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Cardinality-Constrained Multi-objective Optimization: Novel Optimality Conditions and Algorithms

Author

Listed:
  • Matteo Lapucci

    (University of Florence)

  • Pierluigi Mansueto

    (University of Florence)

Abstract

In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous necessary optimality condition for sparse single-objective optimization, we define the concept of L-stationarity and we analyze its relationships with other existing conditions and Pareto optimality concepts. We then propose two novel algorithmic approaches: the first one is an iterative hard thresholding method aiming to find a single L-stationary solution, while the second one is a two-stage algorithm designed to construct an approximation of the whole Pareto front. Both methods are characterized by theoretical properties of convergence to points satisfying necessary conditions for Pareto optimality. Moreover, we report numerical results establishing the practical effectiveness of the proposed methodologies.

Suggested Citation

  • Matteo Lapucci & Pierluigi Mansueto, 2024. "Cardinality-Constrained Multi-objective Optimization: Novel Optimality Conditions and Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 201(1), pages 323-351, April.
    • Handle: RePEc:spr:joptap:v:201:y:2024:i:1:d:10.1007_s10957-024-02397-3
    DOI: 10.1007/s10957-024-02397-3
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    References listed on IDEAS

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    1. Christian Kanzow & Matteo Lapucci, 2023. "Inexact penalty decomposition methods for optimization problems with geometric constraints," Computational Optimization and Applications, Springer, vol. 85(3), pages 937-971, July.
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