minimum spanning trees Algorithm

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4.5. Minimum Spanning Trees
•Definition of MST
•Generic MST algorithm
•Kruskal's algorithm
•Prim's algorithm

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Definition of MST
• Let G=(V,E) be a connected, undirected graph.
• For each edge (u,v) in E, we have a weight w(u,v) specifying
the cost (length of edge) to connect u and v.
• We wish to find a (acyclic) subset T of E that connects all of
the vertices in V and whose total weight is minimized.
• Since the total weight is minimized, the subset T must be
acyclic (no circuit).
• Thus, T is a tree. We call it a spanning tree.
• The problem of determining the tree T is called the
minimum-spanning-tree problem.

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Application of MST: an example
• In the design of electronic circuitry, it is often
necessary to make a set of pins electrically
equivalent by wiring them together.
• To interconnect n pins, we can use n-1 wires, each
connecting two pins.
• We want to minimize the total length of the wires.
• Minimum Spanning Trees can be used to model
this problem.

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Here is an example of a connected graph
and its minimum spanning tree:
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Notice that the tree is not unique:
replacing (b,c) with (a,h) yields another spanning tree
with the same minimum weight.

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Growing a MST(Generic Algorithm)
• Set A is always a subset of some minimum spanning tree.
This property is called the invariant Property.
• An edge (u,v) is a safe edge for A if adding the edge to A
does not destroy the invariant.
• A safe edge is just the CORRECT edge to choose to add to
T.
GENERIC_MST(G,w)
1 A:={}
2 while A does not form a spanning tree do
3 find an edge (u,v) that is safe for A
4 A:=A∪{(u,v)}
5 return A

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How to find a safe edge
We need some definitions and a theorem.
• A cut (S,V-S) of an undirected graph G=(V,E) is a
partition of V.
• An edge crosses the cut (S,V-S) if one of its
endpoints is in S and the other is in V-S.
• An edge is a light edge crossing a cut if its weight
is the minimum of any edge crossing the cut.

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V-S↓
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S↑ ↑ S
↓ V-S
• This figure shows a cut (S,V-S) of the
graph.
• The edge (d,c) is the unique light edge
crossing the cut.

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Theorem 1
• Let G=(V,E) be a connected, undirected graph with a real-
valued weight function w defined on E.
• Let A be a subset of E that is included in some minimum
spanning tree for G.
• Let (S,V-S) be any cut of G such that for any edge (u, v) in
A, {u, v} ⊆ S or {u, v} ⊆ (V-S).
• Let (u,v) be a light edge crossing (S,V-S).
• Then, edge (u,v) is safe for A.
• Proof: Let Topt be a minimum spanning tree.(Blue)
• A --a subset of Topt and (u,v)-- a light edge crossing (S, V-S)
• If (u,v) is NOT safe, then (u,v) is not in T. (See the red edge in Figure)
• There MUST be another edge (u’, v’) in Topt crossing (S, V-S).(Green)
• We can replace (u’,v’) in Topt with (u, v) and get another treeT’opt
• Since (u, v) is light (the shortest edge connect crossing (S, V-S), T’opt is
also optimal. 1
2
1
1 1
1 1
1

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Corollary .2
• Let G=(V,E) be a connected, undirected graph with a real-
valued weight function w defined on E.
• Let A be a subset of E that is included in some minimum
spanning tree for G.
• Let C be a connected component (tree) in the forest
GA=(V,A).
• Let (u,v) be a light edge (shortest among all edges
connecting C with others components) connecting C to
some other component in GA.
• Then, edge (u,v) is safe for A. (For Kruskal’s algorithm)

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The algorithms of Kruskal and Prim
• The two algorithms are elaborations of the generic
algorithm.
• They each use a specific rule to determine a safe
edge in line 3 of GENERIC_MST.
• In Kruskal's algorithm,
– The set A is a forest.
– The safe edge added to A is always a least-weight edge
in the graph that connects two distinct components.
• In Prim's algorithm,
– The set A forms a single tree.
– The safe edge added to A is always a least-weight edge
connecting the tree to a vertex not in the tree.

