Borůvka's algorithm: Difference between revisions

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{{Short description|Method for finding minimum spanning trees}}

[[File:Boruvka's algorithm (Sollin's algorithm) Anim.gif|thumb|upright=1.35|Animation of Boruvka's algorithm]]

{{Infobox Algorithm

{{graph search algorithm}}

|image=[[File:Boruvka's algorithm (Sollin's algorithm) Anim.gif|thumbframeless|upright=1.35|Animation of Boruvka's algorithm]]

|caption=Animation of Borůvka's algorithm

'''Borůvka's algorithm''' is a [[greedy algorithm]] for finding a [[minimum spanning tree]] in a graph for which all edge weights are distinct,

|class=[[Minimum spanning tree|Minimum spanning tree algorithm]]

|data=[[Graph (abstract data type)|Graph]]

|time=<math>O(|E|\log |V|)</math>

}}

'''Borůvka's algorithm''' is a [[greedy algorithm]] for finding a [[minimum spanning tree]] in a graph for which all edge weights are distinct,

or a minimum spanning forest in the case of a graph that is not connected.

It was first published in 1926 by [[Otakar Borůvka]] as a method of constructing an efficient [[electricity network]] for [[Moravia]].<ref>{{cite journal | last = Borůvka | first = Otakar | authorlinkauthor-link = Otakar Borůvka | year = 1926 | title = O jistém problému minimálním |trans-title=About a certain minimal problem | journal = Práce Mor. Přírodověd. Spol. V Brně III | volume = 3 | pages = 37–58 | url = https://dml.cz/handle/10338.dmlcz/500114 | language = Czechcs, Germande}}</ref><ref>{{cite journal | last = Borůvka | first = Otakar | authorlinkauthor-link = Otakar Borůvka | year = 1926 | title = Příspěvek k řešení otázky ekonomické stavby elektrovodních sítí (Contribution to the solution of a problem of economical construction of electrical networks) | journal = Elektronický Obzor | volume = 15 | pages = 153&ndash;154 | language = Czechcs }}</ref><ref>{{cite journal

| last1 = Nešetřil | first1 = Jaroslav | author1-link = Jaroslav Nešetřil

| last2 = Milková | first2 = Eva

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| hdl-access = free

}}</ref>

The algorithm was rediscovered by [[Gustave Choquet|Choquet]] in 1938;<ref>{{cite journal | last = Choquet | first = Gustave | authorlinkauthor-link = Gustave Choquet | year = 1938 | title = Étude de certains réseaux de routes | journal = Comptes Rendus de l'Académie des Sciences | volume = 206 | pages = 310&ndash;313 | language = Frenchfr }}</ref> again by [[Kazimierz Florek|Florek]], [[Jan Łukasiewicz|Łukasiewicz]], [[Julian Perkal|Perkal]], [[Hugo Steinhaus|Steinhaus]], and [[Stefan Zubrzycki|Zubrzycki]] in 1951;<ref>{{cite journal

| last1 = Florek | first1 = K.

| last2 = Łukaszewicz | first2 = J. | author2-link = Jan Łukasiewicz

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| last4 = Steinhaus | first4 = Hugo | author4-link = Hugo Steinhaus

| last5 = Zubrzycki | first5 = S.

| journal = Colloquium MathematicaeMathematicum

| language = Frenchfr

| mr = 0048832

| pages = 282–285

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| url = https://eudml.org/doc/209969

| volume = 2

| year = 1951| issue = 3–4

| year = 1951}}</ref> and again by Georges Sollin in 1965.<ref>{{cite journal | last = Sollin | first = Georges | year = 1965 | title = Le tracé de canalisation | journal = Programming, Games, and Transportation Networks | language = French }}</ref> This algorithm is frequently called '''Sollin's algorithm''', especially in the [[parallel computing]] literature.

| doi = 10.4064/cm-2-3-4-282-285

| year = 1951}}</ref> and again by Georges Sollin in 1965.<ref>{{cite journal | last = Sollin | first = Georges | year = 1965 | title = Le tracé de canalisation | journal = Programming, Games, and Transportation Networks | language = Frenchfr }}</ref> This algorithm is frequently called '''Sollin's algorithm''', especially in the [[parallel computing]] literature.

