Sequence submodular maximization meets streaming
In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals t...
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| Published in | Journal of combinatorial optimization Vol. 41; no. 1; pp. 43 - 55 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Springer US
01.01.2021
Springer Nature B.V |
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| Abstract | In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals the utility value in selecting a single element and the weight of an edge reveals the additional profit with respect to a certain selection sequence. The edges are visited in a streaming fashion and the aim is to sieve a sequence of at most
k
elements from the stream, such that the utility is maximized. In this work, we present an edge-based threshold procedure, which makes one pass over the stream, attains an approximation ratio of
(
1
/
(
2
Δ
+
1
)
-
O
(
ϵ
)
)
, consumes
O
(
k
Δ
/
ϵ
)
memory source in total and
O
(
log
(
k
Δ
)
/
ϵ
)
update time per edge, where
Δ
is the minimum of the maximal outdegree and indegree of the directed graph. |
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| AbstractList | In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals the utility value in selecting a single element and the weight of an edge reveals the additional profit with respect to a certain selection sequence. The edges are visited in a streaming fashion and the aim is to sieve a sequence of at most k elements from the stream, such that the utility is maximized. In this work, we present an edge-based threshold procedure, which makes one pass over the stream, attains an approximation ratio of (1/(2Δ+1)-O(ϵ)), consumes O(kΔ/ϵ) memory source in total and O(log(kΔ)/ϵ) update time per edge, where Δ is the minimum of the maximal outdegree and indegree of the directed graph. In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals the utility value in selecting a single element and the weight of an edge reveals the additional profit with respect to a certain selection sequence. The edges are visited in a streaming fashion and the aim is to sieve a sequence of at most k elements from the stream, such that the utility is maximized. In this work, we present an edge-based threshold procedure, which makes one pass over the stream, attains an approximation ratio of ( 1 / ( 2 Δ + 1 ) - O ( ϵ ) ) , consumes O ( k Δ / ϵ ) memory source in total and O ( log ( k Δ ) / ϵ ) update time per edge, where Δ is the minimum of the maximal outdegree and indegree of the directed graph. |
| Author | Zhang, Dongmei Guo, Longkun Yang, Ruiqi Xu, Dachuan |
| Author_xml | – sequence: 1 givenname: Ruiqi surname: Yang fullname: Yang, Ruiqi organization: Department of Operations Research and Information Engineering, Beijing University of Technology, School of Mathematical Sciences, University of Chinese Academy Sciences – sequence: 2 givenname: Dachuan surname: Xu fullname: Xu, Dachuan organization: Department of Operations Research and Information Engineering, Beijing University of Technology – sequence: 3 givenname: Longkun orcidid: 0000-0003-2891-4253 surname: Guo fullname: Guo, Longkun email: longkun.guo@gmail.com organization: Shandong Key Laboratory of Computer Networks, School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) – sequence: 4 givenname: Dongmei surname: Zhang fullname: Zhang, Dongmei organization: School of Computer Science and Technology, Shandong Jianzhu University |
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| Cites_doi | 10.1016/0166-218X(84)90003-9 10.1145/2422436.2422466 10.1142/S0217595919500222 10.1137/1.9781611973730.80 10.1007/s10107-015-0900-7 10.1007/978-3-642-22006-7_29 10.1007/s40305-019-00244-1 10.1109/FOCS.2011.46 10.1145/2623330.2623637 10.1007/s11590-019-01430-z 10.1007/BF01588971 10.1016/S0167-6377(03)00062-2 10.1007/978-3-030-26176-4_51 10.1137/130929205 10.1007/s10898-019-00840-8 10.1137/080733991 10.1145/285055.285059 10.1109/FOCS.2012.55 10.1007/978-3-662-47672-7_26 10.1137/090779346 10.1137/090750020 10.1007/s40305-014-0053-z 10.1137/1.9781611973082.83 10.24963/ijcai.2018/379 10.1137/1.9781611975482.16 10.1137/1.9781611973730.76 10.1109/FOCS.2016.34 10.1007/s40305-018-0233-3 10.1007/s10878-014-9707-3 10.1007/978-3-642-17572-5_20 10.1007/978-3-030-36412-0_46 |
