Maximum Matching Sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model
The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-strea...
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| Vydáno v: | Algorithmica Ročník 86; číslo 4; s. 1173 - 1209 |
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| Abstract | The problem of finding a maximum size matching in a graph (known as the
maximum matching
problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains
1
/
2
-approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021.
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19
) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than
1
/
2
-approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. |
|---|---|
| AbstractList | The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1/2-approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1/2-approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19 ) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. |
| Author | Szarf, Ariel Feldman, Moran |
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| Cites_doi | 10.4230/LIPIcs.ICALP.2021.19 10.1145/3564246.3585110 10.4230/LIPIcs.APPROX-RANDOM.2016.17 10.1145/3230819 10.1145/3154855 10.4230/LIPIcs.MFCS.2018.74 10.4230/LIPIcs.ESA.2017.29 10.1137/1.9781611974782.113 10.1145/3583668.3594570 10.4230/LIPIcs.ICALP.2020.12 10.1137/1.9781611973105.121 10.1109/ICDMW.2016.0092 10.4230/OASIcs.SOSA.2018.14 10.1109/FOCS46700.2020.00041 10.1137/1.9781611973099.41 10.1145/3519935.3520039 10.4230/LIPIcs.APPROX-RANDOM.2014.96 10.1137/1.9781611976465.112 10.48550/arXiv.2307.02968 10.4230/LIPIcs.APPROX-RANDOM.2017.15 10.1007/978-3-642-32512-0_20 10.1145/3406325.3451113 10.1137/1.9781611977073.32 10.1137/1.9781611976496.18 10.48550/arXiv.2307.08772 10.1137/1.9781611973402.55 10.1145/3274668 10.1007/11538462_15 10.4230/LIPIcs.APPROX/RANDOM.2021.19 10.1002/net.3230210203 10.1137/1.9781611974331.ch92 10.1016/j.tcs.2005.09.013 10.1007/s00453-010-9438-5 10.1137/100801901 10.1145/3406325.3451110 10.1137/0202019 |
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| Keywords | Maximum matching Semi-streaming algorithms Multi-pass algorithms Adversarial order streams |
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| References | Assadi, S., Jambulapati, A., Jin, Y., Sidford, A., Tian, K.: Semi-streaming bipartite matching in fewer passes and optimal space. arXiv e-prints pp. arXiv–2011 (2020) Kapralov, M.: Better bounds for matchings in the streaming model. In: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2013), pp. 1679–1697. https://doi.org/10.1137/1.9781611973105.121 Assadi, S.: A simple (1-ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document})-approximation semi-streaming algorithm for maximum (weighted) matching. CoRR arXiv:abs/2307.02968 (2023). https://doi.org/10.48550/arXiv.2307.02968 Fischer, M., Mitrovic, S., Uitto, J.: Deterministic (1+ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document})-approximate maximum matching with poly(1/ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}) passes in the semi-streaming model and beyond. In: Leonardi, S., Gupta, A. (eds.) STOC ’22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20–24, 2022, pp. 248–260. ACM (2022). https://doi.org/10.1145/3519935.3520039 HopcroftJEKarpRMAn n5/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n^{5/2}$$\end{document} algorithm for maximum matchings in bipartite graphsSIAM J. Comput.19732422523133769910.1137/0202019 Kapralov, M.: Space lower bounds for approximating maximum matching in the edge arrival model. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, SODA 2021, January 10–13, 2021 (2021), pp. 1874–1893. https://doi.org/10.1137/1.9781611976465.112 Bernstein, A.: Improved bounds for matching in random-order streams. In: Czumaj, A. Dawar, A., Merelli, E. (eds.) 47th International Colloquium on Automata, Languages, and Programming, ICALP 2020, July 8–11, 2020, Saarbrücken, Germany. LIPIcs, vol. 168, (Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020), pp. 12:1–12:13. https://doi.org/10.4230/LIPIcs.ICALP.2020.12 Konrad, C., Magniez, F., Mathieu, C.: Maximum matching in semi-streaming with few passes. