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UID:pretalx-juliacon2023-8FAGEC@pretalx.com
DTSTART;TZID=EST:20230728T103000
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DESCRIPTION:We present the key ideas for finding first-order critical point
 s of multi-objective optimization problems with nonlinear objectives and c
 onstraints. A gradient-based trust-region algorithm is modified to employ 
 local\, derivative-free surrogate models instead\, and a so-called Filter 
 ensures convergence towards feasibility. We show results of a prototype im
 plementation in Julia\, relying heavily on JuMP and suitable LP or QP solv
 ers\, that confirm the use of surrogates to reduce function calls.
DTSTAMP:20260413T023503Z
LOCATION:32-D463 (Star)
SUMMARY:Surrogate-Assisted Multi-Objective Optimization with Constraints - 
 Manuel Berkemeier
URL:https://pretalx.com/juliacon2023/talk/8FAGEC/
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