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Consensus-Based Optimization on the Sphere: Convergence to Global
  Minimizers and Machine Learning

Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning

31 January 2020
M. Fornasier
Hui Huang
L. Pareschi
Philippe Sünnen
ArXivPDFHTML

Papers citing "Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning"

3 / 3 papers shown
Title
Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization
Explainable and Class-Revealing Signal Feature Extraction via Scattering Transform and Constrained Zeroth-Order Optimization
N. Saito
David S. Weber
37
0
0
08 Feb 2025
Reproducing kernel Hilbert spaces in the mean field limit
Reproducing kernel Hilbert spaces in the mean field limit
Christian Fiedler
Michael Herty
M. Rom
C. Segala
Sebastian Trimpe
19
6
0
28 Feb 2023
Leveraging Memory Effects and Gradient Information in Consensus-Based
  Optimization: On Global Convergence in Mean-Field Law
Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law
Konstantin Riedl
16
14
0
22 Nov 2022
1