<p>Randomly sampling an acyclic orientation on the complete bipartite graph <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="26_2025_741_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="34" /> </InlineMediaObject> <EquationSource Format="TEX">\(K_{n,k}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>K</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> </math></EquationSource> </InlineEquation> with parts of size <i>n</i> and <i>k</i>, we investigate the length of the longest path. We provide a probability generating function for the distribution of the longest path length, and we use analytic combinatorics to perform asymptotic analysis of the probability distribution in the case of equal part sizes <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="26_2025_741_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">\(n=k\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>n</mi> <mo>=</mo> <mi>k</mi> </mrow> </math></EquationSource> </InlineEquation> tending toward infinity. We show that the distribution is asymptotically Gaussian, and we obtain precise asymptotics for the mean and variance. These results address a question asked by Peter J. Cameron.</p>

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The Distribution of the Length of the Longest Path in Random Acyclic Orientations of a Complete Bipartite Graph

  • Jessica Khera,
  • Erik Lundberg

摘要

Randomly sampling an acyclic orientation on the complete bipartite graph \(K_{n,k}\) K n , k with parts of size n and k, we investigate the length of the longest path. We provide a probability generating function for the distribution of the longest path length, and we use analytic combinatorics to perform asymptotic analysis of the probability distribution in the case of equal part sizes \(n=k\) n = k tending toward infinity. We show that the distribution is asymptotically Gaussian, and we obtain precise asymptotics for the mean and variance. These results address a question asked by Peter J. Cameron.