Abstract
The maximum matching problem is among the most well-studied problems in combinatorial optimization with many applications. The matching problem is well-known to be efficiently solvable, that is, there are algorithms that solve the matching problem using polynomial space and time. However, as large data sets become more prevalent, there is a growing interest in sublinear algorithms — these are algorithms whose resource requirements are substantially smaller than the size of the input that they operate on. In this talk, we will describe some results that illustrate surprising effectiveness of randomization in solving exact and approximate matching problems in sublinear space or time.
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Khanna, S. (2014). Matchings, Random Walks, and Sampling. In: Dediu, AH., Martín-Vide, C., Sierra-Rodríguez, JL., Truthe, B. (eds) Language and Automata Theory and Applications. LATA 2014. Lecture Notes in Computer Science, vol 8370. Springer, Cham. https://doi.org/10.1007/978-3-319-04921-2_3
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DOI: https://doi.org/10.1007/978-3-319-04921-2_3
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