Prim Algorithm
How does it run?
- Choose an arbitrary vertex $v$ and let $S={v}$
- Choose a vertex $u$ closest to $S$ and add it into $S$ and record the corresponding edge
- go on until all vertices are in $S$
- The recorded edges and $S$ constructs a Minimum Spanning Tree
Let $G=(V,E,W)$ be a weithted connected graph and has $n$ vertices.
$T$ is a tree built through Prim Algorithm.
Show $T$ is a minimum spanning tree:
First, there must be an optimal solution which contains the edge with the minimal weight.
If not, we can add this edge into the solution and obtain a cycle, then remove an edge with larger weight and we can obtain a better solution.
Recursively, if $e_1,e_2,\cdots,e_k$ are in a part of some optimal solution, then $e_{k+1}$ is also in it.
Kruskal Algorithm
The main idea of Kruskal Algorithm is linking different connected components continuously with the edge with minimal weight, until there is only one connected componen remains.
Procedure:
- Each vertex constructs a single connected component
- linking
- finish
Rightness:
Denote the result of Kruskal Algorithm by $T$ and the Minimal Spanning Tree by $T_{min}$
Assume that there are $k$ different edges between $T$ and $T_{min}$ (permutated by weight increasingly)
$$
\begin{aligned}
T:& a_1,a_2,\cdots,a_k\\
T_{min}:& b_1,b_2,\cdots,b_k
\end{aligned}
$$
Add $a_1$ into $T_{min}$ and it leads to a cycle including some of $b_1,b_2,\cdots,b_k$
Remove the most weighted edge $b$ in this cycle and belonging to $b_1,b_2,\cdots,b_k$
Then obtain a new spanning tree $T_1$
Edge $a_1$ and edge $b$ must have the same weight
- Since $T_{min}$ is the minimum spanning tree, $W_{a_1}\ge W_b$
- Since $b$ is not in $T$, according to the rule of Kruskal Algorithm, there are 2 cases:
- $b$ Leads to cycle with vertices added already (since $a_1$ has the minimal weight in $a_i$s, those vertices should be the common vertices of $T$ and $T_{min}$), but is impossible because $b$ is exactly in $T_{min}$
- or the second case, the weight of $b$ is no less than $a_1$ and would be considered after $a_1$
- Then it’s clear, $W_{a_1}=W_b$
And $T_1$ is also a minimum spanning tree
Following this way, we can convert $T_{min}$ into $T$ with keeping its weight
Hence $T$ is also a minimum spanning tree
Intresting
MST is the short of “Minimum Spanning Tree”.
Why not “Minimal Spanning Tree”?
One kind of explanation:
- minimum is a quantitative representation of the smallest amount needed
- minimal is a is a qualitative characteristic