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SUMMARY:Stochastic particle systems for global optimization: a journey fro
 m metaheuristics to PDEs - Lorenzo Pareschi\, Heriot-Watt University
DTSTART:20240607T120000Z
DTEND:20240607T130000Z
UID:TALK217042@talks.cam.ac.uk
CONTACT:AI Aviles-Rivero
DESCRIPTION:Optimization methods based on stochastic particle systems have
  a rich history and hold significant importance in various applications to
 day\, spanning from machine learning to optimal control. Many of these met
 hods rely on metaheuristic algorithms\, which often lack a rigorous mathem
 atical foundation. Recently\, leveraging tools inspired by statistical phy
 sics has enabled the description of these gradient-free algorithms through
  the lens of kinetic and mean-field PDEs. This approach provides convergen
 ce guarantees to the global minimum under mild assumptions on the objectiv
 e function and allows for the introduction of novel enhancements to improv
 e the algorithms' performance. In this presentation\, we will exemplify th
 ese concepts using popular algorithms like simulated annealing\, genetic a
 lgorithms and particle swarm optimization.\n
LOCATION:MR2 Centre for Mathematical Sciences
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