2190. Warm-Starting Strategies in Scalarization Methods for Multi-Objective Optimization
Invited abstract in session FA-5: Multiobjective Optimization 4: Complex Systems, Scalarizations, and Related Problems, stream Decision Theory and Multi-criteria Decision Making.
Friday, 8:45-10:15Room: H7
Authors (first author is the speaker)
| 1. | Stephanie Riedmüller
|
| Applied Algorithmic Intelligence Methods, Zuse Institute Berlin | |
| 2. | Janina Zittel
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| Applied Algorithmic Intelligence Methods Department, Zuse Institute Berlin | |
| 3. | Thorsten Koch
|
| Applied Algorithmic Intelligence Methods, ZIB / TU Berlin |
Abstract
We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in mathematical programming. Scalarization methods - particularly the weighted sum and epsilon-constraint methods - remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. Scalarization methods are not only used as standalone tools but also serve as the foundation for more advanced and specialized algorithms. While warm-starting has been applied in this context before, a systematic methodology and analysis remain lacking. We address this gap by providing a theoretical characterization of warm-starting within scalarization methods. Central to this analysis is the sequencing of subproblems, which significantly influences warm-start efficiency. However, optimizing the order of subproblems to maximize warm-start efficiency can conflict with alternative criteria, such as the early identification of infeasible regions. We quantify these trade-offs through an extensive computational study.
Keywords
- Multi-Objective Programming
Status: accepted
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