Derivative free optimization methods
| dc.contributor.advisor | Mitiku, Semu (PhD) | |
| dc.contributor.author | Yohannes, Flagot | |
| dc.date.accessioned | 2018-07-13T07:29:06Z | |
| dc.date.accessioned | 2023-11-04T12:32:32Z | |
| dc.date.available | 2018-07-13T07:29:06Z | |
| dc.date.available | 2023-11-04T12:32:32Z | |
| dc.date.issued | 2012-01 | |
| dc.description.abstract | Let be a continuous function on, and suppose is a smooth nonlinear function. Such functions arise in many applications, and very often minimizers are points at whichis not differentiable. Of particular interest is the case where the gradient and the Hessian cannot be computed for any . I present a practical, robust algorithm to locally minimize such functions, based on model sampling or search. No derivatives information is required by the algorithm | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/8495 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Derivative free optimization methods | en_US |
| dc.title | Derivative free optimization methods | en_US |
| dc.type | Thesis | en_US |