Borszukovszki, Márton (2026) Adaptive algorithms for randomized preconditioning. Bachelor's Thesis, Applied Mathematics.
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Abstract
Large-scale linear algebra problems arise in many scientific applications, where efficiently computing the solution is often computationally challenging. The performance of iterative solvers such as CG and LSQR mainly depends on the conditioning of the problem. Randomized preconditioning techniques such as the Nyström and Sketch-and-Precondition methods have been shown to improve the convergence of iterative solvers, but their performance is highly influenced by the choice of sketch size. In this thesis adaptive algorithms for selecting an appropriate sketch size are investigated for both randomized Nyström and SAP. These adaptive strategies evaluate the accuracy of an approximation for growing sketch sizes until a prescribed stopping criterion is met. By varying the stopping criterion, the trade-off between computational cost and approximation quality is studied.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Meier, M. and Verstappen, R.W.C.P. |
| Degree programme: | Applied Mathematics |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 08 Jul 2026 13:44 |
| Last Modified: | 08 Jul 2026 13:44 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/37397 |
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