The algorithm begins by finding the minimum-weight edge incident to each vertex of the graph, and adding all of those edges to the forest.

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== Pseudocode ==

Designating each vertex or set of connected vertices a "[[connected component (graph theory)|component]]", pseudocode for Borůvka's algorithm is:

The following pseudocode illustrates a basic implementation of Borůvka's algorithm.

In the conditional clauses, every edge ''uv'' is considered cheaper than "None". The purpose of the ''completed'' variable is to determine whether the forest ''F'' is yet a spanning forest.

If edges do not have distinct weights, then a consistent '''tie-breaking rule''' must be used, (e.g. breakingbased tieson bysome the[[total objectorder]] identifierson ofvertices theor edges) can be used.

This can be achieved by representing vertices as integers and comparing them directly; comparing their [[memory address]]es; etc.

A tie-breaking rule is necessary to ensure that the created graph is indeed a forest, that is, it does not contain cycles. For example, consider a triangle graph with nodes {''a'',''b'',''c''} and all edges of weight 1. Then a cycle could be created if we select ''ab'' as the minimal weight edge for {''a''}, ''bc'' for {''b''}, and ''ca'' for {''c''}.

A tie-breaking rule which orders edges first by source, then by destination, will prevent creation of a cycle, resulting in the minimal spanning tree {''ab'', ''bc''}.

'''algorithm''' Borůvka '''is'''

'''input:''' A weighted undirected graph ''G'' whose= edges have(''V'', distinct weights''E'').

'''output:''' ''F'', is thea minimum spanning forest of ''G''.

Initialize a forest ''F'' to be a set of one-vertex trees(''V'', one for each''{{prime|E}}'') vertexwhere of''{{prime|E}}'' the= graph{}.

''completed'while''' ''F'' has more than one component:= '''dofalse'''

'''while''' not ''completed'' '''do'''

Find the [[Component_(graph_theory)|connected componentscomponent]]s of ''F'' and labelassign to each vertex of ''G'' by its component

Initialize the cheapest edge for each component to "None"

'''for each''' edge ''uv'' ofin ''GE'', where ''u'' and ''v'' are in different components of 'do'F'':

'''if'''let ''uwx'' andbe ''v''the havecheapest differentedge for the component labels:of ''u''

'''if''' is-preferred-over(''uv'' is cheaper than the cheapest edge for the component of, ''uwx'') '''then'''

Set ''uv'' as the cheapest edge for the component of ''u''

let '''if'yz'' ''uv'' is cheaper thanbe the cheapest edge for the component of ''v'' '''then'''

'''if''' Set is-preferred-over(''uv'', as the cheapest edge for the component of''yz'') ''v'then'''

'''for each Set ''uv'' componentas whosethe cheapest edge isfor notthe "None"component of '''do'v''

'''if''' all components Add itshave cheapest edge set to "None" '''then'F''

''// no more trees can be merged -- we are finished''

''completed'' := '''true'''

'''else'''

''completed'' := '''false'''

'''for each''' component whose cheapest edge is not "None" '''do'''

Add its cheapest edge to ''E'''

'''function''' is-preferred-over(''edge1'', ''edge2'') '''is'''

'''return''' (''edge2'' is "None") or

(weight(''edge1'') < weight(''edge2'')) or

(weight(''edge1'') = weight(''edge2'') and tie-breaking-rule(''edge1'', ''edge2''))

'''function''' tie-breaking-rule(''edge1'', ''edge2'') '''is'''

The tie-breaking rule; returns '''true''' if and only if ''edge1''

is preferred over ''edge2'' in the case of a tie.

AnAs an optimization, (notone necessary for the analysis) is tocould remove from ''G'' each edge that is found to connect two vertices in the same component, asso eachthat it does not contribute to the time for searching for cheapest edges in later othercomponents.