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques—15th International Workshop, APPROX 2012, and 16th International Workshop, RANDOM 2012, Cambridge, MA, USA, August 15–17, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7408, pp. 231–242 (2012). https://doi.org/10.1007/978-3-642-32512-0_20 EdmondsJMaximum matching and a polyhedron with 0, 1-verticesJ. Res. Natl. Bureau Stand. B196569125–1305556183532 Cormode, G., Jowhari, H., Monemizadeh, M., Muthukrishnan, S.: The sparse awakens: streaming algorithms for matching size estimation in sparse graphs. In: 25th Annual European Symposium on Algorithms, ESA 2017, September 4–6, 2017, Vienna, Austria. LIPIcs, vol. 87, pp. 29:1–29:15 (2017). https://doi.org/10.4230/LIPIcs.ESA.2017.29 Kale, S., Tirodkar, S.: Maximum matching in two, three, and a few more passes over graph streams. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2017, August 16-18, 2017, Berkeley, CA, USA. LIPIcs, vol. 81 (2017), pp. 15:1–15:21. https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2017.15 Kapralov, M., Khanna, S., Sudan, M.: Approximating matching size from random streams. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2014), pp. 734–751. https://doi.org/10.1137/1.9781611973402.55 McGregor, A., Vorotnikova, S.: Planar matching in streams revisited. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), pp. 17:1–17:12 (2016). https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2016.17 Assadi, S., Liu, S.C., Tarjan, R.E.: An auction algorithm for bipartite matching in streaming and massively parallel computation models. In: 4th Symposium on Simplicity in Algorithms (SOSA), pp. 165–171 (2021). https://doi.org/10.1137/1.9781611976496.18 ZelkeMWeighted matching in the semi-streaming modelAlgorithmica2012621–2120288603310.1007/s00453-010-9438-5 Assadi, S., Khanna, S., Li, Y.: On estimating maximum matching size in graph streams. In: Klein, P.N. (ed) Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16–19, pp. 1723–1742. SIAM (2017). https://doi.org/10.1137/1.9781611974782.113 McGregor, A.: Finding graph matchings in data streams. In: Approximation, Randomization and Combinatorial Optimization, Algorithms and Techniques, 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX) and 9th International Workshop on Randomization and Computation (RANDOM) (2005), pp. 170–181. https://doi.org/10.1007/11538462_15 Esfandiari, H., Hajiaghayi, M., Monemizadeh, M.: Finding large matchings in semi-streaming. In: Domeniconi, C., Gullo, F., Bonchi, F., Domingo-Ferrer, J., Baeza-Yates, R., Zhou, Z., Wu, X. (eds.) IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016, December 12–15, 2016, Barcelona, Spain, pp. 608–614. IEEE Computer Society (2016). https://doi.org/10.1109/ICDMW.2016.0092 Assadi, S., Kol, G., Saxena, R.R., Yu, H.: Multi-pass graph streaming lower bounds for cycle counting, max-cut, matching size, and other problems. In: Irani, S. (ed) 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16–19, 2020, pp. 354–364. IEEE (2020). https://doi.org/10.1109/FOCS46700.2020.00041 FeigenbaumJKannanSMcGregorASuriSZhangJOn graph problems in a semi-streaming modelTheor. Comput. Sci.20053482–3207216218137610.1016/j.tcs.2005.09.013 BalinskiMLGonzalezJMaximum matchings in bipartite graphs via strong spanning treesNetworks1991212165179109383710.1002/net.3230210203 Huang, S., Su, H.: (1-ε)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1-\varepsilon )$$\end{document}-approximate maximum weighted matching in poly(1/ε,logn)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/\varepsilon , \log n)$$\end{document} time in the distributed and parallel settings. In: