- Title
- Local optimization method with global multidimensional search
- Creator
- Bagirov, Adil; Rubinov, Alex; Zhang, Jiapu
- Date
- 2005
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39479
- Identifier
- vital:81
- Identifier
-
https://doi.org/10.1007/s10898-004-2700-0
- Identifier
- ISSN:0925-5001
- Abstract
- This paper presents a new method for solving global optimization problems. We use a local technique based on the notion of discrete gradients for finding a cone of descent directions and then we use a global cutting angle algorithm for finding global minimum within the intersection of the cone and the feasible region. We present results of numerical experiments with well-known test problems and with the so-called cluster function. These results confirm that the proposed algorithms allows one to find a global minimizer or at least a deep local minimizer of a function with a huge amount of shallow local minima. © Springer 2005.; C1
- Publisher
- Springer
- Relation
- Journal of Global Optimization Vol. 32, no. 2 (2005), p. 161-179
- Rights
- Centre for Informatics and Applied Optimization
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0103 Numerical and Computational Mathematics; Derivative-free optimization; Discrete gradient; Global optimization; Lipschitz programming; Cutting Angle Method; Algorithms; Functions; Gradient method; Problem solving
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
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