Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916868968677376 |
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| author | Riedmüller, Stephanie Zittel, Janina Koch, Thorsten |
| author_facet | Riedmüller, Stephanie Zittel, Janina Koch, Thorsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization Riedmüller, Stephanie Zittel, Janina Koch, Thorsten Optimization and Control 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. |
| title | Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2507.21933 |