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| Keywords | Streaming algorithm Sequence Submodular maximization Threshold |
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| References | Chekuri C, Gupta S, Quanrud K (2015) Streaming algorithms for submodular function maximization. In Proceedings of the 42nd International Colloquium on Automata, Languages and Programming, pp 318–330 CalinescuGChekuriCPalMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J Comput201140617401766286319310.1137/080733991 Mitrovic M, Kazemi E, Feldman M, Krause A, Karbasi A (2019) Adaptive sequence submodularity. ArXiv:1902.05981 WangYXuDJiangYZhangDMinimizing ratio of monotone non-submodular functionsJ Oper Res Soc China201973449459400811110.1007/s40305-019-00244-1 ConfortiMCornuéjolsGSubmodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the Rado-Edmonds theoremDiscrete Appl Math19847325127473689010.1016/0166-218X(84)90003-9 WuWZhangZDuDSet function optimizationJ Oper Res Soc China201972183193395703910.1007/s40305-018-0233-3 Gharan SO, Vondrák J (2011) Submodular maximization by simulated annealing. In Proceedings of the 22nd Annual ACM-SIAM symposium on discrete algorithms, pp 1098–1116 Gupta A, Roth A, Schoenebeck G, Talwar K (2010) Constrained non-monotone submodular maximization: offline and secretary algorithms. In Proceedings of the 6th international conference on internet and network economics, pp 246–257 Norouzi-Fard A, Tarnawski J, Mitrović S, Zandieh A, Mousavifar A, Svensson O (2018) Beyond 1/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/2$$\end{document}-approximation for submodular maximization on massive data streams. In Proceedings of the 35th international conference on machine learning, pp 3826–3835 Tschiatschek S, Singla A, Krause A (2017) Selecting sequences of items via submodular maximization. In: Proceedings of the 31st AAAI conference on artificial intelligence, pp 2667–2673 Buchbinder N, Feldman M, Garg M (2019) Deterministic (1/2+ϵ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/2+\epsilon )$$\end{document}-approximation for submodular maximization over a matroid. In Proceedings of the 30th Annual ACM-SIAM symposium on discrete algorithms, pp 241–254 Haba R, Kazemi E, Feldman F, Karbasi A (2020) Streaming submodular maximization under a k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-set system constraint. ArXiv: 2002.03352 Badanidiyuru A, Mirzasoleiman B, Karbasi A, Krause A (2014) 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 Bogunovic I, Zhao J, Cevher V (2018) Robust maximization of non-submodular objectives. In Proceedings of the 21st international conference on artificial intelligence and statistics, pp 890–899 GolovinDKrauseAAdaptive submodularity: theory and applications in active learning and stochastic optimizationJ Artif Intell Res20114214274862874807 Yang R, Xu D, Guo L, Zhang D (2019) Sequence submodular maximization meets streaming. In: Proceedings of the 13th international conference on combinatorial optimization and applications, pp 565–575 Das A, Kempe D (2011) 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 JiangYWangYXuDYangRZhangYStreaming algorithm for maximizing a monotone non-submodular function under d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}-knapsack constraintOptim Lett201910.1007/s11590-019-01430-z Feldman M, Karbasi A, Kazemi E (2018) Do less, get more: Streaming submodular maximization with subsampling. In Proceedings of the 32nd international conference on neural information processing systems, pp 730–740 Buchbinder N, Feldman M, Naor JS, Schwartz R (2014) Submodular maximization with cardinality constraints. In Proceedings of the 25th Annual ACM-SIAM symposium on discrete algorithms, pp 1433–1452 Huang CC, Kakimura N, Yoshida Y (2017) Streaming algorithms for maximizing monotone submodular functions under a knapsack constraint. In Proceedings of the 20th international workshop on approximation algorithms for combinatorial optimization problems and the 21st international workshop on randomization and computation No. 11, 11:1–11:14 Yang R, Xu D, Du D, Xu Y, Yan X (2019) Maximization of constrained non-submodular functions. In: Proceedings of the 25th international computing and combinatorics conference, pp 615–626 NemhauserGLWolseyLAFisherMLAn analysis of approximations for maximizing submodular