Submodular optimization with submodular cover and submodular knapsack constraints

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Iyer, Rishabh ^a   Bilmes, Jeff ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS;

APPROXIMATION FACTOR; COOPERATIVE COST; HARDNESS RESULT; KNAPSACK CONSTRAINTS; SENSOR PLACEMENT; SUBMODULAR FUNCTIONS; SUBMODULAR OPTIMIZATIONS; UPPER BOUND CONSTRAINTS;

CONSTRAINED OPTIMIZATION;

EID: 84898975039     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. A. Atamtürk and V. Narayanan. The submodular knapsack polytope. Discrete Optimization, 2009.
2. M. Conforti and G. Cornuejols. Submodular set functions, matroids and the greedy algorithm: tight worstcase bounds and some generalizations of the Rado-Edmonds theorem. Discrete Applied Mathematics, 7(3):251-274, 1984.
3. U. Feige. A threshold of ln n for approximating set cover. Journal of the ACM (JACM), 1998.
4. S. Fujishige. Submodular functions and optimization, volume 58. Elsevier Science, 2005.
5. J. Garofolo, F. Lamel, L., J. W., Fiscus, D. Pallet, and N. Dahlgren. Timit, acoustic-phonetic continuous speech corpus. In DARPA, 1993.
6. M. Goemans, N. Harvey, S. Iwata, and V. Mirrokni. Approximating submodular functions everywhere. In SODA, pages 535-544, 2009.
7. A. Guillory and J. Bilmes. Interactive submodular set cover. In ICML, 2010.
8. A. Guillory and J. Bilmes. Simultaneous learning and covering with adversarial noise. In ICML, 2011.
9. R. Iyer and J. Bilmes. Algorithms for approximate minimization of the difference between submodular functions, with applications. In UAI, 2012.
10. R. Iyer and J. Bilmes. The submodular Bregman and Lovász-Bregman divergences with applications. In NIPS, 2012.
11. R. Iyer and J. Bilmes. Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints: Extended arxiv version, 2013.
12. R. Iyer, S. Jegelka, and J. Bilmes. Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions.In NIPS, 2013.
13. R. Iyer, S. Jegelka, and J. Bilmes. Fast semidifferential based submodular function optimization. In ICML, 2013.
14. S. Jegelka and J. A. Bilmes. Submodularity beyond submodular energies: coupling edges in graph cuts. In CVPR, 2011.
15. Y. Kawahara and T. Washio. Prismatic algorithm for discrete dc programming problems. In NIPS, 2011.
16. H. Kellerer, U. Pferschy, and D. Pisinger. Knapsack problems. Springer Verlag, 2004.
17. A. Krause and C. Guestrin. A note on the budgeted maximization on submodular functions. Technical Report CMU-CALD-05-103, Carnegie Mellon University, 2005.
18. A. Krause, B. McMahan, C. Guestrin, and A. Gupta. Robust submodular observation selection. Journal of Machine Learning Research (JMLR), 9:2761-2801, 2008.
19. A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. JMLR, 9:235-284, 2008.
20. H. Lin and J. Bilmes. How to select a good training-data subset for transcription: Submodular active selection for sequences. In Interspeech, 2009.
21. H. Lin and J. Bilmes. Multi-document summarization via budgeted maximization of submodular functions. In NAACL, 2010.
22. H. Lin and J. Bilmes. A class of submodular functions for document summarization. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT-2011), Portland, OR, June 2011.
23. H. Lin and J. Bilmes. Optimal selection of limited vocabulary speech corpora. In Interspeech, 2011.
24. R. C. Moore and W. Lewis. Intelligent selection of language model training data. In Proceedings of the ACL 2010 Conference Short Papers, pages 220-224. Association for Computational Linguistics, 2010.
25. M. Narasimhan and J. Bilmes. A submodular-supermodular procedure with applications to discriminative structure learning. In UAI, 2005.
26. E. Nikolova. Approximation algorithms for offline risk-averse combinatorial optimization, 2010.
27. J. Rousu and J. Shawe-Taylor. Efficient computation of gapped substring kernels on large alphabets. Journal of Machine Learning Research, 6(2):1323, 2006.
28. M. Sviridenko. A note on maximizing a submodular set function subject to a knapsack constraint. Operations Research Letters, 32(1):41-43, 2004.
29. L. A.Wolsey. An analysis of the greedy algorithm for the submodular set covering problem. Combinatorica, 2(4):385-393, 1982.