Title	Parameter Calibration for Optimization under Uncertainty
Creator	Lam, Henry
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-03-06T14:41
Description	Optimization formulations to handle decision-making under uncertainty often contain parameters needed to be calibrated from data. Examples include uncertainty set sizes in robust optimization, and Monte Carlo sample sizes in constraint sampling or scenario generation. We investigate strategies to select good parameter values based on data splitting and the validation of their performances in terms of feasibility and optimality. We analyze the effectiveness of these strategies in relation to the complexity of the optimization class and problem dimension.
Extent	29 minutes
Subject	Mathematics; Operations research, mathematical programming; Probability theory and stochastic processes; Operation research
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Michigan
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-09-03
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0371891
URI	http://hdl.handle.net/2429/67075
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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