set functions-IMath Program197814126529450386610.1007/BF01588971 Bai W, Bilmes J (2018) Greed is still good: Maximizing monotone submodular +\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document} supermodular(BP) functions. In Proceedings of the 35th international conference on machine learning, pp 314–323 SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper Res Lett20043214143201710710.1016/S0167-6377(03)00062-2 Ene A, Nguyen HL (2016) Constrained submodular maximization: Beyond 1/e\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/e$$\end{document}. In Proceedings of the 57th IEEE annual symposium on foundations of computer science, pp 248–257 WangYXuDWangYZhangDNon-submodular maximization on massive data streamsJ Glob Optim2020764729743407837610.1007/s10898-019-00840-8 YangRXuDJiangYWangYZhangDApproximating robust parameterized submodular function maximization in large-scalesAsia-Pac J Oper Res2019364671680399646910.1142/S0217595919500222 DuDLiYXiuNXuDSimultaneous approximation of multi-criteria submodular function maximizationJ Oper Res Soc China201423271290326193810.1007/s40305-014-0053-z LeeJMirrokniVSNagarajanVSviridenkoMMaximizing nonmonotone submodular functions under matroid or knapsack constraintsSIAM J Discrete Math201023420532078259497110.1137/090750020 Sviridenko M, Vondrák J, Ward J (2015) Optimal approximation for submodular and supermodular optimization with bounded curvature. In: Proceedings of the 26th Annual ACM-SIAM symposium on discrete algorithms, pp 1134–1148 Kazemi E, Mitrovic M, Zadimoghaddam M, Lattanzi S, Karbasi A (2019) Submodular streaming in all its glory: Tight approximation, minimum memory and low adaptive complexity. In Proceedings of the 36th international conference on machine learning, pp 3311–3320 BuchbinderNFeldmanMSeffiJSchwartzRA tight linear time (1/2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/2)$$\end{document}-approximation for unconstrained submodular maximizationSIAM J Comput201544513841402341614010.1137/130929205 Feldman M, Naor J, Schwartz R (2011) A unified continuous greedy algorithm for submodular maximization. In Proceedings of the 52nd IEEE annual symposium on foundations of computer science, pp 570–579 Alaluf N, Feldman M (2019) Making a sieve random: Improved semi-streaming algorithm for submodular maximization under a cardinality constraint. ArXiv: 1906.11237 ChakrabartiAKaleSSubmodular maximization meets streaming: Matchings, matroids, and moreMath Program20151541–2225247342193410.1007/s10107-015-0900-7 FeigeUMirrokniVSVondrákJMaximizing non-monotone submodular functionsSIAM J Comput201140411331153282531210.1137/090779346 WangZMoranBWangXPanQApproximation for maximizing monotone non-decreasing set functions with a greedy methodJ Comb Optim20163112943344024410.1007/s10878-014-9707-3 Bian AA, Buhmann JM, Krause A, Tschiatschek S (2017) Guarantees for greedy maximization of non-submodular functions with applications. In Proceedings of the 34th international conference on machine learning, pp 498–507 Feige U, Izsak R (2013) Welfare maximization and the supermodular degree. In Proceedings of the 4th conference on innovations in theoretical computer science, pp 247-256 Mitrovic M, Feldman M, Krause A, Karbasi A (2018) Submodularity on hypergraphs: From sets to sequences. In Proceedings of the 21st international conference on artificial intelligence and statistics, pp 1177–1184 FeigeUA threshold of lnn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ln n$$\end{document} for approximating set coverJ ACM1998454634652167509510.1145/285055.285059 Elenberg ER, Dimakis AG, Feldman M, Karbasi A (2017) Streaming weak submodularity: Interpreting neural networks on the fly. In Proceedings of the 31st international conference on neural information processing systems, pp 4044–4054 Feldman M, Naor JS, Schwartz R (2011) Nonmonotone submodular maximization via a structural continuous greedy algorithm. In Proceedings of the 38th international colloquium on au 662_CR11 662_CR33 662_CR34 662_CR13 662_CR36 W Wu (662_CR43) 2019; 7 662_CR31 J Lee (662_CR32) 2010; 23 Y Wang (662_CR41) 2019; 7 A Chakrabarti (662_CR10) 2015; 154 662_CR27 U Feige (662_CR18) 1998; 45 662_CR28 662_CR29 662_CR22 Z Wang (662_CR40) 2016; 31 662_CR44 M Conforti (662_CR12) 1984; 7 662_CR23 662_CR45 662_CR24 G Calinescu (662_CR9) 2011; 40 662_CR25 662_CR21 M Sviridenko (662_CR37) 2004; 32 U Feige (662_CR20) 2011; 40 N Buchbinder (662_CR8) 2015; 44 Y Jiang (662_CR30) 2019 662_CR2 662_CR3 D Golovin (662_CR26) 2011; 42 Y Wang (662_CR42) 2020; 76 662_CR1 662_CR6 662_CR19 662_CR7 662_CR4 662_CR5 662_CR15 662_CR16 662_CR38 D Du (662_CR14) 2014; 2 662_CR17 GL Nemhauser (662_CR35) 1978; 14 662_CR39 R Yang (662_CR46) 2019; 36 |
