Filters








38,010 Hits in 3.3 sec

Improved Bounds for Online Stochastic Matching [chapter]

Bahman Bahmani, Michael Kapralov
2010 Lecture Notes in Computer Science  
We study the online stochastic matching problem in a form motivated by Internet display advertisement.  ...  On the hardness side, we improve the upper bound of 0.989 on the competitive ratio of any online algorithm obtained by Feldman et al. to 1 − 1/(e + e 2 ) ≈ 0.902.  ...  Our results and techniques In this paper we give improved upper and lower bounds for three questions related to the stochastic matching problem.  ... 
doi:10.1007/978-3-642-15775-2_15 fatcat:zupa5wmgxfennpv35qxeabdrsi

Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts [article]

Brian Brubach and Karthik Abinav Sankararaman and Aravind Srinivasan and Pan Xu
2019 arXiv   pre-print
This framework has a high potential for further improvements since new algorithms for offline stochastic matching can directly improve the ratio for the online problem.  ...  We present an online attenuation framework that uses an algorithm for offline stochastic matching as a black box.  ...  Improved bounds for online stochastic matching. In Algorithms-ESA 2010. Springer, 2010, pp. When LP is the cure for your matching woes: Improved bounds for stochastic matchings.  ... 
arXiv:1804.08062v2 fatcat:oldb342s45bjrhadi55gfn7b3q

Online Matching with Stochastic Rewards

Aranyak Mehta, Debmalya Panigrahi
2012 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science  
This result shows that the best achievable competitive ratio for the ONLINE STOCHASTIC MATCHING problem is provably worse than that for the (non-stochastic) online matching problem.  ...  To address such applications, we propose and study the online matching problem with stochastic rewards (called the ONLINE STOCHASTIC MATCHING problem) in this paper.  ...  It turns out that this is indeed the case for the ONLINE STOCHASTIC MATCHING problem. We show an upper bound on the performance of non-adaptive algorithms for the ONLINE STOCHASTIC MATCHING problem.  ... 
doi:10.1109/focs.2012.65 dblp:conf/focs/MehtaP12 fatcat:p476yn2hojco7maehmvzn527uy

Stochastic Online Metric Matching

Anupam Gupta, Guru Guruganesh, Binghui Peng, David Wajc, Michael Wagner
2019 International Colloquium on Automata, Languages and Programming  
Such stochastic arrival models have been widely studied for the maximization variants of the online matching problem; however, the only known result for the minimization problem is a tight O(log n)-competitiveness  ...  We study the minimum-cost metric perfect matching problem under online i.i.d arrivals.  ...  We are hopeful that our work will spur further research in online minimum-cost perfect matching under stochastic arrivals, and close the gap between our upper bounds and the (trivial) lower bounds for  ... 
doi:10.4230/lipics.icalp.2019.67 dblp:conf/icalp/GuptaGPW19 fatcat:qbxsfljgb5gafomqqopudugzcm

Online Stochastic Matching: Online Actions Based on Offline Statistics [article]

Vahideh H. Manshadi, Shayan Oveis Gharan, Amin Saberi
2011 arXiv   pre-print
We consider the online stochastic matching problem proposed by Feldman et al. [FMMM09] as a model of display ad allocation.  ...  This improves upon the 5/6 hardness result proved by Goel and Mehta GM08 for the permutation model.  ...  We should also point out that competitive analysis is not the only possible or necessarily the most suitable approach for this problem.  ... 
arXiv:1007.1673v2 fatcat:s3s4vl3fo5bqrng25i6by6zgsm

Bipartite Stochastic Matching: Online, Random Order, and I.I.D. Models [article]

Allan Borodin, Calum MacRury, Akash Rakheja
2021 arXiv   pre-print
Within the context of stochastic probing with commitment, we consider the online stochastic matching problem; that is, the one sided online bipartite matching problem where edges adjacent to an online  ...  Our main contribution is to provide a new LP relaxation and a unified approach for establishing new and improved competitive bounds in three different input model settings (namely, adversarial, random  ...  Acknowledgements We would like to thank Denis Pankratov for his helpful comments.  ... 
arXiv:2004.14304v3 fatcat:kvj247cr3zgtnonz62xrwr3syi

Improved Approximation Algorithms for Stochastic-Matching Problems [article]

Marek Adamczyk, Brian Brubach, Fabrizio Grandoni, Karthik A. Sankararaman, Aravind Srinivasan, Pan Xu
2020 arXiv   pre-print
We consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. In this problem, we are given an undirected graph.  ...  Second, the timeout of a node v upper-bounds the number of edges incident to v that can be probed. The goal is to maximize the expected weight of the constructed matching.  ...  Stochastic Matching in Bipartite Graphs In this section we present our improved approximation algorithm for Stochastic Matching in bipartite graphs.  ... 
arXiv:2010.08142v1 fatcat:tfhu6dtdrbb47nuojagcnedn3i

Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience [article]

Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan
2021 arXiv   pre-print
Our approach to online matching utilizes black-box algorithms for matching on star graphs under various models of patience.  ...  Finally, we present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, bounding the worst-case performance of some popular greedy approaches  ...  Nathaniel Grammel was supported in part by NSF award CCF-1918749, and by research awards from Amazon and Google. 31 Will Ma would like to thank Jake Feldman and Danny Segev for instructive discussions  ... 
arXiv:1907.03963v3 fatcat:mujhjz7afvf6hm4e5eaiwabkdq

Improved Approximation Algorithms for Stochastic Matching [chapter]

Marek Adamczyk, Fabrizio Grandoni, Joydeep Mukherjee
2015 Lecture Notes in Computer Science  
In this paper we consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating.  ...  The process is constrained in two ways: once an edge is taken it cannot be removed from the matching, and the timeout of node v upper-bounds the number of edges incident to v that can be probed.  ...  There is an expected 4.07-approximation algorithm for Online Stochastic Matching with Timeouts.  ... 
doi:10.1007/978-3-662-48350-3_1 fatcat:pldpv2i44zamtlhgmelflvr2r4

Online Primal Dual Meets Online Matching with Stochastic Rewards: Configuration LP to the Rescue [article]

Zhiyi Huang, Qiankun Zhang
2020 arXiv   pre-print
Our main result is a 0.572 competitive algorithm for the case of vanishing and unequal probabilities, improving the best previous bound of 0.534 by Mehta, Waggoner, and Zadimoghaddam (SODA 2015) and, in  ...  This paper unlocks the power of randomized online primal dual in online matching with stochastic rewards by employing the configuration linear program rather than the standard matching linear program used  ...  Online Matching with Stochastic Rewards.  ... 
arXiv:2002.01802v1 fatcat:5odr7ts7mjbz3bbafcmq4oplly

The Power of Multiple Choices in Online Stochastic Matching [article]

Zhiyi Huang and Xinkai Shu and Shuyi Yan
2022 arXiv   pre-print
We study the power of multiple choices in online stochastic matching.  ...  For unweighted and vertex-weighted matching, we adopt the online correlated selection (OCS) technique into the stochastic setting, and improve the competitive ratios to 0.716, from 0.711 and 0.7 respectively  ...  Acknowledgments We thank Donglei Du for helpful discussions on DR submodular functions. We also thank Zipei Nie and Nengkun Yu for their help with the analysis of differential inequalities.  ... 
arXiv:2203.02883v1 fatcat:yzgholx77nfk5dez4wx6kdybka

Online Stochastic Matching: Beating 1-1/e [article]

Jon Feldman, Aranyak Mehta, Vahab Mirrokni, S. Muthukrishnan
2009 arXiv   pre-print
Our main result is a 0.67-approximation online algorithm for stochastic bipartite matching, breaking this 1 - 1/e barrier.  ...  We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet.  ...  We thank Ciamac Moallemi and Nicole Immorlica for pointing us to related work.  ... 
arXiv:0905.4100v1 fatcat:vkaxtilzybf6vnftxsvos2e3se

When LP Is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings [chapter]

Nikhil Bansal, Anupam Gupta, Jian Li, Julián Mestre, Viswanath Nagarajan, Atri Rudra
2010 Lecture Notes in Computer Science  
of the stochastic online matching problem [Feldman et al.  ...  ICALP 09]. • Combining our LP-rounding algorithm with the natural greedy algorithm, we give an improved 3.46 approximation for unweighted stochastic matching on general graphs. • We introduce a generalization  ...  We would like to thank Aravind Srinivasan for helpful discussions.  ... 
doi:10.1007/978-3-642-15781-3_19 fatcat:uokxdnlqkzbsxl3mddinzajm2y

When LP Is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings

Nikhil Bansal, Anupam Gupta, Jian Li, Julián Mestre, Viswanath Nagarajan, Atri Rudra
2011 Algorithmica  
of the stochastic online matching problem [Feldman et al.  ...  ICALP 09]. • Combining our LP-rounding algorithm with the natural greedy algorithm, we give an improved 3.46 approximation for unweighted stochastic matching on general graphs. • We introduce a generalization  ...  We would like to thank Aravind Srinivasan for helpful discussions.  ... 
doi:10.1007/s00453-011-9511-8 fatcat:f2kkcixfybgmrasf6kn5bq2b4u

When LP is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings [article]

Nikhil Bansal, Anupam Gupta, Jian Li, Julian Mestre, Viswanath Nagarajan, Atri Rudra
2010 arXiv   pre-print
Combining our LP-rounding algorithm with the natural greedy algorithm, we give an improved 3.46 approximation for unweighted stochastic matching on general graphs.  ...  Our main results are the following: We give a 4 approximation for weighted stochastic matching on general graphs, and a 3 approximation on bipartite graphs.  ...  We would like to thank Aravind Srinivasan for helpful discussions.  ... 
arXiv:1008.5356v1 fatcat:4c6rug3nsbhkxjvxuojsju456e
« Previous Showing results 1 — 15 out of 38,010 results