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X-FROM-URL:https://www.cs.jhu.edu/~mdinitz/theory
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DESCRIPTION:Speaker: Leonidas Tsepenekas\nAffiliation: University of Maryla
nd\nTitle: Approximating Two-Stage Stochastic Supplier Problems\nAbstract:
\nThe main focus of this talk will be radius-based (supplier) clustering i
n the two-stage stochastic setting with recourse\, where the inherent stoc
hasticity of the model comes in the form of a budget constraint. Our event
ual goal is to provide results in the most general distributional setting\
, where there is only black-box access to the underlying distribution. To
that end\, we follow a two-step approach. First\, we develop algorithms fo
r a restricted version of the problem\, in which all possible scenarios ar
e explicitly provided\; second\, we employ a novel scenario-discarding var
iant of the standard Sample Average Approximation (SAA) method\, in which
we also crucially exploit structural properties of the algorithms develope
d for the first step of the framework. In this way\, we manage to generali
ze the results of the latter to the black-box model. Finally\, we note tha
t the scenario-discarding modification to the SAA method is necessary in o
rder to optimize over the radius.\nPaper: https://arxiv.org/abs/2008.03325
DTSTART;TZID=America/New_York:20210407T120000
DTEND;TZID=America/New_York:20210407T130000
LOCATION:https://wse.zoom.us/j/91450299380
SEQUENCE:0
SUMMARY:[Theory Seminar] Leonidas Tsepenekas
URL:https://www.cs.jhu.edu/~mdinitz/theory/event/theory-seminar-leonidas-ts
epenekas/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nSpeaker: Leon
idas Tsepenekas

\nAffiliation: University of Maryland

\nTitle:
Approximating Two-Stage Stochastic Supplier Problems

\nAbstract:

\nThe main focus of this talk will be radius-based (supplier) clustering
in the two-stage stochastic setting with recourse\, where the inherent st
ochasticity of the model comes in the form of a budget constraint. Our eve
ntual goal is to provide results in the most general distributional settin
g\, where there is only black-box access to the underlying distribution. T
o that end\, we follow a two-step approach. First\, we develop algorithms
for a restricted version of the problem\, in which all possible scenarios
are explicitly provided\; second\, we employ a novel scenario-discarding v
ariant of the standard Sample Average Approximation (SAA) method\, in whic
h we also crucially exploit structural properties of the algorithms develo
ped for the first step of the framework. In this way\, we manage to genera
lize the results of the latter to the black-box model. Finally\, we note t
hat the scenario-discarding modification to the SAA method is necessary in
order to optimize over the radius.

\nPaper: https://arxiv.org/abs/2
008.03325

\n
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