Oshman, R., Nolin, A., Halldórsson, M.M., Balliu, A. (eds.), ACM Symposium on Principles of Distributed Computing (PODC), pp. 44–54. ACM (2023). https://doi.org/10.1145/3583668.3594570 Konrad, C.: A simple augmentation method for matchings with applications to streaming algorithms. In: 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS). LIPIcs, vol. 117 (2018), pp. 74:1–74:16. https://doi.org/10.4230/LIPIcs.MFCS.2018.74 Chitnis, R., Cormode, G., Esfandiari, H., Hajiaghayi, M., McGregor, A., Monemizadeh, M., Vorotnikova, S.: Kernelization via sampling with applications to finding matchings and related problems in dynamic graph streams. In: Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2016), pp. 1326–1344. https://doi.org/10.1137/1.9781611974331.ch92 McGregor, A., Vorotnikova, S.: A simple, space-efficient, streaming algorithm for matchings in low arboricity graphs. In: 1st Symposium on Simplicity in Algorithms (SOSA). OASICS, vol. 61, pp. 14:1–14:4 (2018). https://doi.org/10.4230/OASIcs.SOSA.2018.14 Assadi, S., N, V.: Graph streaming lower bounds for parameter estimation and property testing via a streaming XOR lemma. In: Khuller, S., Williams, V.V. (eds.) STOC ’21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, June 21–25, 2021, pp. 612–625. ACM (2021). https://doi.org/10.1145/3406325.3451110 AhnKJGuhaSAccess to data and number of iterations: Dual primal algorithms for maximum matching under resource constraintsACM Trans. Parallel Comput.20184417:117:4010.1145/3154855 Assadi, S., Behnezhad, S.: Beating two-thirds for random-order streaming matching. In: 48th International Colloquium on Automata, Languages, and Programming, ICALP 2021, July 12–16, 2021. LIPIcs, vol. 198, pp. 19:1–19:13 (2021). https://doi.org/10.4230/LIPIcs.ICALP.2021.19 Assadi, S., Behnezhad, S., Khanna, S., Li, H.: On regularity lemma and barriers in streaming and dynamic matching. In: Saha, B., Servedio, R.A. (eds.) 55th Annual ACM Symposium on Theory of Computing (STOC), pp. 131–144. ACM (2023). https://doi.org/10.1145/3564246.3585110 Konrad, C., Naidu, K.K.: On two-pass streaming algorithms for maximum bipartite matching. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol. 207 (2021), pp. 19:1–19:18. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19 Assadi, S.: A two-pass (conditional) lower bound for semi-streaming maximum matching. In: Naor, J.S., Buchbinder, N. (eds.) ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 708–742. SIAM (2022). https://doi.org/10.1137/1.9781611977073.32 PazASchwartzmanGA (2+ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{ma 1190_CR24 H Esfandiari (1190_CR17) 2018; 14 1190_CR23 1190_CR25 1190_CR28 1190_CR27 1190_CR29 1190_CR20 1190_CR22 1190_CR21 KJ Ahn (1190_CR26) 2018; 4 J Edmonds (1190_CR3) 1965; 69 ML Balinski (1190_CR2) 1991; 21 1190_CR9 1190_CR8 1190_CR7 1190_CR6 1190_CR13 1190_CR12 1190_CR34 1190_CR15 JE Hopcroft (1190_CR4) 1973; 2 1190_CR14 J Feigenbaum (1190_CR5) 2005; 348 1190_CR1 1190_CR16 1190_CR38 1190_CR19 1190_CR18 1190_CR31 1190_CR30 L Epstein (1190_CR35) 2011; 25 1190_CR11 1190_CR33 A Paz (1190_CR37) 2019; 15 1190_CR10 1190_CR32 M Zelke (1190_CR36) 2012; 62 |
| References_xml | – reference: Konrad, C.: A simple augmentation method for matchings with applications to streaming algorithms. In: 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS). LIPIcs, vol. 117 (2018), pp. 74:1–74:16. https://doi.org/10.4230/LIPIcs.MFCS.2018.74 – reference: Azarmehr, A., Behnezhad, S., Roghani, M.: Fully dynamic matching: (2-√\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\surd $$\end{document}2)-approximation in polylog update time. CoRR arXiv:abs/2307.08772 (2023). https://doi.org/10.48550/arXiv.2307.08772 – reference: McGregor, A.: Finding graph matchings in data streams. In: Approximation, Randomization and Combinatorial Optimization, Algorithms and Techniques, 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX) and 9th International Workshop on Randomization and Computation (RANDOM) (2005), pp. 170–181. https://doi.org/10.1007/11538462_15 – reference: Assadi, S., Jambulapati, A., Jin, Y., Sidford, A., Tian, K.: Semi-streaming bipartite matching in fewer passes and optimal space. arXiv e-prints pp. arXiv–2011 (2020) – reference: McGregor, A., Vorotnikova, S.: Planar matching in streams revisited. