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SUMMARY:Consensus-Based Optimization and Sampling - Franca Hoffmann (CALTE
 CH (California Institute of Technology))
DTSTART:20240715T100000Z
DTEND:20240715T110000Z
UID:TALK219157@talks.cam.ac.uk
DESCRIPTION:Particle methods provide a powerful paradigm for solving compl
 ex global optimization problems leading to highly parallelizable algorithm
 s. Despite widespread and growing adoption\, theory underpinning their beh
 avior has been mainly based on meta-heuristics. In application settings in
 volving black-box procedures\, or where gradients are too costly to obtain
 \, one relies on derivative-free approaches instead. This talk will focus 
 on two recent techniques\, consensus-based optimization and consensus-base
 d sampling. We explain how these methods can be used for the following two
  goals: (i) generating approximate samples from a given target distributio
 n\, and (ii) optimizing a given objective function. They circumvent the ne
 ed for gradients via Laplace's principle. We investigate the properties of
  this family of methods in terms of various parameter choices and present 
 an overview of recent advances in the field.
LOCATION:External
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