57 citations · 148 across the 15 of their papers we have counts for
4 papers · 1 filter
Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law
Konstantin Riedl
In this paper we study consensus-based optimization (CBO), a versatile, flexible and customizable optimization method suitable for performing nonconvex and nonsmooth global optimiz…
On the Global Convergence of Particle Swarm Optimization Methods
Hui Huang, Jinniao Qiu, Konstantin Riedl
In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimization method by using tools from stochastic calculus and the analysis of partial dif…
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law
Massimo Fornasier, Timo Klock, Konstantin Riedl
In this paper we study anisotropic consensus-based optimization (CBO), a multi-agent metaheuristic derivative-free optimization method capable of globally minimizing nonconvex and…
Consensus-Based Optimization Methods Converge Globally
Massimo Fornasier, Timo Klock, Konstantin Riedl
In this paper, we study consensus-based optimization (CBO), which is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth…