Abstract <p> The problem of finding a minimum spanning tree (MST) on an arbitrary set of <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(n\)</EquationSource> </InlineEquation> points in <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\)</EquationSource> </InlineEquation>-space with the <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(l_1\)</EquationSource> </InlineEquation>-norm is considered. It is known that, for each fixed <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq5.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="41" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\ge 2\)</EquationSource> </InlineEquation>, there exists an algorithm solving this problem in time <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq6.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="207" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(n(\log n+\log^{r_d}n\log\log n))\)</EquationSource> </InlineEquation>, where <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq7.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="112" /> </InlineMediaObject> <EquationSource Format="TEX">\(r_d\in\{0,1, 2,4\}\)</EquationSource> </InlineEquation> if <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq8.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="106" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\in \{2,3,4,5\}\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq9.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="50" /> </InlineMediaObject> <EquationSource Format="TEX">\(r_d=d\)</EquationSource> </InlineEquation> if <InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq10.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="41" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\ge 6\)</EquationSource> </InlineEquation>. For <InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq11.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="40" /> </InlineMediaObject> <EquationSource Format="TEX">\(d=3\)</EquationSource> </InlineEquation>, the complexity can be improved to <InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq12.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="73" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(n\log n)\)</EquationSource> </InlineEquation>. In the paper, for any fixed <InlineEquation ID="IEq13"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq13.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="41" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\geq 2\)</EquationSource> </InlineEquation>, an algorithm of complexity <InlineEquation ID="IEq14"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq14.gif" Format="GIF" Height="22" Rendition="HTML" Resolution="72" Type="Linedraw" Width="95" /> </InlineMediaObject> <EquationSource Format="TEX">\(O(n\log^{d-1} n)\)</EquationSource> </InlineEquation> solving the MST problem under consideration is proposed, which improves the existing algorithms for <InlineEquation ID="IEq15"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11006_2025_3081_Article_IEq15.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="41" /> </InlineMediaObject> <EquationSource Format="TEX">\(d\geq 6\)</EquationSource> </InlineEquation>. </p>

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Efficient Search for a Minimum Tree in a Space with the \(l_1\)-Norm

  • K. V. Kaymakov,
  • D. S. Malyshev

摘要

Abstract

The problem of finding a minimum spanning tree (MST) on an arbitrary set of \(n\) points in \(d\) -space with the \(l_1\) -norm is considered. It is known that, for each fixed \(d\ge 2\) , there exists an algorithm solving this problem in time \(O(n(\log n+\log^{r_d}n\log\log n))\) , where \(r_d\in\{0,1, 2,4\}\) if \(d\in \{2,3,4,5\}\) and \(r_d=d\) if \(d\ge 6\) . For \(d=3\) , the complexity can be improved to \(O(n\log n)\) . In the paper, for any fixed \(d\geq 2\) , an algorithm of complexity \(O(n\log^{d-1} n)\) solving the MST problem under consideration is proposed, which improves the existing algorithms for \(d\geq 6\) .