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SUMMARY:Adaptive Pareto Front Sampling Based on Parametric Sensitivity Ana
lysis in a Bi-Objective Setting
DTSTART;VALUE=DATE-TIME:20181109T090000Z
DTEND;VALUE=DATE-TIME:20181109T093000Z
DTSTAMP;VALUE=DATE-TIME:20200705T045814Z
UID:indico-contribution-3865@indico.esa.int
DESCRIPTION:Speakers: Arne Berger ()\nIn order to solve non-linear multiob
jective optimization problems\, one usually solves multiple scalarized sub
problems. This provides a discrete approximation of the Pareto front which
gives useful information for the decision maker who\, in praxis\, has to
select one single solution. If the desired solution is not part of the pr
ecomputed discrete approximation one needs to apply interpolation techniqu
es.\nThis contribution shows a method which uses information from parametr
ic sensitivity analysis of the scalarized subproblems in order to choose t
he stepsize between samples adaptively to obtain a better interpolation be
tween precomputed solutions. The problems are solved with the NLP solver W
ORHP which provides sensitivity information in an efficient way by reusing
the factorization of the KKT matrix of the last optimization iteration. W
e show the basic functionality of the presented method by applying it to s
everal bi-objective optimization problems. The method can also be used for
more than two objectives if one can identify the neighboring precomputed
points which are then used for interpolation.\n\nhttps://indico.esa.int/ev
ent/224/contributions/3865/
LOCATION: Single 107
URL:https://indico.esa.int/event/224/contributions/3865/
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