534. Parametrized convex MINLP: Warm-starting with Outer Approximation for Sequence of MINLPs
Invited abstract in session WC-10: Computational Aspects in Multiobjective Optimization, stream Multiobjective and Vector Optimization.
Wednesday, 14:00-16:00Room: B100/8011
Authors (first author is the speaker)
| 1. | Erik Tamm
|
| Department of Mathematics, KTH Royal Institute of Technology | |
| 2. | Gabriele Eichfelder
|
| Institute of Mathematics, Technische Universität Ilmenau | |
| 3. | Jan Kronqvist
|
| Mathematics, KTH Royal Institute of Technology |
Abstract
This work addresses the challenge of efficiently solving parametrized sequences of convex Mixed-Integer Nonlinear Programming (MINLP) problems through warm-starting techniques. While solving such sequences is known to be difficult, the general case of convex MINLPs has received limited attention in the literature. Our research introduces warm-starting for the outer approximation algorithm and explores its potential to enhance computational efficiency. This approach is particularly relevant for applications involving multiobjective MINLPs using scalarization techniques.
The main theoretical contribution of this paper is the identification of conditions under which warm-starting significantly improves the performance of solving these problem sequences. We support our theoretical findings with the implementation of three proposed warm-starting rules, demonstrating noticeable performance improvements. Our methods are especially effective for problems where the optimal integer part remains optimal for several problems in the sequence.
These results highlight the potential for further research to enhance the computational efficiency of solving parametrized convex MINLPs by developing warm-starting techniques.
Keywords
- Nonlinear mixed integer optimization
- Multi-objective optimization
Status: accepted
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