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We investigate a singularly perturbed, non-convex variational problem arising in materials science with a combination of geometrical and numerical methods. Our starting point is a work by Stefan M\"uller, where it is proven that the…

Dynamical Systems · Mathematics 2026-04-10 Annalisa Iuorio , Christian Kuehn , Peter Szmolyan

In order to develop a differential calculus for error propagation we study local Dirichlet forms on probability spaces with square field operator $\Gamma$ -- i.e. error structures -- and we are looking for an object related to $\Gamma$…

Probability · Mathematics 2007-05-23 Nicolas Bouleau

The problem of determining the configuration of points from partial distance information, known as the Euclidean Distance Geometry (EDG) problem, is fundamental to many tasks in the applied sciences. In this paper, we propose two algorithms…

Optimization and Control · Mathematics 2024-10-10 Chandler Smith , HanQin Cai , Abiy Tasissa

A variant of the usual Lagrangian scheme is developed which describes both the equations of motion and the variational equations of a system. The required (prolonged) Lagrangian is defined in an extended configuration space comprising both…

Mathematical Physics · Physics 2016-09-21 C. M. Arizmendi , J. Delgado , H. N. Núñez-Yépez , A. L. Salas-Brito

Gradient descent algorithms have been used in countless applications since the inception of Newton's method. The explosion in the number of applications of neural networks has re-energized efforts in recent years to improve the standard…

Machine Learning · Computer Science 2020-10-30 Chad Kelterborn , Marcin Mazur , Bogdan V. Petrenko

Algorithms based on gradient descent for computing the elastic shape registration of two simple surfaces in 3-dimensional space and therefore the elastic shape distance between them have been proposed by Kurtek, Jermyn, et al., and more…

Graphics · Computer Science 2024-11-21 Javier Bernal , Jim Lawrence

In this paper we provide a definition of fractional gradient operators, related to directional derivatives. We develop a fractional vector calculus, providing a probabilistic interpretation and mathematical tools to treat multidimensional…

Mathematical Physics · Physics 2013-05-21 M. D'Ovidio , R. Garra

We propose an algorithmic framework, that employs active subspace techniques, for scalable global optimization of functions with low effective dimension (also referred to as low-rank functions). This proposal replaces the original…

Optimization and Control · Mathematics 2024-02-01 Coralia Cartis , Xinzhu Liang , Estelle Massart , Adilet Otemissov

This is the first of a series of papers devoted to a thorough analysis of the class of gradient flows in a metric space $(X,\mathsf{d})$ that can be characterized by Evolution Variational Inequalities. We present new results concerning the…

Functional Analysis · Mathematics 2018-10-10 Matteo Muratori , Giuseppe Savaré

The Evans function has become a standard tool in the mathematical study of nonlinear wave stability. In particular, computation of its zero set gives a convenient numerical method for determining the point spectrum of the associated linear…

Analysis of PDEs · Mathematics 2017-07-10 Blake Barker , Jeffrey Humpherys , Gregory Lyng , Kevin Zumbrun

Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct…

Computer Vision and Pattern Recognition · Computer Science 2020-11-11 Rick Groenendijk , Sezer Karaoglu , Theo Gevers , Thomas Mensink

We propose an efficient semi-Lagrangian method for solving the two-dimensional incompressible Euler equations with high precision on a coarse grid. The new approach evolves the flow map using the gradient-augmented level set method (GALSM).…

Numerical Analysis · Mathematics 2023-02-21 Xi-Yuan Yin , Olivier Mercier , Badal Yadav , Kai Schneider , Jean-Christophe Nave

We introduce a new framework of numerical multiscale methods for advection-dominated problems motivated by climate sciences. Current numerical multiscale methods (MsFEM) work well on stationary elliptic problems but have difficulties when…

Computational Engineering, Finance, and Science · Computer Science 2019-05-28 Konrad Simon , Jörn Behrens

Nonlocal models have recently had a major impact in nonlinear continuum mechanics and are used to describe physical systems/processes which cannot be accurately described by classical, calculus based "local" approaches. In part, this is due…

Optimization and Control · Mathematics 2021-03-10 Sriram Nagaraj

This paper deals with nonsmooth convex optimization problems in Euclidean spaces. We identify special elements of the subdifferential of a convex function, called specular gradients. Based on this observation, we propose three numerical…

Optimization and Control · Mathematics 2026-05-26 Kiyuob Jung

We deal with boundary value problems for second-order nonlinear elliptic equations in divergence form, which emerge as Euler-Lagrange equations of integral functionals of the Calculus of Variations built upon possibly anisotropic norms of…

Analysis of PDEs · Mathematics 2023-10-02 Carlo Alberto Antonini , Andrea Cianchi , Giulio Ciraolo , Alberto Farina , Vladimir Maz'ya

We propose the use of algebras of generalized functions for the analysis of certain highly singular problems in the calculus of variations. After a general study of extremal problems on open subsets of Euclidean space in this setting we…

Functional Analysis · Mathematics 2008-09-11 Sanja Konjik , Michael Kunzinger , Michael Oberguggenberger

E727 in the Enestrom index. This is a translation from the Latin original "Accuratior evolutio problematis de linea brevissima in superficie quacunque ducenda" (1779). Given a surface $pdx+qdy+rdz=0$, Euler wants to develop equations that…

History and Overview · Mathematics 2008-01-08 Leonhard Euler

Several recent works have explored stochastic gradient methods for variational inference that exploit the geometry of the variational-parameter space. However, the theoretical properties of these methods are not well-understood and these…

Machine Learning · Statistics 2016-08-15 Mohammad Emtiyaz Khan , Reza Babanezhad , Wu Lin , Mark Schmidt , Masashi Sugiyama

With the increasing interest in applying the methodology of difference-of-convex (dc) optimization to diverse problems in engineering and statistics, this paper establishes the dc property of many well-known functions not previously known…

Optimization and Control · Mathematics 2019-02-20 Maher Nouiehed , Jong-Shi Pang , Meisam Razaviyayn