scientific article; zbMATH DE number 7626752
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Publication:5053253
Hui Huang, Philippe Sünnen, Massimo Fornasier, Lorenzo Pareschi
Publication date: 6 December 2022
Full work available at URL: https://arxiv.org/abs/2001.11988
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
global optimizationmean-field limitnumerical methods for SDEconsensus-based optimizationasymptotic convergence analysisstochastic Kuramoto-Vicsek model
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Constrained Consensus-Based Optimization ⋮ Binary interaction methods for high dimensional global optimization and machine learning ⋮ Stochastic consensus dynamics for nonconvex optimization on the Stiefel manifold: Mean-field limit and convergence ⋮ Zero-Inertia Limit: From Particle Swarm Optimization to Consensus-Based Optimization ⋮ Anisotropic Diffusion in Consensus-Based Optimization on the Sphere ⋮ On the stochastic robustness of complete clustering predictability for a first‐order consensus model ⋮ On the mean‐field limit for the consensus‐based optimization ⋮ Reproducing kernel Hilbert spaces in the mean field limit ⋮ Kinetic-based optimization enhanced by genetic dynamics ⋮ Collective behaviors of stochastic agent-based models and applications to finance and optimization ⋮ An adaptive consensus based method for multi-objective optimization with uniform Pareto front approximation ⋮ On the global convergence of particle swarm optimization methods ⋮ Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling
Uses Software
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