Parallel cost-aware optimization of multidimensional black-box functions

Parallel cost-aware optimization of multidimensional black-box functions

Sihlovec, Oliver (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Algorithmics)

Spaan, M.T.J. (mentor)
de Vries, J.A. (mentor)
Lofi, C. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-05-28

Scientific problems are often concerned with optimization of control variables of complex systems, for instance hyperparameters of machine learning models. A popular solution for such intractable environments is Bayesian optimization. However, many implementations disregard dynamic evaluation costs associated with the optimization procedure. Furthermore, another common trope among
Bayesian algorithms is that they are short-sighted and do not consider long-term effects of their actions. This paper investigates the viability of multitimestep cost-aware Bayesian optimizers and evaluates their performance in environments with delayed rewards. To this end, we combine existing works on parallel Bayesian optimizers and costaware heuristics. Our findings reveal that although
such parallel optimizers yield more optimal results and are more resistant to delayed feedback compared to their myopic counterparts, they are unable to achieve cost-awareness.

Bayesian Optimization
Machine learning
Multi Objective Optimisation
Parallelization
Cost-awareness

http://resolver.tudelft.nl/uuid:90723fc9-e862-448d-aa7c-9ef8795c35a7

https://github.com/osihlovec/ca-qei This link redirects to the student's code repository containing the code, which has been utilized throughout the research and which can be used to reproduce the results outlined in the paper.

Student theses

bachelor thesis

© 2023 Oliver Sihlovec