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Non-submodular Optimization and Non-convex Relaxation

In: Theory, Algorithms, and Experiments in Applied Optimization

Author

Listed:
  • Weili Wu

    (University of Texas at Dallas)

  • Zhao Zhang

    (Zhejiang Normal University)

  • Wei Li

    (Texas Southern University)

  • Ding-Zhu Du

    (University of Texas at Dallas)

Abstract

Usually, non-submodular optimization problems are NP-hard. Therefore, design and analysis of approximation algorithms are important tasks in the study of non-submodular optimizations. However, the traditional methods do not work well. In this article, we give an extensive survey for recent developments in this research direction.

Suggested Citation

  • Weili Wu & Zhao Zhang & Wei Li & Ding-Zhu Du, 2025. "Non-submodular Optimization and Non-convex Relaxation," Springer Optimization and Its Applications, in: Boris Goldengorin (ed.), Theory, Algorithms, and Experiments in Applied Optimization, pages 377-391, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-91357-0_17
    DOI: 10.1007/978-3-031-91357-0_17
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