If edges do not have distinct weights, then a consistent tie-breaking rule (e.g. breaking ties by the object identifiers of the edges) can be used.

An optimization (not necessary for the analysis) is to remove from ''G'' each edge that is found to connect two vertices in the same component as each other.

== Complexity ==

Borůvka's algorithm can be shown to take {{math|[[Big O notation|O]](log ''V'')}} iterations of the outer loop until it terminates, and therefore to run in time {{math|[[Big O notation|O]](''E'' log ''V'')}}, where ''{{mvar|E''}} is the number of edges, and ''{{mvar|V''}} is the number of vertices in {{mvar|G}} (assuming {{math|''GE'' ≥ ''V''}}). In [[planar graph]]s, and more generally in families of graphs closed under [[graph minor]] operations, it can be made to run in linear time, by removing all but the cheapest edge between each pair of components after each stage of the algorithm.<ref>{{Cite book|last=Eppstein|first=David|authorlinkauthor-link=David Eppstein|contribution=Spanning trees and spanners|title=Handbook of Computational Geometry|editor1-first=J.-R.|editor1-last=Sack|editor1-link=Jörg-Rüdiger Sack|editor2-first=J.|editor2-last=Urrutia|editor2-link= Jorge Urrutia Galicia|publisher=Elsevier|year=1999|pages=425–461|postscript=<!--None-->}}; {{Cite journal|last=Mareš|first=Martin|title=Two linear time algorithms for MST on minor closed graph classes|journal=Archivum Mathematicum|volume=40|year=2004|issue=3|pages=315–320|url=http://www.emis.de/journals/AM/04-3/am1139.pdf|postscript=<!--None-->}}.</ref>

== Example ==

{| border=1 cellspacing=2 cellpadding=5 class="wikitable"

! | Image

! width="100" | components

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== Other algorithms ==

Other algorithms for this problem include [[Prim's algorithm]] and [[Kruskal's algorithm]]. Fast parallel algorithms can be obtained by combining Prim's algorithm with Borůvka's.<ref>{{cite journal|last1=Bader|first1=David A.|last2=Cong|first2=Guojing|title=Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs|journal=Journal of Parallel and Distributed Computing|date=2006|volume=66|issue=11|pages=1366–1378|doi=10.1016/j.jpdc.2006.06.001|citeseerx=10.1.1.129.8991|s2cid=2004627}}</ref>

A faster randomized minimum spanning tree algorithm based in part on Borůvka's algorithm due to Karger, Klein, and Tarjan runs in expected {{math|O(''E'')}} time.<ref>{{cite journal|last1=Karger|first1=David R.|last2=Klein|first2=Philip N.|last3=Tarjan|first3=Robert E.|title=A randomized linear-time algorithm to find minimum spanning trees|journal=Journal of the ACM|date=1995|volume=42|issue=2|pages=321–328|doi=10.1145/201019.201022|citeseerx=10.1.1.39.9012|s2cid=832583}}</ref> The best known (deterministic) minimum spanning tree algorithm by [[Bernard Chazelle]] is also based in part on Borůvka's and runs in {{math|O(''E'' α(''E'',''V''))}} time, where α is the inverse of the [[Ackermann function#Inverse|inverse Ackermann function]].<ref>{{Cite journal|last=Chazelle|first=Bernard|title=A minimum spanning tree algorithm with inverse-Ackermann type complexity|journal=J. ACM|volume=47|year=2000|issue=6|pages=1028–1047|url=http://www.cs.princeton.edu/~chazelle/pubs/mst.pdf|doi=10.1145/355541.355562|citeseerx=10.1.1.115.2318|s2cid=6276962}}</ref> These randomized and deterministic algorithms combine steps of Borůvka's algorithm, reducing the number of components that remain to be connected, with steps of a different type that reduce the number of edges between pairs of components.

==Notes==

<references/>

{{Graph traversal algorithms}}

{{DEFAULTSORT:Boruvka's Algorithm}}

[[Category:Graph algorithms]]

[[Category:Spanning tree]]