 |
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| Volume 3, Number 2, May 2007, pp. 387-397 | ||||||||||
| Julien Ugon | ||||||||||
| Key words: | ||||||||||
| global optimization, nonconvex, relaxation | ||||||||||
| Mathematices Subject Classification: 49M20, 49M37, 65K05, 90C26, 90C30, 90C56 | ||||||||||
| References | ||||||||||
|
||||||||||
| Volume 3, Number 2, May 2007, pp. 387-397 | ||||||||||
| Julien Ugon | ||||||||||
| Key words: | ||||||||||
| global optimization, nonconvex, relaxation | ||||||||||
| Mathematices Subject Classification: 49M20, 49M37, 65K05, 90C26, 90C30, 90C56 | ||||||||||
| References | ||||||||||
|
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| Abstract: | ||||
| A popular approach for solving complex optimization problems is through relaxation: some constraints are removed in order to have a convex problem approximating the original problem. On the other hand, direct approaches for solving such problems are becoming increasingly powerful. This paper examines two cases drawn from data analysis, in order to compare the two techniques. | ||||
| Optimization solvers and problem formulations for solving data clustering problems | ||