Streaming algorithms for maximizing the difference of submodular functions and the sum of submodular and supermodular functions-Reference-Cited by-同舟云学术

Streaming algorithms for maximizing the difference of submodular functions and the sum of submodular and supermodular functions

Published:2023-02-23 Issue:7 Volume:17 Page:1643-1667
ISSN:1862-4472
Container-title:Optimization Letters
language:en
Short-container-title:Optim Lett

Author:

Lu Cheng,Yang Wenguo,Gao Suixiang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Business, Management and Accounting (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s11590-023-01979-w.pdf

Reference38 articles.

1. Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 671–680 (2014)

2. Bai, W., Bilmes, J.: Greed is still good: maximizing monotone submodular+ supermodular (bp) functions. In: International Conference on Machine Learning, pp. 304–313. PMLR (2018)

3. Bai, W., Iyer, R., Wei, K., Bilmes, J.: Algorithms for optimizing the ratio of submodular functions. In: International Conference on Machine Learning, pp. 2751–2759. PMLR (2016)

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

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