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Konstantin Riedl
Konstantin Riedl
Technical University of Munich, Munich Center for Machine Learning
Zweryfikowany adres z ma.tum.de - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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Consensus-based optimization methods converge globally
M Fornasier, T Klock, K Riedl
arXiv preprint arXiv:2103.15130, 2021
362021
On the global convergence of particle swarm optimization methods
H Huang, J Qiu, K Riedl
Applied Mathematics & Optimization 88 (2), 30, 2023
242023
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
192022
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
112023
Consensus-based optimization for saddle point problems
H Huang, J Qiu, K Riedl
SIAM Journal on Control and Optimization 62 (2), 1093-1121, 2024
72024
Gradient is all you need?
K Riedl, T Klock, C Geldhauser, M Fornasier
arXiv preprint arXiv:2306.09778, 2023
42023
Consensus-Based Optimization with Truncated Noise
M Fornasier, P Richtárik, K Riedl, L Sun
European Journal of Applied Mathematics, 1-24, 2023
12023
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
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
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