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4225. A global optimization method for a quadratic reverse convex programming problem
Invited abstract in session WA-41: Convex optimization algorithms, stream Nonsmooth Optimization.
Wednesday, 8:30-10:00Room: 97 (building: 306)
Authors (first author is the speaker)
| 1. | Syuuji Yamada
|
| Faculty of Science, Niigata University |
Abstract
In this talk, we propose a global optimization method for a quadratic reverse convex programming problem (QRC) whose feasible set is expressed as the area excluded the interior of a convex set from another convex set. It is known that many global optimization problems can be transformed into such a problem. Iterative solution methods for solving (QRC) have been proposed by many other researchers. One of the difficulty for solving (QRC) is that all locally optimal solutions do not always satisfy KKT conditions. In order to overcome this drawback, we introduce a procedure by combining parametric optimization techniques and Lipschitz optimization methods for finding locally optimal solutions of (QRC). Moreover, we propose an algorithm for finding a globally optimal solution of (QRC) by incorporating such a procedure into a branch and bound procedure.
Keywords
- Global Optimization
- Programming, Nonlinear
- Programming, Quadratic
Status: accepted
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