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Journal articles on the topic 'Optimal matching'

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Published: 4 June 2021

Last updated: 11 March 2023

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1

Mazón, José Manuel, Julio Daniel Rossi, and Julián Toledo. "On optimal matching measures for matching problems related to the Euclidean distance." Mathematica Bohemica 139, no. 4 (2014): 553–66. http://dx.doi.org/10.21136/mb.2014.144132.

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2

CHENG, EDDIE, and SACHIN PADMANABHAN. "MATCHING PRECLUSION AND CONDITIONAL MATCHING PRECLUSION FOR CROSSED CUBES." Parallel Processing Letters 22, no. 02 (May 16, 2012): 1250005. http://dx.doi.org/10.1142/s0129626412500053.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find the matching preclusion number and the conditional matching preclusion number with the classification of the optimal sets for the class of crossed cubes, an important variant of the class of hypercubes. Indeed, we will establish more general results on the matching preclusion and the conditional matching preclusion problems for a larger class of interconnection networks.

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3

Cabra, Luís M. B. "Optimal matching auctions." Economics Letters 37, no. 1 (September 1991): 7–9. http://dx.doi.org/10.1016/0165-1765(91)90234-c.

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4

Baccara, Mariagiovanna, SangMok Lee, and Leeat Yariv. "Optimal dynamic matching." Theoretical Economics 15, no. 3 (2020): 1221–78. http://dx.doi.org/10.3982/te3740.

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We study a dynamic matching environment where individuals arrive sequentially. There is a trade‐off between waiting for a thicker market, allowing for higher‐quality matches, and minimizing agents' waiting costs. The optimal mechanism cumulates a stock of incongruent pairs up to a threshold and matches all others in an assortative fashion instantaneously. In discretionary settings, a similar protocol ensues in equilibrium, but expected queues are inefficiently long. We quantify the welfare gain from centralization, which can be substantial, even for low waiting costs. We also evaluate welfare improvements generated by alternative priority protocols.

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5

CHENG, EDDIE, RANDY JIA, and DAVID LU. "MATCHING PRECLUSION AND CONDITIONAL MATCHING PRECLUSION FOR AUGMENTED CUBES." Journal of Interconnection Networks 11, no. 01n02 (March 2010): 35–60. http://dx.doi.org/10.1142/s0219265910002726.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those incident to a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those incident to a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find this number and classify all optimal sets for the augmented cubes, a class of networks designed as an improvement of the hypercubes.

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6

Ekeland, Ivar. "An optimal matching problem." ESAIM: Control, Optimisation and Calculus of Variations 11, no. 1 (December 15, 2004): 57–71. http://dx.doi.org/10.1051/cocv:2004034.

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7

Michalis, Constantine, Nicholas E. Scott-Samuel, David P. Gibson, and Innes C. Cuthill. "Optimal background matching camouflage." Proceedings of the Royal Society B: Biological Sciences 284, no. 1858 (July 12, 2017): 20170709. http://dx.doi.org/10.1098/rspb.2017.0709.

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Background matching is the most familiar and widespread camouflage strategy: avoiding detection by having a similar colour and pattern to the background. Optimizing background matching is straightforward in a homogeneous environment, or when the habitat has very distinct sub-types and there is divergent selection leading to polymorphism. However, most backgrounds have continuous variation in colour and texture, so what is the best solution? Not all samples of the background are likely to be equally inconspicuous, and laboratory experiments on birds and humans support this view. Theory suggests that the most probable background sample (in the statistical sense), at the size of the prey, would, on average, be the most cryptic. We present an analysis, based on realistic assumptions about low-level vision, that estimates the distribution of background colours and visual textures, and predicts the best camouflage. We present data from a field experiment that tests and supports our predictions, using artificial moth-like targets under bird predation. Additionally, we present analogous data for humans, under tightly controlled viewing conditions, searching for targets on a computer screen. These data show that, in the absence of predator learning, the best single camouflage pattern for heterogeneous backgrounds is the most probable sample.

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8

Hollister, Matissa. "Is Optimal Matching Suboptimal?" Sociological Methods & Research 38, no. 2 (November 2009): 235–64. http://dx.doi.org/10.1177/0049124109346164.

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9

Ramachandran, T., and K. Velusamy. "Optimal Matching Using SMA." Journal of Information and Optimization Sciences 35, no. 4 (July 4, 2014): 359–72. http://dx.doi.org/10.1080/02522667.2014.926705.

