A variety of theoretical frameworks, e.g. the Lasserre hierarchy of relaxations, basic steps in generalized disjunctive programming, and the reformulation linearization technique, offer discrete levels for optimization formulations and 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 Computational Optimization in the Imperial College London Department of Computing. Foundations of her research are in numerical optimization algorithms and 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 and machine learning. Ruth received an SB from MIT (2007) and her PhD (2012), from Princeton.

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. Her work has been recognized with best paper awards from: the Journal of Global Optimization (2013), International Conference on Autonomous Agents & Multi-Agent Systems (Best Innovative Demo, 2020), Conference on the Integration of Constraint Programming, Artificial Intelligence, & Operations Research (2021), and Optimization & Engineering (2021). Ruth’s research team develops popular open-source code, for example the Optimization & Machine Learning Toolkit won the 2022 COIN-OR Cup for its contribution to open-source operations research software development.