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This paper presents an efficient spectral method for solving the fractional Fredholm integro-differential equations. The non-smoothness of the solutions to such problems leads to the performance of spectral methods based on the classical…

Numerical Analysis · Mathematics 2022-09-23 Y. Talaei , S. Noeiaghdam , H. Hosseinzadeh

This paper is devoted to studying the global and finite convergence of the semi-smooth Newton method for solving a piecewise linear system that arises in cone-constrained quadratic programming problems and absolute value equations. We first…

Optimization and Control · Mathematics 2023-01-24 Nicolas F. Armijo , Yunier Bello-Cruz , Gabriel Haeser

In this work, an efficient approximation scheme has been proposed for getting accurate approximate solution of nonlinear partial differential equations with constant or variable coefficients satisfying initial conditions in a series of…

Analysis of PDEs · Mathematics 2020-09-04 Prakash Kumar Das , M. M. Panja

In this paper, we propose a new method that combines the inexact Newton method with a procedure to obtain a feasible inexact projection for solving constrained smooth and nonsmooth equations. The local convergence theorems are established…

Optimization and Control · Mathematics 2019-03-19 Fabiana R. de Oliveira , Orizon P. Ferreira

Incremental methods are widely utilized for solving finite-sum optimization problems in machine learning and signal processing. In this paper, we study a family of incremental methods -- including incremental subgradient, incremental…

Optimization and Control · Mathematics 2022-12-26 Xiao Li , Zhihui Zhu , Anthony Man-Cho So , Jason D Lee

We develop a convergence theory for non-monotone approximation schemes for fully nonlinear parabolic partial differential equations. Modern computational methods such as kernel-based collocation, spectral methods, physics-informed neural…

Numerical Analysis · Mathematics 2026-05-08 Yumiharu Nakano

This paper presents a weakly intrusive strategy for computing a low-rank approximation of the solution of a system of nonlinear parameter-dependent equations. The proposed strategy relies on a Newton-like iterative solver which only…

Numerical Analysis · Mathematics 2020-05-06 Loic Giraldi , Anthony Nouy

Consider a linear operator equation $x - Kx = f$, where $f$ is given and $K$ is a Fredholm integral operator with a Green's function type kernel defined on $C[0, 1]$. For $r \geq 0$, we employ the interpolatory projection at $2r + 1$…

Numerical Analysis · Mathematics 2026-02-20 Gobinda Rakshit , Shashank K. Shukla , Akshay S. Rane

We present numerical methods based on the fast Fourier transform (FFT) to solve convolution integral equations on a semi-infinite interval (Wiener-Hopf equation) or on a finite interval (Fredholm equation). We extend and improve a FFT-based…

Numerical Analysis · Mathematics 2021-06-11 Guido Germano , Carolyn E Phelan , Daniele Marazzina , Gianluca Fusai

A numerical method for approximating weak solutions of an aggregation equation with degenerate diffusion is introduced. The numerical method consists of a stabilized finite element method together with a mass lumping technique and an extra…

We propose a method based on sinc series approximations for computing the Rayleigh-Sommerfeld and Fresnel diffraction integrals of optics. The diffraction integrals are given in terms of a convolution, and our proposed numerical approach is…

Computational Physics · Physics 2021-11-16 Max Cubillos , Edwin Jimenez

The Legendre-based ultraspherical spectral method for ordinary differential equations is combined with a formula for the convolution of two Legendre series to produce a new technique for solving linear Fredholm and Volterra…

Numerical Analysis · Mathematics 2017-12-04 Nicholas Hale

In this paper, we provide a new scheme for approximating the weakly efficient solution set for a class of vector optimization problems with rational objectives over a feasible set defined by finitely many polynomial inequalities. More…

Optimization and Control · Mathematics 2022-05-26 Feng Guo , Liguo Jiao

Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper…

Machine Learning · Computer Science 2011-11-04 Sangkyun Lee , Stephen J. Wright

We present a machine learning approach to the inversion of Fredholm integrals of the first kind. The approach provides a natural regularization in cases where the inverse of the Fredholm kernel is ill-conditioned. It also provides an…

Strongly Correlated Electrons · Physics 2016-12-16 Louis-Francois Arsenault , Richard Neuberg , Lauren A. Hannah , Andrew J. Millis

The phenomenon of collisional breakage in particulate processes has garnered significant interest due to its wide-ranging applications in fields such as milling, astrophysics, and disk formation. This study investigates the analysis of the…

Analysis of PDEs · Mathematics 2024-12-04 Sanjiv Kumar Bariwal , Rajesh Kumar

A numerical scheme is presented for the solution of Fredholm second-kind boundary integral equations with right-hand sides that are singular at a finite set of boundary points. The boundaries themselves may be non-smooth. The scheme, which…

Numerical Analysis · Mathematics 2021-11-24 Johan Helsing , Shidong Jiang

The Fredholm integral equations of the first kind is a typical ill-posed problem, so that it is usually difficult to obtain its analytical minimal-norm solution. This paper gives a closed-form minimal-norm solution for the degenerate kernel…

Numerical Analysis · Mathematics 2024-06-12 Renjun Qiu , Ming Xu , Wei Qu

The variational approach to fracture is effective for simulating the nucleation and propagation of complex crack patterns, but is computationally demanding. The model is a strongly nonlinear non-convex variational inequality that demands…

Numerical Analysis · Mathematics 2016-05-18 Patrick E. Farrell , Corrado Maurini

We survey classical kernel methods for providing nonparametric solutions to problems involving measurement error. In particular we outline kernel-based methodology in this setting, and discuss its basic properties. Then we point to close…

Methodology · Statistics 2010-03-02 Aurore Delaigle , Peter Hall