Mathematical Institute

How consensus-based optimization can be interpreted as a stochastic relaxation of gradient descent

Riedl, K Klock, T Geldhauser, C Fornasier, M (27 Jun 2024)

Convergence of anisotropic consensus-based optimization in mean-field law

Fornasier, M Riedl, K Klock, T Proceedings of the 25th International Conference on the Applications of Evolutionary Computation (EvoApplications 2022) 738-754 (15 Apr 2022)

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

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

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?

Riedl, K Klock, T Geldhauser, C Fornasier, M (16 Jun 2023) http://arxiv.org/abs/2306.09778v1

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)

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