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10

Didier, Gilles. "Optimal pattern matching algorithms." Journal of Complexity 51 (April 2019): 79–109. http://dx.doi.org/10.1016/j.jco.2018.10.003.

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11

Fredriksson, Kimmo, and Szymon Grabowski. "Average-optimal string matching." Journal of Discrete Algorithms 7, no. 4 (December 2009): 579–94. http://dx.doi.org/10.1016/j.jda.2008.09.001.

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12

Deng, Xiaotie. "Distributed near-optimal matching." Combinatorica 16, no. 4 (December 1996): 453–64. http://dx.doi.org/10.1007/bf01271265.

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13

CHENG, EDDIE, DAVID LU, and BRIAN XU. "STRONG MATCHING PRECLUSION OF PANCAKE GRAPHS." Journal of Interconnection Networks 14, no. 02 (June 2013): 1350007. http://dx.doi.org/10.1142/s0219265913500072.

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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem that was introduced by Park and Ihm. In this paper, we examine the properties of pancake graphs by finding its strong matching preclusion number and categorizing all optimal solutions.

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14

Bienkowski, Marcin, David Fuchssteiner, Jan Marcinkowski, and Stefan Schmid. "Online Dynamic B-Matching." ACM SIGMETRICS Performance Evaluation Review 48, no. 3 (March 5, 2021): 99–108. http://dx.doi.org/10.1145/3453953.3453976.

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This paper initiates the study of online algorithms for the maximum weight b-matching problem, a generalization of maximum weight matching where each node has at most b≥1 adjacent matching edges. The problem is motivated by emerging optical technologies which allow to enhance datacenter networks with reconfigurable matchings, providing direct connectivity between frequently communicating racks. These additional links may improve network performance, by leveraging spatial and temporal structure in the workload. We show that the underlying algorithmic problem features an intriguing connection to online paging (a.k.a. caching), but introduces a novel challenge. Our main contribution is an online algorithm which is O(b)- competitive; we also prove that this is asymptotically optimal. We complement our theoretical results with extensive trace-driven simulations, based on real-world datacenter workloads as well as synthetic traffic traces.

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15

Peters, Jannik. "Online Elicitation of Necessarily Optimal Matchings." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5164–72. http://dx.doi.org/10.1609/aaai.v36i5.20451.

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In this paper, we study the problem of eliciting preferences of agents in the house allocation model. For this we build on a recently introduced model and focus on the task of eliciting preferences to find matchings which are necessarily optimal, i.e., optimal under all possible completions of the elicited preferences. In particular, we investigate the elicitation of necessarily Pareto optimal (NPO) and necessarily rank-maximal (NRM) matchings. Most importantly, we answer an open question and give an online algorithm for eliciting an NRM matching in the next-best query model which is 3/2-competitive, i.e., it takes at most 3/2 as many queries as an optimal algorithm. Besides this, we extend this field of research by introducing two new natural models of elicitation and by studying both the complexity of determining whether a necessarily optimal matching exists in them, and by giving online algorithms for these models.

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16

Anantapantula, Sai, Christopher Melekian, and Eddie Cheng. "Matching Preclusion for the Shuffle-Cubes." Parallel Processing Letters 28, no. 03 (September 2018): 1850012. http://dx.doi.org/10.1142/s0129626418500123.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. A graph is maximally matched if its matching preclusion number is equal to its minimum degree, and is super matched if the matching preclusion number can only be achieved by deleting all edges incident to a single vertex. In this paper, we determine the matching preclusion number and classify the optimal matching preclusion sets for the shuffle-cube graphs, a variant of the well-known hypercubes.

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17

CHENG, EDDIE, and LÁSZLÓ LIPTÁK. "CONDITIONAL MATCHING PRECLUSION FOR (n,k)-STAR GRAPHS." Parallel Processing Letters 23, no. 01 (March 2013): 1350004. http://dx.doi.org/10.1142/s0129626413500047.

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The matching preclusion number of an even graph G, denoted by mp (G), is the minimum number of edges whose deletion leaves the resulting graph without perfect matchings. The conditional matching preclusion number of an even graph G, denoted by mp 1(G), is the minimum number of edges whose deletion leaves the resulting graph with neither perfect matchings nor isolated vertices. The class of (n,k)-star graphs is a popular class of interconnection networks for which the matching preclusion number and the classification of the corresponding optimal solutions were known. However, the conditional version of this problem was open. In this paper, we determine the conditional matching preclusion for (n,k)-star graphs as well as classify the corresponding optimal solutions via several new results. In addition, an alternate proof of the results on the matching preclusion problem will also be given.

