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The Multi-Objective Polynomial Optimization

Author

Listed:
  • Jiawang Nie

    (Department of Mathematics, University of California, San Diego, La Jolla, California 92093)

  • Zi Yang

    (Department of Mathematics and Statistics, University at Albany, State University of New York, Albany, New York 12222)

Abstract

The multi-objective optimization is to optimize several objective functions over a common feasible set. Because the objectives usually do not share a common optimizer, people often consider (weakly) Pareto points. This paper studies multi-objective optimization problems that are given by polynomial functions. First, we study the geometry for (weakly) Pareto values and represent Pareto front as the boundary of a convex set. Linear scalarization problems (LSPs) and Chebyshev scalarization problems (CSPs) are typical approaches for getting (weakly) Pareto points. For LSPs, we show how to use tight relaxations to solve them and how to detect existence or nonexistence of proper weights. For CSPs, we show how to solve them by moment relaxations. Moreover, we show how to check whether a given point is a (weakly) Pareto point or not and how to detect existence or nonexistence of (weakly) Pareto points. We also study how to detect unboundedness of polynomial optimization, which is used to detect nonexistence of proper weights or (weakly) Pareto points.

Suggested Citation

  • Jiawang Nie & Zi Yang, 2024. "The Multi-Objective Polynomial Optimization," Mathematics of Operations Research, INFORMS, vol. 49(4), pages 2723-2748, November.
    • Handle: RePEc:inm:ormoor:v:49:y:2024:i:4:p:2723-2748
    DOI: 10.1287/moor.2023.0200
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