Volume 40, Number 3, July-September 2006
|285 - 302
|08 November 2006
Convex quadratic underestimation and Branch and Bound for univariate global optimization with one nonconvex constraint
Laboratoire de l'Informatique Théorique et Appliquée, UFR
Université Paul Verlaine – Metz, Ile du Saulcy, 57045 Metz, France; email@example.com
2 Département de Mathématiques, Faculté des Sciences, Université de Tizi-Ouzou, Algeria.
Accepted: 6 April 2006
The purpose of this paper is to demonstrate that, for globally minimize one dimensional nonconvex problems with both twice differentiable function and constraint, we can propose an efficient algorithm based on Branch and Bound techniques. The method is first displayed in the simple case with an interval constraint. The extension is displayed afterwards to the general case with an additional nonconvex twice differentiable constraint. A quadratic bounding function which is better than the well known linear underestimator is proposed while w-subdivision is added to support the branching procedure. Computational results on several and various types of functions show the efficiency of our algorithms and their superiority with respect to the existing methods.
Key words: Global optimization / branch and bound / quadratic underestimation / w-subdivision.
© EDP Sciences, 2006
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.