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18

WANG, SHUENN-SHYANG, and BOR-SEN CHEN. "Model matching: optimal approximation approach." International Journal of Control 45, no. 6 (June 1987): 1899–907. http://dx.doi.org/10.1080/00207178708933854.

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19

Rosenbaum, Paul R. "Optimal Matching for Observational Studies." Journal of the American Statistical Association 84, no. 408 (December 1989): 1024–32. http://dx.doi.org/10.1080/01621459.1989.10478868.

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20

Yukich, J. E. "Optimal matching and empirical measures." Proceedings of the American Mathematical Society 107, no. 4 (April 1, 1989): 1051. http://dx.doi.org/10.1090/s0002-9939-1989-1000171-8.

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21

Huang, Chun-Hsi, Xin He, and Min Qian. "Communication-optimal parallel parenthesis matching." Parallel Computing 32, no. 1 (January 2006): 14–23. http://dx.doi.org/10.1016/j.parco.2005.07.001.

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22

Ben-Kiki, Oren, Philip Bille, Dany Breslauer, Leszek Ga̧sieniec, Roberto Grossi, and Oren Weimann. "Towards optimal packed string matching." Theoretical Computer Science 525 (March 2014): 111–29. http://dx.doi.org/10.1016/j.tcs.2013.06.013.

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23

Allen, Jeffery C., and John W. Rockway. "Characterizing Optimal Multiport Matching Transformers." Circuits, Systems, and Signal Processing 31, no. 4 (January 14, 2012): 1513–34. http://dx.doi.org/10.1007/s00034-011-9387-5.

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24

Biemann, Torsten, and Deepak K. Datta. "Optimal Matching in Management Research." Academy of Management Proceedings 2012, no. 1 (July 2012): 12910. http://dx.doi.org/10.5465/ambpp.2012.12910abstract.

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25

Greevy, R. "Optimal multivariate matching before randomization." Biostatistics 5, no. 2 (April 1, 2004): 263–75. http://dx.doi.org/10.1093/biostatistics/5.2.263.

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26

Amir, Amihood, Gary Benson, and Martin Farach. "Optimal Two-Dimensional Compressed Matching." Journal of Algorithms 24, no. 2 (August 1997): 354–79. http://dx.doi.org/10.1006/jagm.1997.0860.

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27

Allauzen, Cyril, and Mathieu Raffinot. "Simple Optimal String Matching Algorithm." Journal of Algorithms 36, no. 1 (July 2000): 102–16. http://dx.doi.org/10.1006/jagm.2000.1087.

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28

Cox, Pedro, Henri Maitre, Michel Minoux, and Celso Ribeiro. "Optimal matching of convex polygons." Pattern Recognition Letters 9, no. 5 (June 1989): 327–34. http://dx.doi.org/10.1016/0167-8655(89)90061-5.

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29

Ventura, Jose A., and Jen-Ming Chen. "Optimal matching of nonconvex polygons." Pattern Recognition Letters 14, no. 6 (June 1993): 445–52. http://dx.doi.org/10.1016/0167-8655(93)90023-7.

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30

Coles, Peter, and Ran Shorrer. "Optimal truncation in matching markets." Games and Economic Behavior 87 (September 2014): 591–615. http://dx.doi.org/10.1016/j.geb.2014.01.005.

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31

Cunningham, William H., and James F. Geelen. "The optimal path-matching problem." Combinatorica 17, no. 3 (September 1997): 315–37. http://dx.doi.org/10.1007/bf01215915.

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32

Lesnard, Laurent, and Thibaut de Saint Pol. "Introduction aux méthodes d'appariement optimal (Optimal Matching Analysis)." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 90, no. 1 (April 2006): 5–25. http://dx.doi.org/10.1177/075910630609000103.

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33

CHENG, EDDIE, and OMER SIDDIQUI. "Strong Matching Preclusion of Arrangement Graphs." Journal of Interconnection Networks 16, no. 02 (June 2016): 1650004. http://dx.doi.org/10.1142/s0219265916500043.

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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph with neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem that was introduced by Park and Ihm. The class of arrangement graphs was introduced as a common generalization of the star graphs and alternating group graphs, and to provide an even richer class of interconnection networks. In this paper, the goal is to find the strong matching preclusion number of arrangement graphs and to categorize all optimal strong matching preclusion sets of these graphs.

