Bonandin, SabrinaHerty, Michael2025-09-152025-09-152025Bonandin, S. & Herty, M. 2025, 'Kinetic variable-sample methods for stochastic optimization problems', Communications on Pure and Applied Analysis, doi : 10.3934/cpaa.2025081.1534-0392 (print)1553-5258 (online)10.3934/cpaa.2025081http://hdl.handle.net/2263/104317We discuss kinetic-based particle optimization methods and variable-sample strategies for problems where the cost function represents the expected value of a random mapping. Kinetic-based optimization methods rely on a consensus mechanism targeting the global minimizer, and they exploit tools of kinetic theory to establish a rigorous framework for proving convergence to that minimizer. Variable-sample strategies replace the expected value by an approximation at each iteration of the optimization algorithm. We combine these approaches and introduce a novel algorithm based on instantaneous collisions governed by a linear Boltzmann-type equation. After proving the convergence of the resulting kinetic method under appropriate parameter constraints, we establish a connection to a recently introduced consensus-based method for solving the random problem in a suitable scaling. Finally, we showcase its enhanced computational efficiency compared to the aforementioned algorithm and validate the consistency of the proposed modeling approaches through several numerical experiments.en© 2025 American Institute of Mathematical Sciences.Global optimizationStochastic optimization problemsParticle-based methodsConsensus-based optimizationBoltzmann equationKinetic equationsPostprint Article