Consensus-based optimization methods converge globally M Fornasier, T Klock, K Riedl arXiv preprint arXiv:2103.15130, 2021 | 43 | 2021 |
On the global convergence of particle swarm optimization methods H Huang, J Qiu, K Riedl Applied Mathematics & Optimization 88 (2), 30, 2023 | 34 | 2023 |
Convergence of anisotropic consensus-based optimization in mean-field law M Fornasier, T Klock, K Riedl International Conference on the Applications of Evolutionary Computation …, 2022 | 27 | 2022 |
Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law K Riedl European Journal of Applied Mathematics, 1-32, 2023 | 17 | 2023 |
Consensus-based optimization for saddle point problems H Huang, J Qiu, K Riedl SIAM Journal on Control and Optimization 62 (2), 1093-1121, 2024 | 11 | 2024 |
Gradient is all you need? K Riedl, T Klock, C Geldhauser, M Fornasier arXiv preprint arXiv:2306.09778, 2023 | 7 | 2023 |
Consensus-Based Optimization with Truncated Noise M Fornasier, P Richtárik, K Riedl, L Sun European Journal of Applied Mathematics, 1-24, 2023 | 6 | 2023 |
CBX: Python and Julia packages for consensus-based interacting particle methods R Bailo, A Barbaro, SN Gomes, K Riedl, T Roith, C Totzeck, U Vaes arXiv preprint arXiv:2403.14470, 2024 | 4 | 2024 |
How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent K Riedl, T Klock, C Geldhauser, M Fornasier ICML 2024 Workshop on Differentiable Almost Everything: Differentiable …, 2024 | | 2024 |
Understanding Consensus-Based Optimization: Two Analytical Perspectives K Riedl Oberwolfach Reports 20 (4), 2990-2992, 2024 | | 2024 |
Non-Convex Approaches to Compressed Sensing and Robust Recovery of Simultaneously Structured Signals from Inaccurate and Incomplete Information K Riedl Technical University of Munich, 2019 | | 2019 |
On multilevel algorithms for the estimation of failure probabilities and rare event simulation K Riedl Technical University of Munich, 2018 | | 2018 |