output
20182026
most citedGenerative AI

1.3k citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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6 papers · 1 filter

math.NA2026

Refined rates of convergence for target-data dependent greedy generalized interpolation with Sobolev kernels

Bernard Haasdonk, Gabriele Santin, Tizian Wenzel +1

Greedy methods have recently been successfully applied to generalized kernel interpolation, or the recovery of a function from data stemming from the evaluation of linear functiona…

math.NA2025

A Variational Framework for the Complexity of PDE Solutions

Juan Esteban Suarez Cardona, Holger Boche, Gitta Kutyniok

Partial Differential Equations (PDEs) are fundamental mathematical models for describing physical phenomena, yet most PDEs of practical interest require numerical approximations. T…

math.NA2025

Kernel-based Greedy Approximation of Parametric Elliptic Boundary Value Problems

Bernard Haasdonk, Gabriele Santin, Tizian Wenzel

We recently introduced a scale of kernel-based greedy schemes for approximating the solutions of elliptic boundary value problems. The procedure is based on a generalized interpola…

math.NA2022★ 14 cited

Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law

Konstantin Riedl

In this paper we study consensus-based optimization (CBO), a versatile, flexible and customizable optimization method suitable for performing nonconvex and nonsmooth global optimiz…

math.NA2022★ 4 cited

An adaptive damped Newton method for strongly monotone and Lipschitz continuous operator equations

Pascal Heid

We will consider the damped Newton method for strongly monotone and Lipschitz continuous operator equations in a variational setting. We will provide a very accessible justificatio…

math.NA2022★ 57 cited

On the Global Convergence of Particle Swarm Optimization Methods

Hui Huang, Jinniao Qiu, Konstantin Riedl

In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimization method by using tools from stochastic calculus and the analysis of partial dif…