Length performance.
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tiffUpdated May 30, 2023
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EmailClick to copy linkLink copiedCiteFabrizio Altarelli; Alfredo Braunstein; Luca Dall’Asta; Caterina De Bacco; Silvio Franz (2023). Length performance. [Dataset]. http://doi.org/10.1371/journal.pone.0145222.g007tiffAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0145222.g007Dataset updatedMay 30, 2023AuthorsFabrizio Altarelli; Alfredo Braunstein; Luca Dall’Asta; Caterina De Bacco; Silvio FranzLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionWe plot (left) the relative performance of MSG over MP in terms of total length of the solution paths: y = 100(Lg/LMP − 1). Here Lg and LMP denote the total path lengths calculated with MSG and MP respectively. We use Reg, RER, ER and SF graphs of fixed size V = 103 and average degree 〈k〉 = 3,5,7 (from top to bottom). On the right we report the number of instances where the two algorithms find the same solution in term of Macc/M over 100 realizations.
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FacebookTwitterEmailAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
We plot (left) the relative performance of MSG over MP in terms of total length of the solution paths: y = 100(Lg/LMP − 1). Here Lg and LMP denote the total path lengths calculated with MSG and MP respectively. We use Reg, RER, ER and SF graphs of fixed size V = 103 and average degree 〈k〉 = 3,5,7 (from top to bottom). On the right we report the number of instances where the two algorithms find the same solution in term of Macc/M over 100 realizations.