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851. A dual decomposition approach for convex problems with linear complementarity constraints
Invited abstract in session TB-4: Topics in Mixed Integer Nonlinear Programming 1, stream MINLP.
Tuesday, 10:30-12:00Room: 1001 (building: 202)
Authors (first author is the speaker)
| 1. | Luca Mencarelli
|
| Dipartimento di Informatica, Università di Pisa | |
| 2. | Giandomenico Mastroeni
|
| Computer Science, University of Pisa |
Abstract
In this talk, we tackle general convex mathematical programs with a complementarity constraint. We introduce a novel decomposition method for this latter problem build on a sequence of convex parametrised sub-problems. Via Wolfe duality theory, we derive optimality conditions to improve the optimal value of the parametrised sub-problems and valid linear inequality cuts. Moreover, we present preliminary encouraging experimental results for quadratic convex problems with complementarity constraints and convex binary programs.
Keywords
- Programming, Nonlinear
- Programming, Mixed-Integer
- Convex Optimization
Status: accepted
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