Talk by Davide Previtali (UniBG) on "Surrogate-based methods for black-box and preference-based optimization" Friday May 6 at 10:30
by IDSIA Announcements of talks＠IDSIA
(On behalf of Loris Roveda)
Dear all,
Friday May 6 at 10:30 in room B1.11 (Campus Est) we will have a seminar "Surrogate-based methods for black-box and preference-based optimization: theory and a controller tuning case study" from Davide Previtali (Università di Bergamo).
Please find the abstract and biography below.
See you there,
Loris
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TITLE: Surrogate-based methods for black-box and preference-based optimization: theory and a controller tuning case study
ABSTRACT: Many engineering and machine learning applications require the calibration of some parameters. For example, in the context of control systems, there is often the need to tune the controller parameters to achieve adequate closed-loop performances. Quite often, the tuning is found by minimizing a suitable performance indicator, such as the settling time of the closed-loop step response or the Integral Time Absolute Error (ITAE). Usually, the mathematical relationship between the performance indicator and the controller parameters is unknown; instead, the metric can only be computed from the signals obtained by performing experiments on the system. Further complications arise if no performance indicator is available and, instead, the quality of a tuning is assessed by a decision-maker, who compares different calibrations for the controller parameters and chooses the one which he/she prefers the most.
In this seminar, we address how to solve optimization problems where (i) no analytical formulation of the objective function is available, but it can be measured by performing experiments, or (ii) we need to find the optimal parameters’ calibration based only on the preferences expressed by a decision-maker. The former case is referred to as black-box optimization (BBO) while the latter is called preference-based optimization (PBO). We show how BBO and PBO problems can be solved using surrogate-based optimization algorithms and address their convergence to the global optimizer. Finally, we present a case study where we apply black-box and preference-based optimization to tune the controller of a forming press.
BIO: Davide Previtali was born in Bergamo, Italy in 1995. He received his M. Sc. degree cum laude in Computer Science Engineering from Università degli Studi di Bergamo (Italy) in 2019. Since October 2019, he is a a Ph.D student in Engineering and Applied Sciences at the Control systems and Automation Laboratory of Università degli Studi di Bergamo (CAL UNIBG). His research interests include black-box and preference-based optimization as well as the application of adaptive control schemes in the industrial setting.
1 year, 12 months
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