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), pp. 17:1–17:12 (2016). https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2016.17 – reference: Huang, S., Su, H.: (1-ε)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1-\varepsilon )$$\end{document}-approximate maximum weighted matching in poly(1/ε,logn)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1/\varepsilon , \log n)$$\end{document} time in the distributed and parallel settings. In: Oshman, R., Nolin, A., Halldórsson, M.M., Balliu, A. (eds.), ACM Symposium on Principles of Distributed Computing (PODC), pp. 44–54. ACM (2023). https://doi.org/10.1145/3583668.3594570 – reference: EsfandiariHHajiaghayiMLiaghatVMonemizadehMOnakKStreaming algorithms for estimating the matching size in planar graphs and beyondACM Trans. Algorithms201814448:148:23387432010.1145/3230819 – reference: Kale, S., Tirodkar, S.: Maximum matching in two, three, and a few more passes over graph streams. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2017, August 16-18, 2017, Berkeley, CA, USA. LIPIcs, vol. 81 (2017), pp. 15:1–15:21. https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2017.15 – reference: Konrad, C., Magniez, F., Mathieu, C.: Maximum matching in semi-streaming with few passes. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques—15th International Workshop, APPROX 2012, and 16th International Workshop, RANDOM 2012, Cambridge, MA, USA, August 15–17, 2012. Proceedings. Lecture Notes in Computer Science, vol. 7408, pp. 231–242 (2012). https://doi.org/10.1007/978-3-642-32512-0_20 – reference: Bernstein, A., Dudeja, A., Langley, Z.: A framework for dynamic matching in weighted graphs. In: Khuller, S., Williams, V.V. (eds.) STOC ’21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, June 21–25, 2021, pp. 668–681. ACM (2021). https://doi.org/10.1145/3406325.3451113 – reference: Crouch, M.S., Stubbs, D.M.: Improved streaming algorithms for weighted matching, via unweighted matching. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM). LIPIcs, vol. 28, pp. 96–104 (2014). https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2014.96 – reference: McGregor, A., Vorotnikova, S.: A simple, space-efficient, streaming algorithm for matchings in low arboricity graphs. In: 1st Symposium on Simplicity in Algorithms (SOSA). OASICS, vol. 61, pp. 14:1–14:4 (2018). https://doi.org/10.4230/OASIcs.SOSA.2018.14 – reference: Assadi, S.: A simple (1-ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document})-approximation semi-streaming algorithm for maximum (weighted) matching. CoRR arXiv:abs/2307.02968 (2023). https://doi.org/10.48550/arXiv.2307.02968 – reference: Assadi, S., N, V.: Graph streaming lower bounds for parameter estimation and property testing via a streaming XOR lemma. In: Khuller, S., Williams, V.V. (eds.) STOC ’21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, June 21–25, 2021, pp. 612–625. ACM (2021). https://doi.org/10.1145/3406325.3451110 – reference: Chitnis, R., Cormode, G., Esfandiari, H., Hajiaghayi, M., McGregor, A., Monemizadeh, M., Vorotnikova, S.: Kernelization via sampling with applications to finding matchings and related problems in dynamic graph streams. In: Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2016), pp. 1326–1344. https://doi.org/10.1137/1.9781611974331.ch92 – reference: Goel, A., Kapralov, M., Khanna, S.: On the communication and streaming complexity of maximum bipartite matching. In: Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2012), pp. 468–485. https://doi.org/10.1137/1.9781611973099.41 – reference: Assadi, S., Behnezhad, S., Khanna, S., Li, H.: On regularity lemma and barriers in streaming and dynamic matching. In: Saha, B., Servedio, R.A. (eds.) 55th Annual ACM Symposium on Theory of Computing (STOC), pp. 131–144. ACM (2023). https://doi.org/10.1145/3564246.3585110 – reference: HopcroftJEKarpRMAn n5/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n^{5/2}$$\end{document} algorithm for maximum matchings in bipartite graphsSIAM J. Comput.19732422523133769910.1137/0202019 – reference: Cormode, G., Jowhari, H., Monemizadeh, M., Muthukrishnan, S.: The sparse awakens: streaming algorithms for matching size estimation in sparse graphs. In: 25th Annual European Symposium on Algorithms, ESA 2017, September 4–6, 2017, Vienna, Austria. LIPIcs, vol. 87, pp. 29:1–29:15 (2017). https://doi.org/10.4230/LIPIcs.ESA.2017.29 – reference: AhnKJGuhaSAccess to data and number of iterations: Dual primal algorithms for maximum matching under resource constraintsACM Trans. Parallel Comput.20184417:117:4010.1145/3154855 – reference: EpsteinLLevinAMestreJSegevDImproved approximation guarantees for weighted matching in the semi-streaming modelSIAM J. Discrete Math.201125312511265283759510.1137/100801901 – reference: Assadi, S., Liu, S.C., Tarjan, R.E.: An auction algorithm for bipartite matching in streaming and massively parallel computation models. In: 4th Symposium on Simplicity in Algorithms (SOSA), pp. 165–171 (2021). https://doi.org/10.1137/1.9781611976496.18 – reference: Assadi, S., Kol, G., Saxena, R.R., Yu, H.: Multi-pass graph streaming lower bounds for cycle counting, max-cut, matching size, and other problems. In: Irani, S. (ed) 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16–19, 2020, pp. 354–364. IEEE (2020). https://doi.org/10.1109/FOCS46700.2020.00041 – reference: BalinskiMLGonzalezJMaximum matchings in bipartite graphs via strong spanning treesNetworks1991212165179109383710.1002/net.3230210203 – reference: FeigenbaumJKannanSMcGregorASuriSZhangJOn graph problems in a semi-streaming modelTheor. Comput. Sci.20053482–3207216218137610.1016/j.tcs.2005.09.013 – reference: Esfandiari, H., Hajiaghayi, M., Monemizadeh, M.: Finding large matchings in semi-streaming. In: Domeniconi, C., Gullo, F., Bonchi, F., Domingo-Ferrer, J., Baeza-Yates, R., Zhou, Z., Wu, X. (eds.) IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016, December 12–15, 2016, Barcelona, Spain, pp. 608–614. IEEE Computer Society (2016). https://doi.org/10.1109/ICDMW.2016.0092 – reference: Kapralov, M.: Better bounds for matchings in the streaming model. In: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2013), pp. 1679–1697. https://doi.org/10.1137/1.9781611973105.121 – reference: Kapralov, M., Khanna, S., Sudan, M.: Approximating matching size from random streams. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2014), pp. 734–751. https://doi.org/10.1137/1.9781611973402.55 – reference: Assadi, S., Behnezhad, S.: Beating two-thirds for random-order streaming matching. In: 48th International Colloquium on Automata, Languages, and Programming, ICALP 2021, July 12–16, 2021. LIPIcs, vol. 198, pp. 19:1–19:13 (2021). https://doi.org/10.4230/LIPIcs.ICALP.2021.19 – reference: PazASchwartzmanGA (2+ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2 + \epsilon $$\end{document})-approximation for maximum weight matching in the semi-streaming modelACM Trans. Algorithms201915218:118:15395109110.1145/3274668 – reference: Bernstein, A.: Improved bounds for matching in random-order streams. In: Czumaj, A. Dawar, A., Merelli, E. (eds.) 47th International Colloquium on Automata, Languages, and Programming, ICALP 2020, July 8–11, 2020, Saarbrücken, Germany. LIPIcs, vol. 168, (Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020), pp. 12:1–12:13. https://doi.org/10.4230/LIPIcs.ICALP.2020.12 – reference: Kapralov, M.: Space lower bounds for approximating maximum matching in the edge arrival model. