Numerical Analysis in Nonlinear Least Squares Methods and Applications
| dc.contributor.author | Eu, Christina Nguk Ling | |
| dc.contributor.supervisor | Prof. Bean San Goh | en_US |
| dc.date.accessioned | 2018-11-19T07:45:48Z | |
| dc.date.available | 2018-11-19T07:45:48Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/70491 | |
| dc.description.abstract |
The approximate greatest descent (AGD) method and a two-phase AGD method (AGDN) are proposed as new methods for a nonlinear least squares problem. Numerical experiments show that these methods outperform existing methods including the Levenberg-Marquardt method. However, the AGDN method outperforms the AGD method with a faster convergence. If the AGDN method fails due to singularity of the Hessian matrix, the AGD method should be used. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Numerical Analysis in Nonlinear Least Squares Methods and Applications | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | MPhil | en_US |
| curtin.department | Electrical and Computer Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Engineering and Science (Sarawak) | en_US |