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34

BONNEVILLE, PHILIP, EDDIE CHENG, and JOSEPH RENZI. "STRONG MATCHING PRECLUSION FOR THE ALTERNATING GROUP GRAPHS AND SPLIT-STARS." Journal of Interconnection Networks 12, no. 04 (December 2011): 277–98. http://dx.doi.org/10.1142/s0219265911003003.

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The strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. This is an extension of the matching preclusion problem and has recently been introduced by Park and Ihm.15 In this paper, we examine properties of strong matching preclusion for alternating group graphs, by finding their strong matching preclusion numbers and categorizing all optimal solutions. More importantly, we prove a general result on taking a Cartesian product of a graph with K2 (an edge) to obtain the corresponding results for split-stars.

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35

Chen, Jiehua, Piotr Skowron, and Manuel Sorge. "Matchings under Preferences: Strength of Stability and Tradeoffs." ACM Transactions on Economics and Computation 9, no. 4 (December 31, 2021): 1–55. http://dx.doi.org/10.1145/3485000.

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We propose two solution concepts for matchings under preferences: robustness and near stability . The former strengthens while the latter relaxes the classical definition of stability by Gale and Shapley (1962). Informally speaking, robustness requires that a matching must be stable in the classical sense, even if the agents slightly change their preferences. Near stability, however, imposes that a matching must become stable (again, in the classical sense) provided the agents are willing to adjust their preferences a bit. Both of our concepts are quantitative; together they provide means for a fine-grained analysis of the stability of matchings. Moreover, our concepts allow the exploration of tradeoffs between stability and other criteria of social optimality, such as the egalitarian cost and the number of unmatched agents. We investigate the computational complexity of finding matchings that implement certain predefined tradeoffs. We provide a polynomial-time algorithm that, given agent preferences, returns a socially optimal robust matching (if it exists), and we prove that finding a socially optimal and nearly stable matching is computationally hard.

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36

Ma, Tianlong, Yaping Mao, Eddie Cheng, and Jinling Wang. "Fractional Matching Preclusion for (n, k)-Star Graphs." Parallel Processing Letters 28, no. 04 (December 2018): 1850017. http://dx.doi.org/10.1142/s0129626418500172.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu introduced the concept of fractional matching preclusion number in 2017. The Fractional Matching Preclusion Number (FMP number) of G is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The Fractional Strong Matching Preclusion Number (FSMP number) of G is the minimum number of vertices and/or edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we obtain the FMP number and the FSMP number for (n, k)-star graphs. In addition, all the optimal fractional strong matching preclusion sets of these graphs are categorized.

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37

Wang, Xia, Tianlong Ma, Jun Yin, and Chengfu Ye. "Fractional matching preclusion for radix triangular mesh." Discrete Mathematics, Algorithms and Applications 11, no. 04 (August 2019): 1950048. http://dx.doi.org/10.1142/s1793830919500484.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu recently introduced the concept of fractional matching preclusion number. The fractional matching preclusion number (FMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The fractional strong matching preclusion number (FSMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices and edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for the radix triangular mesh [Formula: see text], and all the optimal fractional matching preclusion sets and fractional strong matching preclusion sets of these graphs are categorized.

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38

Hosseini, Hadi, Vijay Menon, Nisarg Shah, and Sujoy Sikdar. "Necessarily Optimal One-Sided Matchings." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5481–88. http://dx.doi.org/10.1609/aaai.v35i6.16690.

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We study the classical problem of matching n agents to n objects, where the agents have ranked preferences over the objects. We focus on two popular desiderata from the matching literature: Pareto optimality and rank-maximality. Instead of asking the agents to report their complete preferences, our goal is to learn a desirable matching from partial preferences, specifically a matching that is necessarily Pareto optimal (NPO) or necessarily rank-maximal (NRM) under any completion of the partial preferences. We focus on the top-k model in which agents reveal a prefix of their preference rankings. We design efficient algorithms to check if a given matching is NPO or NRM, and to check whether such a matching exists given top-k partial preferences. We also study online algorithms for eliciting partial preferences adaptively, and prove bounds on their competitive ratio.

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39

Abeywickrama, Tenindra, Victor Liang, and Kian-Lee Tan. "Bipartite Matching." ACM SIGMOD Record 51, no. 1 (May 31, 2022): 51–58. http://dx.doi.org/10.1145/3542700.3542713.

