Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization

Please always quote using this URN: urn:nbn:de:0297-zib-101073
  • 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.

Download full text files

Export metadata

Metadaten
Author:Stephanie RiedmüllerORCiD, Janina ZittelORCiD, Thorsten KochORCiD
Document Type:ZIB-Report
Date of first Publication:2025/08/12
Series (Serial Number):ZIB-Report (25-12)
ISSN:1438-0064
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.