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Nick Sahinidis: Global black-box optimization


This talk presents recent theoretical, algorithmic, and methodological advances for black-box optimization problems for which optimization must be performed in the absence of an algebraic formulation, i.e., by utilizing only data originating from simulations or experiments. We investigate the relative merits of optimizing surrogate models based on generalized linear models and deep learning. Additionally, we present new optimization algorithms for direct data-driven optimization. Our approach combines model-based search with a dynamic domain partition strategy that guarantees convergence to a global optimum. Equipped with a clustering algorithm for balancing global and local search, the proposed approach outperforms existing derivative-free optimization algorithms on a large collection of problems.

Time: Fri 2022-09-23 15.00 - 16.00

Location: seminar room 3418

Video link: Zoom room 63658381373

Language: English

Participating: Nick Sahinidis

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Nick Sahinidis

H. Milton Stewart School of Industrial & Systems Engineering and

School of Chemical & Biomolecular Engineering

Georgia Institute of Technology