Improving the efficiency of DC global optimization methods by improving the DC representation of the objective function
There are infinitely many ways of representing a d.c. function as a difference of convex functions. In this paper we analyze how the computational efficiency of a d.c. optimization algorithm depends on the representation we choose for the objective function, and we address the problem of characteriz...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/1954 |
| Acceso en línea: | https://hdl.handle.net/2117/1954 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical programming dc representation branch and bound outer approximation dc program semi-infinite program Programació (Matemàtica) Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming |
| Sumario: | There are infinitely many ways of representing a d.c. function as a difference of convex functions. In this paper we analyze how the computational efficiency of a d.c. optimization algorithm depends on the representation we choose for the objective function, and we address the problem of characterizing and obtaining a computationally optimal representation. We introduce some theoretical concepts which are necessary for this analysis and report some numerical experiments. |
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