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, SODA 2021, January 10–13, 2021 (2021), pp. 1874–1893. https://doi.org/10.1137/1.9781611976465.112 – reference: Assadi, S., Khanna, S., Li, Y.: On estimating maximum matching size in graph streams. In: Klein, P.N. (ed) Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16–19, pp. 1723–1742. SIAM (2017). https://doi.org/10.1137/1.9781611974782.113 – reference: EdmondsJMaximum matching and a polyhedron with 0, 1-verticesJ. Res. Natl. Bureau Stand. B196569125–1305556183532 – reference: Assadi, S.: A two-pass (conditional) lower bound for semi-streaming maximum matching. In: Naor, J.S., Buchbinder, N. (eds.) ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 708–742. SIAM (2022). https://doi.org/10.1137/1.9781611977073.32 – reference: ZelkeMWeighted matching in the semi-streaming modelAlgorithmica2012621–2120288603310.1007/s00453-010-9438-5 – reference: Konrad, C., Naidu, K.K.: On two-pass streaming algorithms for maximum bipartite matching. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol. 207 (2021), pp. 19:1–19:18. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19 – reference: Fischer, M., Mitrovic, S., Uitto, J.: Deterministic (1+ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document})-approximate maximum matching with poly(1/ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document}) passes in the semi-streaming model and beyond. In: Leonardi, S., Gupta, A. (eds.) STOC ’22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20–24, 2022, pp. 248–260. ACM (2022). https://doi.org/10.1145/3519935.3520039 – ident: 1190_CR32 doi: 10.4230/LIPIcs.ICALP.2021.19 – ident: 1190_CR24 doi: 10.1145/3564246.3585110 – ident: 1190_CR18 doi: 10.4230/LIPIcs.APPROX-RANDOM.2016.17 – volume: 14 start-page: 48:1 issue: 4 year: 2018 ident: 1190_CR17 publication-title: ACM Trans. Algorithms doi: 10.1145/3230819 – volume: 4 start-page: 17:1 issue: 4 year: 2018 ident: 1190_CR26 publication-title: ACM Trans. Parallel Comput. doi: 10.1145/3154855 – ident: 1190_CR9 doi: 10.4230/LIPIcs.MFCS.2018.74 – ident: 1190_CR16 doi: 10.4230/LIPIcs.ESA.2017.29 – ident: 1190_CR20 doi: 10.1137/1.9781611974782.113 – ident: 1190_CR30 doi: 10.1145/3583668.3594570 – ident: 1190_CR33 doi: 10.4230/LIPIcs.ICALP.2020.12 – ident: 1190_CR8 doi: 10.1137/1.9781611973105.121 – ident: 1190_CR12 doi: 10.1109/ICDMW.2016.0092 – ident: 1190_CR19 doi: 10.4230/OASIcs.SOSA.2018.14 – ident: 1190_CR21 doi: 10.1109/FOCS46700.2020.00041 – ident: 1190_CR7 doi: 10.1137/1.9781611973099.41 – ident: 1190_CR29 doi: 10.1145/3519935.3520039 – ident: 1190_CR34 doi: 10.4230/LIPIcs.APPROX-RANDOM.2014.96 – ident: 1190_CR6 doi: 10.1137/1.9781611976465.112 – ident: 1190_CR31 doi: 10.48550/arXiv.2307.02968 – ident: 1190_CR10 doi: 10.4230/LIPIcs.APPROX-RANDOM.2017.15 – ident: 1190_CR11 doi: 10.1007/978-3-642-32512-0_20 – ident: 1190_CR38 doi: 10.1145/3406325.3451113 – ident: 1190_CR13 doi: 10.1137/1.9781611977073.32 – ident: 1190_CR27 – ident: 1190_CR28 doi: 10.1137/1.9781611976496.18 – ident: 1190_CR14 doi: 10.48550/arXiv.2307.08772 – ident: 1190_CR15 doi: 10.1137/1.9781611973402.55 – volume: 15 start-page: 18:1 issue: 2 year: 2019 ident: 1190_CR37 publication-title: ACM Trans. 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maximum matching
problem) is one of the most classical problems in computer science.... The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science.... |
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| SubjectTerms | Algorithm Analysis and Problem Complexity Algorithms Approximation Combinatorial analysis Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Data transmission Graphs Greedy algorithms Matching Mathematical analysis Mathematics of Computing Theory of Computation |
| Title | Maximum Matching Sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model |
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