On the Global Convergence of Particle Swarm Optimization Methods
Huang, H
Qiu, J
Riedl, K
(29 Jan 2022)
http://arxiv.org/abs/2201.12460v2
Consensus-Based Optimization for Saddle Point Problems
Huang, H
Qiu, J
Riedl, K
(23 Dec 2022)
http://arxiv.org/abs/2212.12334v2
Leveraging Memory Effects and Gradient Information in Consensus-Based
Optimization: On Global Convergence in Mean-Field Law
Riedl, K
(22 Nov 2022)
http://arxiv.org/abs/2211.12184v2
Optimization: On Global Convergence in Mean-Field Law
Consensus-Based Optimization with Truncated Noise
Fornasier, M
Richtárik, P
Riedl, K
Sun, L
(25 Oct 2023)
http://arxiv.org/abs/2310.16610v2
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field
Law
Fornasier, M
Klock, T
Riedl, K
(15 Nov 2021)
http://arxiv.org/abs/2111.08136v2
Law
Consensus-Based Optimization Methods Converge Globally
Fornasier, M
Klock, T
Riedl, K
(28 Mar 2021)
http://arxiv.org/abs/2103.15130v6
Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
Riedl, K
Klock, T
Geldhauser, C
Fornasier, M
(16 Jun 2023)
https://arxiv.org/abs/2306.09778v2
Consensus-based optimization for saddle point problems
Huang, H
Qiu, J
Riedl, K
SIAM Journal on Control and Optimization
volume 62
issue 2
1093-1121
(25 Mar 2024)
On the Global Convergence of Particle Swarm Optimization Methods
Huang, H
Qiu, J
Riedl, K
Applied Mathematics & Optimization
volume 88
issue 2
(31 May 2023)
Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law
Riedl, K
European Journal of Applied Mathematics
volume 35
issue 4
483-514
(20 Oct 2023)