symmetric approximation of frames by normalized tight frames and of symmetric orthogonalization of bases by orthonormal bases in Hilbert spaces H . More precisely, we determine whether a given frame…

Functional Analysis · Mathematics 2025-05-08 M. Frank , V. I. Paulsen , T. R. Tiballi

We consider the problem of minimizing the composition of a nonsmooth function with a smooth mapping in the case where the proximity operator of the nonsmooth function can be explicitly computed. We first show that this proximity operator…

Optimization and Control · Mathematics 2023-08-29 Gilles Bareilles , Franck Iutzeler , Jérôme Malick

Fourier extension is an approximation method that alleviates the periodicity requirements of Fourier series and avoids the Gibbs phenomenon when approximating functions. We describe a similar extension approach using regular wavelet bases…

Numerical Analysis · Mathematics 2020-04-08 Vincent Coppé , Daan Huybrechs

We analyse parallel overlapping Schwarz domain decomposition methods for the Helmholtz equation, where the subdomain problems satisfy first-order absorbing (impedance) transmission conditions, and exchange of information between subdomains…

Numerical Analysis · Mathematics 2022-09-07 Shihua Gong , Martin J. Gander , Ivan G. Graham , David Lafontaine , Euan A. Spence

We consider optimal control problems of elliptic PDEs on hypersurfaces in 2- or 3-dimensional Euclidean space. The leading part of the PDE is given by the Laplace-Beltrami operator, which is discretized by finite elements on a polyhedral…

Optimization and Control · Mathematics 2011-01-10 Michael Hinze , Morten Vierling

We propose a First-Order System Least Squares (FOSLS) method based on deep-learning for numerically solving second-order elliptic PDEs. The method we propose is capable of dealing with either variational and non-variational problems, and…

Numerical Analysis · Mathematics 2022-12-15 Francisco M. Bersetche , Juan Pablo Borthagaray

This work is concerned with approximating multivariate functions in unbounded domain by using discrete least-squares projection with random points evaluations. Particular attention are given to functions with random Gaussian or Gamma…

Numerical Analysis · Mathematics 2014-03-27 Tao Tang , Tao Zhou

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any…

Machine Learning · Statistics 2020-08-13 Barak Sober , Yariv Aizenbud , David Levin

In several geophysical applications, such as full waveform inversion and data modelling, we are facing the solution of inhomogeneous Helmholtz equation. The difficulties of solving the Helmholtz equa- tion are two fold. Firstly, in the case…

Geophysics · Physics 2017-12-27 Nasser Kazemi

The purpose of this work is the study of solution techniques for problems involving fractional powers of symmetric coercive elliptic operators in a bounded domain with Dirichlet boundary conditions. These operators can be realized as the…

Numerical Analysis · Mathematics 2013-02-05 Ricardo H. Nochetto , Enrique Otarola , Abner J. Salgado

This paper proposes a global Pad\'{e} approximation of the generalized Mittag-Leffler function $E_{\alpha,\beta}(-x)$ with $x\in[0,+\infty)$. This uniform approximation can account for both the Taylor series for small arguments and…

Classical Analysis and ODEs · Mathematics 2015-12-08 Caibin Zeng , YangQuan Chen

We investigate numerically efficient approximations of eigenspaces associated to symmetric and general matrices. The eigenspaces are factored into a fixed number of fundamental components that can be efficiently manipulated (we consider…

Machine Learning · Computer Science 2021-09-29 Cristian Rusu , Lorenzo Rosasco
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