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Kruskal's algorithm(basic part)
1 (Sort the edges in an increasing order)
2 A:={}
3 while E is not empty do {
3 take an edge (u, v) that is shortest in E
and delete it from E
4 if u and v are in different components then
add (u, v) to A
}
Note: each time a shortest edge in E is considered.

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Kruskal's algorithm (Fun Part, not required)
MST_KRUSKAL(G,w)
1 A:={}
2 for each vertex v in V[G]
3 do MAKE_SET(v)
4 sort the edges of E by nondecreasing weight w
5 for each edge (u,v) in E, in order by
nondecreasing weight
6 do if FIND_SET(u) != FIND_SET(v)
7 then A:=A {(u,v)}∪
8 UNION(u,v)
9 return A
(Disjoint set is discussed in Chapter 21, Page 498)

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Disjoint-Set (Chapter 21, Page 498)
• Keep a collection of sets S1, S2, .., Sk,
– Each Si is a set, e,g, S1={v1, v2, v8}.
• Three operations
– Make-Set(x)-creates a new set whose only member is x.
– Union(x, y) –unites the sets that contain x and y, say, Sx
and Sy, into a new set that is the union of the two sets.
– Find-Set(x)-returns a pointer to the representative of the
set containing x.
– Each operation takes O(log n) time.

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• Our implementation uses a disjoint-set data
structure to maintain several disjoint sets of
elements.
• Each set contains the vertices in a tree of the
current forest.
• The operation FIND_SET(u) returns a
representative element from the set that contains u.
• Thus, we can determine whether two vertices u
and v belong to the same tree by testing whether
FIND_SET(u) equals FIND_SET(v).
• The combining of trees is accomplished by the
UNION procedure.
• Running time O(|E| lg (|E|)). (The analysis is not required.)

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a
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The execution of Kruskal's algorithm (Moderate part)
•The edges are considered by the algorithm in sorted
order by weight.
•The edge under consideration at each step is shown
with a red weight number.

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a
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c d
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a
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a
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a
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a
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a
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a
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a
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a
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a
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c d
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Prim's algorithm(basic part)
MST_PRIM(G,w,r)
1. A={}
2. S:={r} (r is an arbitrary node in V)
3. Q=V-{r};
4. while Q is not empty do {
5 take an edge (u, v) such that (1) u ∈S and v ∈ Q (v∉ S ) and
(u, v) is the shortest edge satisfying (1)
6 add (u, v) to A, add v to S and delete v from Q
}

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Prim's algorithm
MST_PRIM(G,w,r)
1 for each u in Q do
2 key[u]:=∞
3 parent[u]:=NIL
4 key[r]:=0; parent[r]=NIL;
5 Q←V[Q]
6 while Q!={} do
7 u:=EXTRACT_MIN(Q); if parent[u]≠Nil print (u, parent[u])
8 for each v in Adj[u] do
9 if v in Q and w(u,v)<key[v]
10 then parent[v]:=u
11 key[v]:=w(u,v)

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• Grow the minimum spanning tree from the root
vertex r.
• Q is a priority queue, holding all vertices that are
not in the tree now.
• key[v] is the minimum weight of any edge
connecting v to a vertex in the tree.
• parent[v] names the parent of v in the tree.
• When the algorithm terminates, Q is empty; the
minimum spanning tree A for G is thus
A={(v,parent[v]):v∈V-{r}}.
• Running time: O(||E||lg |V|).

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a
b
h
c d
e
fg
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4
8 7
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144
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a
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c d
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The execution of Prim's algorithm(moderate part)
the root
vertex

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a
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a
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a
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a
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a
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Bottleneck spanning tree: A spanning tree of G whose
largest edge weight is minimum over all spanning trees of G.
The value of the bottleneck spanning tree is the weight of the
maximum-weight edge in T.
Theorem: A minimum spanning tree is also a bottleneck
spanning tree. (Challenge problem)

minimum spanning trees Algorithm

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minimum spanning trees Algorithm