Zeijden, Jesse van der (2025) A Hybrid Method for Numerical Optimization. Bachelor's Thesis, Mathematics.
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Abstract
Numerical optimization is concerned with minimizing or maximizing non-differentiable, real functions. Two very commonly used derivative-free minimization methods are the so-called Nelder-Mead method (NM) and Particle Swarm Optimization (PSO). This thesis examines a hybrid of these methods (NM-PSO) which aims to combine both of their strengths. To assess their performance, these methods are used to minimize the run-time of two different linear system solvers (SOR and preconditioned GMRES). These solvers use parameters that need to be chosen beforehand and a good choice can cause the algorithms to require significantly less computing time. The results of these tests suggest that NM-PSO is indeed just as accurate as PSO, while being considerably cheaper in terms of computational cost.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Luppes, R. and Sterk, A.E. |
| Degree programme: | Mathematics |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 06 Feb 2025 08:54 |
| Last Modified: | 06 Feb 2025 08:54 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/34694 |
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