Mathematical Institute

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)

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)

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