Understanding non-convex optimization problems and stochastic optimization algorithms
This thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different mach...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1836 |
| Acesso em linha: | http://hdl.handle.net/20.500.11824/1836 http://hdl.handle.net/10810/66155 |
| Access Level: | acceso abierto |
| Palavra-chave: | artificial intelligence informatics |
| Resumo: | This thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different machines, ensuring an equitable allocation of computational resources. Additionally, a methodology based on stochastic dominance is introduced to compare the performance of optimisation algorithms as random variables. Furthermore, the relationship between Hamming distance and the quadratic assignment problem is analysed. A general early stopping method for learning policies in episodic problems, which does not require specific problem information, is developed. In summary, this thesis contributes to the understanding and improvement of iterative stochastic heuristics in the field of optimisation. |
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