Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
under review
- We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization 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. 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, focusing on the sequencing of subproblems. However, optimizing the order of subproblems to maximize warm-start efficiency may conflict with alternative criteria, such as early identification of infeasible regions. We quantify these trade-offs through an extensive computational study.
| Author: | Stephanie RiedmüllerORCiD, Janina ZittelORCiD, Thorsten KochORCiD |
|---|---|
| Document Type: | In Proceedings |
| Parent Title (English): | Operations Research Proceedings 2025 |
| Year of first publication: | 2025 |
| ArXiv Id: | http://arxiv.org/abs/2507.21933 |
| Preprint: | urn:nbn:de:0297-zib-101073 |

