Forecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model
Wiese, S
Kaszowska-Mojsa, J
Dyer, J
Moran, J
Pangallo, M
Lafond, F
Muellbauer, J
Calinescu, A
Farmer, J
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How consensus-based optimization can be interpreted as a stochastic relaxation of gradient descent
Riedl, K
Klock, T
Geldhauser, C
Fornasier, M
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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
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