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We perform a convergence analysis of a discrete-in-time minimization scheme approximating a finite dimensional singularly perturbed gradient flow. We allow for different scalings between the viscosity parameter $\varepsilon$ and the time…

Analysis of PDEs · Mathematics 2018-11-14 Giovanni Scilla , Francesco Solombrino

Accelerated gradient descent iterations are widely used in optimization. It is known that, in the continuous-time limit, these iterations converge to a second-order differential equation which we refer to as the accelerated gradient flow.…

Optimization and Control · Mathematics 2020-06-16 Mohammad Farazmand

This work is concerned with the gradient flow of absolutely $p$-homogeneous convex functionals on a Hilbert space, which we show to exhibit finite ($p<2$) or infinite extinction time ($p \geq 2$). We give upper bounds for the finite…

Analysis of PDEs · Mathematics 2020-12-25 Leon Bungert , Martin Burger

The motions of a passive scalar $\hat{a}$ in a general high-frequency oscillating flow are studied. Our aim is threefold: (i) to obtain different classes of general solutions; (ii) to identify, classify, and develop related asymptotic…

Fluid Dynamics · Physics 2010-09-22 V. A. Vladimirov

In this note we study the singular vanishing-viscosity limit of a gradient flow set in a finite-dimensional Hilbert space and driven by a smooth, but possibly non convex, time-dependent energy functional. We resort to ideas and techniques…

Analysis of PDEs · Mathematics 2016-11-28 Virginia Agostiniani , Riccarda Rossi

We study the asymptotic convergence of solutions as $t\rightarrow\infty$ of $\partial_t u=-f(u)+\int f(u)$, a nonlocal differential equation that is formally a gradient flow in a constant-mass subspace of $L^2$ arising from simplified…

Classical Analysis and ODEs · Mathematics 2024-09-16 Sangmin Park , Robert L. Pego

We propose a quasi-Grassmannian gradient flow model for eigenvalue problems of linear operators, aiming to efficiently address many eigenpairs. Our model inherently ensures asymptotic orthogonality: without the need for initial…

Numerical Analysis · Mathematics 2025-06-27 Shengyue Wang , Aihui Zhou

In view of solving convex optimization problems with noisy gradient input, we analyze the asymptotic behavior of gradient-like flows under stochastic disturbances. Specifically, we focus on the widely studied class of mirror descent schemes…

Optimization and Control · Mathematics 2017-09-21 Panayotis Mertikopoulos , Mathias Staudigl

We study gradient flows for loss landscapes of fully connected feedforward neural networks with commonly used continuously differentiable activation functions such as the logistic, hyperbolic tangent, softplus or GELU function. We prove…

Machine Learning · Computer Science 2026-01-13 Julian Kranz , Davide Gallon , Steffen Dereich , Arnulf Jentzen

The gradient-flow dynamics of an arbitrary geometric quantity is derived using a generalization of Darcy's Law. We consider flows in both Lagrangian and Eulerian formulations. The Lagrangian formulation includes a dissipative modification…

Adaptation and Self-Organizing Systems · Physics 2008-04-28 Darryl D. Holm , Vakhtang Putkaradze , Cesare Tronci

We study the equation of one-dimensional quasistatic nonlinear viscoelasticity with Dirichlet boundary conditions, in the particular case that the underlying dissipation geometry (provided by the viscosity) is comparable to the Bhattacharya…

Analysis of PDEs · Mathematics 2026-05-12 Alexander Mielke , Billy Sumners

This paper is devoted to the investigation of gradient flows in asymmetric metric spaces (for example, irreversible Finsler manifolds and Minkowski normed spaces) by means of discrete approximation. We study basic properties of curves and…

Differential Geometry · Mathematics 2023-07-21 Shin-ichi Ohta , Wei Zhao

In this paper we study the asymptotic behavior of a very fast diffusion PDE in 1D with periodic boundary conditions. This equation is motivated by the gradient flow approach to the problem of quantization of measures introduced in…

Analysis of PDEs · Mathematics 2016-02-29 Mikaela Iacobelli

In a Hilbert space $\mathcal H$, we study the asymptotic behaviour, as time variable $t$ goes to $+\infty$, of nonautonomous gradient-like dynamical systems involving inertia and multiscale features. Given $\mathcal H$ a general Hilbert…

Optimization and Control · Mathematics 2016-02-02 Hedy Attouch , Marc-Olivier Czarnecki

We study the existence and long-time asymptotics of weak solutions to a system of two nonlinear drift-diffusion equations that has a gradient flow structure in the Wasserstein distance. The two equations are coupled through a…

Analysis of PDEs · Mathematics 2021-12-14 Lisa Beck , Daniel Matthes , Martina Zizza

We extend classical finite-dimensional Fenichel theory in two directions to infinite dimensions. Under comparably weak assumptions we show that the solution of an infinite-dimensional fast-slow system is approximated well by the…

Dynamical Systems · Mathematics 2020-08-26 Felix Hummel , Christian Kuehn

We analyze gradient flows with jumps generated by a finite set of complete vector fields in involution using some Radon measures $u\in \mathcal{U}_a$ as admissible perturbations. Both the evolution of a bounded gradient flow $\{x^u(t,\l)\in…

Analysis of PDEs · Mathematics 2012-03-09 Saima Parveen , Constantin Varsan

Dissipation anomaly-the persistence of finite energy dissipation in the inviscid limit-is a hallmark of turbulence, sometimes regarded as the "zeroth law" of turbulent flows. Here, we demonstrate that this phenomenon is not exclusive to…

Statistical Mechanics · Physics 2025-11-25 Hiroyoshi Nakano , Yuki Minami

A continuous sequence of infinitesimal unitary transformations is used to diagonalize the quantum sine-Gordon model for \beta^2\in(2\pi,\infty). This approach can be understood as an extension of perturbative scaling theory since it links…

Statistical Mechanics · Physics 2009-10-31 Stefan Kehrein

The paper surveys recent progresses in understanding the dynamics and loss landscape of the gradient flow equations associated to deep linear neural networks, i.e., the gradient descent training dynamics (in the limit when the step size…

Machine Learning · Computer Science 2025-11-14 Joel Wendin , Claudio Altafini
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