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20112025
most citedConvergence rates for dispersive approximation schemes to nonlinear Schrödinger equations

2 citations · 2 across the 5 of their papers we have counts for

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6 papers

math.NA2025

Sharp numerical approximation of the Hardy constant

Liviu I. Ignat, Enrique Zuazua

We study the finite element approximation of the best constant in the classical Hardy inequality over bounded domains containing the origin in , for .…

math.NA2025

Optimal convergence rates for the finite element approximation of the Sobolev constant

Liviu I. Ignat, Enrique Zuazua

We establish optimal convergence rates for the continuous piecewise affine finite element approximation of the Sobolev constant in arbitrary dimensions N\geq 2 and for Lebesgue exp…

cs.LG2024

Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

Kang Liu, Ziqi Wang, Enrique Zuazua

In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients may independently choose their tr…

cs.LG2024

FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning

Yongcun Song, Ziqi Wang, Enrique Zuazua

Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challen…

math.NA2024

Optimal control approach for moving bottom detection in one-dimensional shallow waters by surface measurements

Gino Montecinos, Rodrigo Lecaros, Juan López-Ríos +1

We consider the Boussinesq-Peregrine (BP) system as described by Lannes [Lannes, D. (2013). The water waves problem: mathematical analysis and asymptotics (Vol. 188). American Math…

math.NA2011★ 2 cited

Convergence rates for dispersive approximation schemes to nonlinear Schrödinger equations

Liviu Ignat, Enrique Zuazua

This article is devoted to the analysis of the convergence rates of several nu- merical approximation schemes for linear and nonlinear Schrödinger equations on the real line. Recen…