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We define a family of functionals, called p-oscillation functionals, that can be interpreted as discrete versions of the classical total variation functional for p=1 and of the p-Dirichlet functionals for p>1. We introduce the notion of…

Analysis of PDEs · Mathematics 2018-03-06 Annalisa Cesaroni , Serena Dipierro , Matteo Novaga , Enrico Valdinoci

This paper establishes a strict mathematical relationship between an arbitrary continuous function on a compact set and its global minima, like the well-known first order optimality condition for convex and differentiable functions. By…

Optimization and Control · Mathematics 2019-05-27 Xiaopeng Luo

This paper is on $\Gamma$-convergence for degenerate integral functionals related to homogenisation problems in the Heisenberg group. Here both the rescaling and the notion of invariance or periodicity are chosen in a way motivated by the…

Analysis of PDEs · Mathematics 2019-06-28 Nicolas Dirr , Federica Dragoni , Paola Mannucci , Claudio Marchi

We present a rigorous convergence analysis for cylindrical approximations of nonlinear functionals, functional derivatives, and functional differential equations (FDEs). The purpose of this analysis is twofold: first, we prove that…

Numerical Analysis · Mathematics 2021-03-17 Daniele Venturi , Alec Dektor

We consider in 2D the following special case of the Mumford-Shah functional $$ J(u, \Gamma)=\int_{B_1\backslash\Gamma} |\nabla u|^2 dx + \lambda^2 \frac{\pi}{2} \mathcal{H}^1(\Gamma). $$ It is known that if the minimizer has a crack-tip in…

Analysis of PDEs · Mathematics 2020-12-16 John Andersson , Hayk Mikayelyan

We consider the numerical computation of a variational problem that arises from materials science. The target functional is a type of elastic energy that is influenced by obstacles and adhesion. Owing to its strong nonlinearity and…

Numerical Analysis · Mathematics 2016-04-13 T. Kemmochi

The proximal gradient algorithm for minimizing the sum of a smooth and a nonsmooth convex function often converges linearly even without strong convexity. One common reason is that a multiple of the step length at each iteration may…

Optimization and Control · Mathematics 2016-06-29 Dmitriy Drusvyatskiy , Adrian S. Lewis

The present work constitutes a first step towards establishing a systematic framework for treating variational problems that depend on a given input function through a mixture of its derivatives of different orders in different directions.…

Analysis of PDEs · Mathematics 2021-02-09 Adam Prosinski

In this paper we provide an approximation \`a la Ambrosio-Tortorelli of some classical minimization problems involving the length of an unknown one-dimensional set, with an additional connectedness constraint, in dimension two. We introduce…

Metric Geometry · Mathematics 2014-03-13 Matthieu Bonnivard , Antoine Lemenant , Filippo Santambrogio

We study the asymptotic behaviour of sequences of integral functionals depending on moving anisotropies. We introduce and describe the relevant functional setting, establishing uniform Meyers-Serrin type approximations, Poincar\'e…

Analysis of PDEs · Mathematics 2025-04-04 Alberto Maione , Fabio Paronetto , Simone Verzellesi

We propose an alternative to $k$-nearest neighbors for functional data whereby the approximating neighboring curves are piecewise functions built from a functional sample. Using a locally defined distance function that satisfies…

Methodology · Statistics 2021-06-02 Antonio Elías , Raúl Jiménez , Joe Yukich

In this paper we introduce two conceptual algorithms for minimising abstract convex functions. Both algorithms rely on solving a proximal-type subproblem with an abstract Bregman distance based proximal term. We prove their convergence when…

Optimization and Control · Mathematics 2026-01-09 Reinier Díaz Millán , Julien Ugon

Discrete approximations to the equation \begin{equation*} L_{cont}u = u^{(4)} + D(x) u^{(3)} + A(x) u^{(2)} + (A'(x)+H(x)) u^{(1)} + B(x) u = f, \; x\in[0,1] \end{equation*} are considered. This is an extension of the Sturm-Liouville case…

Numerical Analysis · Mathematics 2020-04-06 Matania Ben-Artzi , Benjamin Kramer

We consider a Neumann problem for strictly convex variational functionals of linear growth. We establish the existence of minimisers among $\operatorname{W}^{1,1}$-functions provided that the domain under consideration is simply connected.…

Analysis of PDEs · Mathematics 2019-04-15 Lisa Beck , Miroslav Bulíček , Franz Gmeineder

This paper can be seen as an attempt of rethinking the {\em Extra-Gradient Philosophy} for solving Variational Inequality Problems. We show that the properly defined {\em Reduced Gradients} can be used instead for finding approximate…

Optimization and Control · Mathematics 2023-12-05 Yurii Nesterov

We establish the existence and symmetry of all minimizers of a constrained variational problem involving the fractional gradient. This problem is closely connected to some fractional kinetic equations.

Analysis of PDEs · Mathematics 2012-05-08 H. Hajaiej

A common approach for minimizing a smooth nonlinear function is to employ finite-difference approximations to the gradient. While this can be easily performed when no error is present within the function evaluations, when the function is…

Optimization and Control · Mathematics 2022-03-24 Hao-Jun Michael Shi , Yuchen Xie , Melody Qiming Xuan , Jorge Nocedal

The article considers the discrete analogue of the method of quickest descent for an inverse Acoustics problem in case of a smooth source. The authors derived the gradient of functional in differential and discrete cases, described the…

Computational Engineering, Finance, and Science · Computer Science 2016-01-19 G. Tyulepberdinova , G. Gaziz , N. Kerimbayev , S. Abdykarimova

We prove results on the relaxation and weak* lower semicontinuity of integral functionals of the form \[ \mathcal{F}[u] := \int_{\Omega} f \bigg( \frac{1}{2} \bigl( \nabla u(x) + \nabla u(x)^T \bigr) \bigg)\,\mathrm{d} x, \qquad u : \Omega…

Analysis of PDEs · Mathematics 2020-03-03 Kamil Kosiba , Filip Rindler

This chapter describes how gradient flows and nonlinear power methods in Banach spaces can be used to solve nonlinear eigenvector-dependent eigenvalue problems, and how convergence of (discretized) approximations can be verified. We review…

Numerical Analysis · Mathematics 2022-03-15 Leon Bungert , Martin Burger
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