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Submodular Maximization with Bounded Marginal Values

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12606))

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Abstract

We study the problem of maximizing non-monotone submodular functions subject to a p-independence system constraint. Although the submodularity ratio has been well-studied in maximizing set functions under monotonic scenario, the defined parameter may bring hardness of approximation for the maximization of set functions in the non-monotonic case. In this work, utilizing a lower bound for the marginal values, we investigate the Repeated Greedy introduced by (Feldman et al. 2017) and obtain a parameterized performance guarantee for the above constrained submodular maximization problem.

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References

  1. Bai, W., Bilmes, J.: Greed is still good: maximizing monotone submodular \(+\) supermodular (BP) functions. In: Proceedings of the 35th International Conference on Machine Learning, pp. 314–323 (2018)

    Google Scholar 

  2. Bian, A.A., Buhmann, J.M., Krause, A., Tschiatschek, S.: Guarantees for greedy maximization of non-submodular functions with applications. In: Proceedings of the 34th International Conference on Machine Learning 498–507 (2017)

    Google Scholar 

  3. Bogunovic, I., Zhao, J., Cevher, V.: Robust maximization of non-submodular objectives. In: Proceedings of the 21th International Conference on Artificial Intelligence and Statistics, pp. 890–899 (2018)

    Google Scholar 

  4. Buchbinder, N., Feldman, M.: Deterministic algorithms for submodular maximization problems. ACM Trans. Algorithms 14(3), 32 (2018)

    Article  MathSciNet  Google Scholar 

  5. Buchbinder, N., Feldman, M.: Constrained submodular maximization via a non-symmetric technique. Math. Oper. Res. 44(3), 988–1005 (2019)

    Article  MathSciNet  Google Scholar 

  6. Buchbinder, N., Feldman, M., Seffi, J., Schwartz, R.: A tight linear time \(1/2\)-approximation for unconstrained submodular maximization. SIAM J. Comput. 44(5), 1384–1402 (2015)

    Article  MathSciNet  Google Scholar 

  7. Conforti, M., Cornuéjols, G.: Submodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the Rado-Edmonds theorem. Discrete Appl. Math. 7(3), 251–274 (1984)

    Article  MathSciNet  Google Scholar 

  8. Das, A., Kempe, D.: Submodular meets spectral: greedy algorithms for subset selection, sparse approximation and dictionary selection. In: Proceedings of the 28th International Conference on Machine Learning, pp. 1057–1064 (2011)

    Google Scholar 

  9. Feige, U.: A threshold of \(\ln n\) for approximating set cover. J. ACM 45(4), 634–652 (1998)

    Article  MathSciNet  Google Scholar 

  10. Feige, U., Izsak, R.: Welfare maximization and the supermodular degree. In: Proceedings of the 4th Conference on Innovations in Theoretical Computer Science, pp. 247–256 (2013)

    Google Scholar 

  11. Feige, U., Mirrokni, V.S., Vondrák, J.: Maximizing non-monotone submodular functions. SIAM J. Comput. 40(4), 1133–1153 (2011)

    Article  MathSciNet  Google Scholar 

  12. Feldman, M., Harshaw, C., Karbasi, A.: Greed is good: near-optimal submodular maximization via greedy optimization. In: Proceedings of the 30th Annual Conference on Learning Theory, pp. 758–784 (2017)

    Google Scholar 

  13. Feldman, M., Izsak, R.: Constrained monotone function maximization and the supermodular degree. In: Proceedings of the 17th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and the 18th International Workshop on Randomization and Computation, pp. 160–175 (2014)

    Google Scholar 

  14. G\(\ddot{\rm o}\)lz, P., Procaccia, A.D.: Migration as submodular optimization. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 549–556 (2019)

    Google Scholar 

  15. Gong, S., Nong, Q., Liu, W., Fang, Q.: Parametric monotone function maximization with matroid constraints. J. Global Optim. 75(3), 833–849 (2019). https://doi.org/10.1007/s10898-019-00800-2

    Article  MathSciNet  MATH  Google Scholar 

  16. Gupta, A., Roth, A., Schoenebeck, G., Talwar, K.: Constrained non-monotone submodular maximization: Offline and secretary algorithms. In: Proceedings of the 6th International Conference on Internet and Network Economics, pp. 246–257 (2010)

    Google Scholar 

  17. Han, A., Cao, Z., Cui, S., Wu, B.: Deterministic approximation for submodular maximization over a matroid in nearly linear time, 2020, to appear in NeurIPS

    Google Scholar 

  18. Hassidim, A., Singer, Y.: Optimization for approximate submodularity. In: Proceedings of the 32nd Annual Conference on Neural Information Processing Systems, pp. 396–407 (2018)

    Google Scholar 

  19. Horel, T., Singer, Y.: Maximization of approximately submodular functions. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3053–3061 (2016)

    Google Scholar 

  20. Jiang, Y., Wang, Y., Xu, D., Yang, R., Zhang, Y.: Streaming algorithm for maximizing a monotone non-submodular function under \(d\)-knapsack constraint. Optim. Lett. 13(82), 1–14 (2019)

    MATH  Google Scholar 

  21. Kuhnle, A., Smith, J.D., Crawford, V.G., Thai, M.T.: Fast maximization of non-submodular, monotonic functions on the integer lattice. In: Proceedings of the 35th International Conference on Machine Learning 2791–2800 (2018)

    Google Scholar 

  22. Lee, J., Sviridenko, M., Vondrák, J.: Submodular maximization over multiple matroids via generalized exchange properties. Math. Oper. Res. 35(4), 795–806 (2010)

    Article  MathSciNet  Google Scholar 

  23. Mirzasoleiman, B., Badanidiyuru, A., Karbasi, A.: Fast constrained submodular maximization: personalized data summarization. In: Proceedings of the 33rd International Conference on Machine Learning, pp. 1358–1366 (2016)

    Google Scholar 

  24. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-I. Math. Program. 14(1), 265–294 (1978)

    Article  MathSciNet  Google Scholar 

  25. Qian, C., Shi, J., Yu, Y., Tang, K., Zhou, Z.: Subset selection under noise. In: Proceedings of the 31st Annual Conference on Neural Information Processing Systems, pp. 3560–3570 (2017)

    Google Scholar 

  26. Sviridenko, M., Vondrák, J., Ward, J.: Optimal approximation for submodular and supermodular optimization with bounded curvature. Math. Oper. Res. 42(4), 1197–1218 (2017)

    Article  MathSciNet  Google Scholar 

  27. Wang, Z., Moran, B., Wang, X., Pan, Q.: Approximation for maximizing monotone non-decreasing set functions with a greedy method. J. Combin. Optim. 31(1), 29–43 (2014). https://doi.org/10.1007/s10878-014-9707-3

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The third author is supported by Scientific Research Project of Beijing Municipal Education Commission (No. KM201910005012) and the National Natural Science Foundation of China (No. 11971046). The fourth author is supported by the National Natural Science Foundation of China (No. 11871081).

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Correspondence to Changjun Wang .

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Yang, R., Gao, S., Wang, C., Zhang, D. (2021). Submodular Maximization with Bounded Marginal Values. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-69244-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69243-8

  • Online ISBN: 978-3-030-69244-5

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