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/-73.95623016,40.75563049,13,0,0/550x200?access_token=pk.eyJ1IjoiZ2FyeXNndWlkZSIsImEiOiJjamtobmRiYmgwb3gwM3F1a2o2Mmd0YWw3In0.DinA62CR3-OKtf3axa5O7Q)
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A variety of theoretical frameworks, e.g. the Lasserre hierarchy of relaxations, basic steps in generalized disjunctive programming, & the reformulation linearization technique, offer discrete levels for optimization formulations & relaxations. These discrete choices, for instance picking the first versus the second level of the Lasserre hierarchy, may be problematic for computational optimization strategies. For example, the first order Lasserre relaxation may be too loose but solving the second order Lasserre relaxation may be too computationally expensive. This presentation considers developing intermediates between theoretical optimization frameworks that classically come with discrete levels. We explore the tradeoff between possibly better computational performance with these between formulations versus the possibly explosive number of parameters these formulations introduce.
BIO
Dr Ruth Misener (she/her) is a Professor in the Computational Optimization Group. Foundations of her research are in numerical optimization algorithms & computational software. Her applications focus on optimization challenges arising in industry, e.g. scheduling in manufacturing or experimental design in chemicals research. Ruth also contributes at the interface between operations research & machine learning.
Ruth is the BASF/RAEng Research Chair in Data-Driven Optimization (2022-27). She received the Macfarlane Medal as the overall winner of the 2017 RAEng Engineers Trust Young Engineer of the Year competition. Ruth's research team develops open-source code on GitHub, releases video presentations on YouTube, & announces new research on Twitter (Ruth @RuthMisener, Group @CogImperial).
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