| References_xml | – reference: Bai W, Bilmes J (2018) Greed is still good: Maximizing monotone submodular +\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document} supermodular(BP) functions. In Proceedings of the 35th international conference on machine learning, pp 314–323 – reference: Bogunovic I, Zhao J, Cevher V (2018) Robust maximization of non-submodular objectives. In Proceedings of the 21st international conference on artificial intelligence and statistics, pp 890–899 – reference: WuWZhangZDuDSet function optimizationJ Oper Res Soc China201972183193395703910.1007/s40305-018-0233-3 – reference: Yang R, Xu D, Du D, Xu Y, Yan X (2019) Maximization of constrained non-submodular functions. In: Proceedings of the 25th international computing and combinatorics conference, pp 615–626 – reference: Bian AA, Buhmann JM, Krause A, Tschiatschek S (2017) Guarantees for greedy maximization of non-submodular functions with applications. In Proceedings of the 34th international conference on machine learning, pp 498–507 – reference: ConfortiMCornuéjolsGSubmodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the Rado-Edmonds theoremDiscrete Appl Math19847325127473689010.1016/0166-218X(84)90003-9 – reference: Kazemi E, Mitrovic M, Zadimoghaddam M, Lattanzi S, Karbasi A (2019) Submodular streaming in all its glory: Tight approximation, minimum memory and low adaptive complexity. In Proceedings of the 36th international conference on machine learning, pp 3311–3320 – reference: Buchbinder N, Feldman M, Garg M (2019) Deterministic (1/2+ϵ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/2+\epsilon )$$\end{document}-approximation for submodular maximization over a matroid. In Proceedings of the 30th Annual ACM-SIAM symposium on discrete algorithms, pp 241–254 – reference: Buchbinder N, Feldman M, Naor JS, Schwartz R (2014) Submodular maximization with cardinality constraints. In Proceedings of the 25th Annual ACM-SIAM symposium on discrete algorithms, pp 1433–1452 – reference: Filmus Y, Ward J (2012) A tight combinatorial algorithm for submodular maximization subject to a matroid constraint. In Proceedings of the 53rd Annual IEEE symposium on foundations of computer science, pp 659–668 – reference: GolovinDKrauseAAdaptive submodularity: theory and applications in active learning and stochastic optimizationJ Artif Intell Res20114214274862874807 – reference: LeeJMirrokniVSNagarajanVSviridenkoMMaximizing nonmonotone submodular functions under matroid or knapsack constraintsSIAM J Discrete Math201023420532078259497110.1137/090750020 – reference: Tschiatschek S, Singla A, Krause A (2017) Selecting sequences of items via submodular maximization. In: Proceedings of the 31st AAAI conference on artificial intelligence, pp 2667–2673 – reference: Feldman M, Karbasi A, Kazemi E (2018) Do less, get more: Streaming submodular maximization with subsampling. In Proceedings of the 32nd international conference on neural information processing systems, pp 730–740 – reference: NemhauserGLWolseyLAFisherMLAn analysis of approximations for maximizing submodular set functions-IMath Program197814126529450386610.1007/BF01588971 – reference: WangYXuDWangYZhangDNon-submodular maximization on massive data streamsJ Glob Optim2020764729743407837610.1007/s10898-019-00840-8 – reference: Feldman M, Naor JS, Schwartz R (2011) Nonmonotone submodular maximization via a structural continuous greedy algorithm. In Proceedings of the 38th international colloquium on automata, languages, and programming, pp 342–353 – reference: Mitrovic M, Feldman M, Krause A, Karbasi A (2018) Submodularity on hypergraphs: From sets to sequences. In Proceedings of the 21st international conference on artificial intelligence and statistics, pp 1177–1184 – reference: Das A, Kempe D (2011) 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 – reference: DuDLiYXiuNXuDSimultaneous approximation of multi-criteria submodular function maximizationJ Oper