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The Kuhn-Munkres (KM) algorithm is a classical combinatorial optimization algorithm that is widely used for minimum cost bipartite matching in many real-world applications, such as transportation. For example, a ride-hailing service may use it to find the optimal assignment of drivers to passengers to minimize the overall wait time. Typically, given two bipartite sets, this process involves computing the edge costs between all bipartite pairs and finding an optimal matching. However, existing works overlook the impact of edge cost computation on the overall running time. In reality, edge computation often significantly outweighs the computation of the optimal assignment itself, as in the case of assigning drivers to passengers which involves computation of expensive graph shortest paths. Following on from this, we also observe common real-world settings exhibit a useful property that allows us to incrementally compute edge costs only as required using an inexpensive lower-bound heuristic. This technique significantly reduces the overall cost of assignment compared to the original KM algorithm, as we demonstrate experimentally on multiple real-world data sets and workloads. Moreover, our algorithm is not limited to this domain and is potentially applicable in other settings where lower-bounding heuristics are available.

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40

Reny, Philip J. "Efficient Matching in the School Choice Problem." American Economic Review 112, no. 6 (June 1, 2022): 2025–43. http://dx.doi.org/10.1257/aer.20210240.

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Stable matchings in school choice needn’t be Pareto efficient and can leave thousands of students worse off than necessary. Call a matching μ priority-neutral if no matching can make any student whose priority is violated by μ better off without violating the priority of some student who is made worse off. Call a matching priority-efficient if it is priority-neutral and Pareto efficient. We show that there is a unique priority-efficient matching and that it dominates every priority-neutral matching and every stable matching. Moreover, truth-telling is a maxmin optimal strategy for every student in the mechanism that selects the priority-efficient matching. (JEL C78, I21, I28)

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41

Abbott, Andrew, and John Forrest. "Optimal Matching Methods for Historical Sequences." Journal of Interdisciplinary History 16, no. 3 (1986): 471. http://dx.doi.org/10.2307/204500.

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42

van Sambeeck, J. H. J., S. P. J. van Brummelen, N. M. van Dijk, and M. P. Janssen. "Optimal blood issuing by comprehensive matching." European Journal of Operational Research 296, no. 1 (January 2022): 240–53. http://dx.doi.org/10.1016/j.ejor.2021.02.054.

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43

Cologne, John B., and Yoshisada Shibata. "OPTIMAL CASE-CONTROL MATCHING IN PRACTICE." Epidemiology 6, no. 3 (May 1995): 271–75. http://dx.doi.org/10.1097/00001648-199505000-00014.

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44

Zohdy, M., Nan Loh, and A. A. Abdul-Wahab. "A robust optimal model matching control." IEEE Transactions on Automatic Control 32, no. 5 (May 1987): 410–14. http://dx.doi.org/10.1109/tac.1987.1104617.

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45

Jiancang, Su, Sun Jian, Liu Guozhi, Liu Chunliang, and Ding Zhenjie. "Optimal Matching of Magnetic Pulse Compressor." Plasma Science and Technology 8, no. 2 (March 2006): 229–33. http://dx.doi.org/10.1088/1009-0630/8/2/22.

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46

Shayevitz, Ofer, and Meir Feder. "Optimal Feedback Communication Via Posterior Matching." IEEE Transactions on Information Theory 57, no. 3 (March 2011): 1186–222. http://dx.doi.org/10.1109/tit.2011.2104992.

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47

Yang, C. D., H. Y. Chung, and J. L. Chang. "Optimal model matching: H2 or H∞?" Electronics Letters 26, no. 25 (1990): 2089. http://dx.doi.org/10.1049/el:19901346.

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48

Roumy, Aline, and David Gesbert. "Optimal Matching in Wireless Sensor Networks." IEEE Journal of Selected Topics in Signal Processing 1, no. 4 (December 2007): 725–35. http://dx.doi.org/10.1109/jstsp.2007.909378.

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49

Vishkin, Uzi. "Optimal parallel pattern matching in strings." Information and Control 67, no. 1-3 (October 1985): 91–113. http://dx.doi.org/10.1016/s0019-9958(85)80028-0.

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50

Galil, Zvi. "Optimal parallel algorithms for string matching." Information and Control 67, no. 1-3 (October 1985): 144–57. http://dx.doi.org/10.1016/s0019-9958(85)80031-0.

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