- Title
- Derivative-free hybrid methods in global optimization and their applications
- Creator
- Zhang, Jiapu
- Date
- 2005
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34054
- Identifier
- vital:3522
- Identifier
- https://library.federation.edu.au/search/a?searchtype=t&searcharg=Derivative-free+hybrid+methods+in+global+optimization+and+their+&searchscope=4&SORT=D
- Abstract
- In recent years large-scale global optimization (GO) problems have drawn considerable attention. These problems have many applications, in particular in data mining and biochemistry. Numerical methods for GO are often very time consuming and could not be applied for high-dimensional non-convex and / or non-smooth optimization problems. The thesis explores reasons why we need to develop and study new algorithms for solving large-scale GO problems .... The thesis presents several derivative-free hybrid methods for large scale GO problems. These methods do not guarantee the calculation of a global solution; however, results of numerical experiments presented in this thesis demonstrate that they, as a rule, calculate a solution which is a global one or close to it. Their applications to data mining problems and the protein folding problem are demonstrated.; Doctor of Philosophy
- Publisher
- University of Ballarat
- Rights
- Copyright Jiapu Zhang
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Cluster theory; Data mining; Mathematical optimization; Microclusters; Australian Digital Thesis
- Full Text
- Hits: 4650
- Visitors: 4467
- Downloads: 1063
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Australian Digital Thesis | 4 MB | Adobe Acrobat PDF | View Details Download |