Res Soc China201423271290326193810.1007/s40305-014-0053-z – reference: Ene A, Nguyen HL, Suh A (2019) An optimal streaming algorithm for non-monotone submodular Maximization. ArXiv:1911.12959 – reference: Sviridenko M, Vondrák J, Ward J (2015) Optimal approximation for submodular and supermodular optimization with bounded curvature. In: Proceedings of the 26th Annual ACM-SIAM symposium on discrete algorithms, pp 1134–1148 – reference: WangYXuDJiangYZhangDMinimizing ratio of monotone non-submodular functionsJ Oper Res Soc China201973449459400811110.1007/s40305-019-00244-1 – reference: Gupta A, Roth A, Schoenebeck G, Talwar K (2010) Constrained non-monotone submodular maximization: offline and secretary algorithms. In Proceedings of the 6th international conference on internet and network economics, pp 246–257 – reference: WangZMoranBWangXPanQApproximation for maximizing monotone non-decreasing set functions with a greedy methodJ Comb Optim20163112943344024410.1007/s10878-014-9707-3 – reference: FeigeUA threshold of lnn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ln n$$\end{document} for approximating set coverJ ACM1998454634652167509510.1145/285055.285059 – reference: Haba R, Kazemi E, Feldman F, Karbasi A (2020) Streaming submodular maximization under a k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-set system constraint. ArXiv: 2002.03352 – reference: CalinescuGChekuriCPalMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J Comput201140617401766286319310.1137/080733991 – reference: FeigeUMirrokniVSVondrákJMaximizing non-monotone submodular functionsSIAM J Comput201140411331153282531210.1137/090779346 – reference: Badanidiyuru A, Mirzasoleiman B, Karbasi A, Krause A (2014) 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 – reference: Chekuri C, Gupta S, Quanrud K (2015) Streaming algorithms for submodular function maximization. In Proceedings of the 42nd International Colloquium on Automata, Languages and Programming, pp 318–330 – reference: Feldman M, Naor J, Schwartz R (2011) A unified continuous greedy algorithm for submodular maximization. In Proceedings of the 52nd IEEE annual symposium on foundations of computer science, pp 570–579 – reference: Gharan SO, Vondrák J (2011) Submodular maximization by simulated annealing. In Proceedings of the 22nd Annual ACM-SIAM symposium on discrete algorithms, pp 1098–1116 – reference: Alaluf N, Feldman M (2019) Making a sieve random: Improved semi-streaming algorithm for submodular maximization under a cardinality constraint. ArXiv: 1906.11237 – reference: BuchbinderNFeldmanMSeffiJSchwartzRA tight linear time (1/2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/2)$$\end{document}-approximation for unconstrained submodular maximizationSIAM J Comput201544513841402341614010.1137/130929205 – reference: Elenberg ER, Dimakis AG, Feldman M, Karbasi A (2017) Streaming weak submodularity: Interpreting neural networks on the fly. In Proceedings of the 31st international conference on neural information processing systems, pp 4044–4054 – reference: Huang CC, Kakimura N, Yoshida Y (2017) Streaming algorithms for maximizing monotone submodular functions under a knapsack constraint. In Proceedings of the 20th international workshop on approximation algorithms for combinatorial optimization problems and the 21st international workshop on randomization and computation No. 11, 11:1–11:14 – reference: Ene A, Nguyen HL (2016) Constrained submodular maximization: Beyond 1/e\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/e$$\end{document}. In Proceedings of the 57th IEEE annual symposium on foundations of computer science, pp 248–257 – reference: Feige U, Izsak R (2013) Welfare maximization and the supermodular degree. In Proceedings of the 4th conference on innovations in theoretical computer science, pp 247-256 – reference: ChakrabartiAKaleSSubmodular maximization meets streaming: Matchings, matroids, and moreMath Program20151541–2225247342193410.1007/s10107-015-0900-7 – reference: Norouzi-Fard A, Tarnawski J, Mitrović S, Zandieh A, Mousavifar A, Svensson O (2018) Beyond 1/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/2$$\end{document}-approximation for submodular maximization on massive data streams. In Proceedings of the 35th international conference on machine learning, pp 3826–3835 – reference: SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper Res Lett20043214143201710710.1016/S0167-6377(03)00062-2 – reference: Mitrovic M, Kazemi E, Feldman M, Krause A, Karbasi A (2019) Adaptive sequence submodularity. ArXiv:1902.05981 – reference: JiangYWangYXuDYangRZhangYStreaming algorithm for maximizing a monotone non-submodular function under d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}-knapsack constraintOptim Lett201910.1007/s11590-019-01430-z – reference: Yang R, Xu D, Guo L, Zhang D (2019) Sequence submodular maximization meets streaming. In: Proceedings of the 13th international conference on combinatorial optimization and applications, pp 565–575 – reference: YangRXuDJiangYWangYZhangDApproximating robust parameterized submodular function maximization in large-scalesAsia-Pac J Oper Res2019364671680399646910.1142/S0217595919500222 – volume: 7 start-page: 251 issue: 3 year: 1984 ident: 662_CR12 publication-title: Discrete Appl Math doi: 10.1016/0166-218X(84)90003-9 – ident: 662_CR15 – ident: 662_CR19 doi: 10.1145/2422436.2422466 – ident: 662_CR13 – ident: 662_CR36 – volume: 36 start-page: 671 issue: 4 year: 2019 ident: 662_CR46 publication-title: Asia-Pac J Oper Res doi: 10.1142/S0217595919500222 – ident: 662_CR17 – ident: 662_CR7 doi: 10.1137/1.9781611973730.80 – volume: 154 start-page: 225 issue: 1–2 year: 2015 ident: 662_CR10 publication-title: Math Program doi: 10.1007/s10107-015-0900-7 – ident: 662_CR21 – ident: 662_CR23 doi: 10.1007/978-3-642-22006-7_29 – volume: 7 start-page: 449 issue: 3 year: 2019 ident: 662_CR41 publication-title: J Oper Res Soc China doi: 10.1007/s40305-019-00244-1 – ident: 662_CR22 doi: 10.1109/FOCS.2011.46 – ident: 662_CR5 – ident: 662_CR3 – ident: 662_CR2 doi: 10.1145/2623330.2623637 – year: 2019 ident: 662_CR30 publication-title: Optim Lett doi: 10.1007/s11590-019-01430-z – ident: 662_CR29 – volume: 14 start-page: 265 issue: 1 year: 1978 ident: 662_CR35 publication-title: Math Program doi: 10.1007/BF01588971 – volume: 32 start-page: 41 issue: 1 year: 2004 ident: 662_CR37 publication-title: Oper Res Lett doi: 10.1016/S0167-6377(03)00062-2 – ident: 662_CR34 – ident: 662_CR44 doi: 10.1007/978-3-030-26176-4_51 – volume: 44 start-page: 1384 issue: 5 year: 2015 ident: 662_CR8 publication-title: SIAM J Comput doi: 10.1137/130929205 – volume: 76 start-page: 729 issue: 4 year: 2020 ident: 662_CR42 publication-title: J Glob Optim doi: 10.1007/s10898-019-00840-8 – volume: 40 start-page: 1740 issue: 6 year: 2011 ident: 662_CR9 publication-title: SIAM J Comput doi: 10.1137/080733991 – volume: 45 start-page: 634 issue: 4 year: 1998 ident: 662_CR18 publication-title: J ACM doi: 10.1145/285055.285059 – ident: 662_CR24 doi: 10.1109/FOCS.2012.55 – volume: 42 start-page: 427 issue: 1 year: 2011 ident: 662_CR26 publication-title: J Artif Intell Res – ident: 662_CR11 doi: 10.1007/978-3-662-47672-7_26 – volume: 40 start-page: 1133 issue: 4 year: 2011 ident: 662_CR20 publication-title: SIAM J Comput doi: 10.1137/090779346 – volume: 23 start-page: 2053 issue: 4 year: 2010 ident: 662_CR32 publication-title: SIAM J Discrete Math doi: 10.1137/090750020 – ident: 662_CR1 – ident: 662_CR4 – ident: 662_CR31 – volume: 2 start-page: 271 issue: 3 year: 2014 ident: 662_CR14 publication-title: J Oper Res Soc China doi: 10.1007/s40305-014-0053-z – ident: 662_CR25 doi: 10.1137/1.9781611973082.83 – ident: 662_CR39 doi: 10.24963/ijcai.2018/379 – ident: 662_CR6 doi: 10.1137/1.9781611975482.16 – ident: 662_CR28 – ident: 662_CR38 doi: 10.1137/1.9781611973730.76 – ident: 662_CR33 – ident: 662_CR16 doi: 10.1109/FOCS.2016.34 – volume: 7 start-page: 183 issue: 2 year: 2019 ident: 662_CR43 publication-title: J Oper Res Soc China doi: 10.1007/s40305-018-0233-3 – volume: 31 start-page: 29 issue: 1 year: 2016 ident: 662_CR40 publication-title: J Comb Optim doi: 10.1007/s10878-014-9707-3 – ident: 662_CR27 doi: 10.1007/978-3-642-17572-5_20 – ident: 662_CR45 doi: 10.1007/978-3-030-36412-0_46 |
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| SubjectTerms | Combinatorics Convex and Discrete Geometry Graph theory Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Maximization Operations Research/Decision Theory Optimization Sequences Theory of Computation Weight |
| Title | Sequence submodular maximization meets streaming |
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