[ Algorithm ] 08. Minimum Spanning Trees

38A·2023년 5월 20일
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🖥️ MST

Spanning Tree : Spanning graph + Tree(acycle)

  • Problem: given a connected, undirected, weighted graph, find a spanning tree using edges that minimize the total weight
  • Undirected graph G = (V, E)
  • Weight w(u,v) on each edge (u,v) \in E
  • Spanning tree of G’ is a minimal subgraph of G such that
    • V(G’) = V(G) and G’ is connected
    • Any connected graph with n vertices must have at least n-1 edges. All connected graphs with n-1 edges are trees.
      ➡️ connected + n vertices + n-1 edges : cycle이 없음을 의미
  • Find T \subseteq E s.t.
    • T connects all vertices (T is a spanning tree), and
    • w(T) = (u,v)E\sum_{(u,v) \in E} w(u,v)(모든 edge weight의 합) is minimized
  • Has n -1 edges, no cycle, might not be unique
  • Example)
    Interconnect n pins with n-1 wires, each connecting two pins so that we use the least amount of wire.
  • We’ll look at two greedy algorithms
    • Kruskal’s algorithm
    • Prim’s algorithm

Building up the solution

  • Build a set A of edges
  • Initially, A is empty
  • As we add edges to A(= n-1, exit), maintain a loop invariant : A is a subset of some MST
  • Add only edges that maintain the invariant(불변성)
    • If A is a subset of some MST, an edge (u,v) is safe for A if and only if A U { (u,v) } is also a subset of some MST
    • So, we will add only safe edges

Generic MST algorithm➡️ safe : looks best (smallest or largest)

Finding a safe edge

  • Let S \subset V and A \subseteq E
    • A cut (S, V – S) is a partition of vertices into disjoint sets S and V – S
    • Edge (u, v) \in E crosses cut (S, V – S ) if one endpoint is in S and the other is in V – S
    • A cut respects A if and only if no edge in A crosses the cut
      ➡️ cut을 지나가지 않음
    • An edge is a light edge crossing a cut if and only if its weight is minimum over all edges crossing the cut. For a given cut, there can be more than one light edge crossing it (값이 같으면 여러개)

Theorem

Let A be a subset of E in some MST, (S, V – S) be a cut that respects A, and (u, v) be a light edge crossing (S, V – S )
Then, (u, v) is safe for A

Proof)
1. Let T be a MST that includes A, and assume that T does not contain the light edge (u, v)
2. We shall construct another MST T ́ that includes A U { (u,v) } .
3. Edge (u,v) forms a cycle with the edges on the path p from u to v in T
➡️ n -1 edge에서 n개가 되기 떄문
4. Since u and v are on opposite sides of the cut (S, V – S) there is at least one edge in T on the path p that also crosses the cut. Let (x,y) be any such edge.
5. Removing (x,y) breaks T into two components. And adding (u,v) reconnects them to form a new spanning tree T ́
w(T ́) = w(T) – w(x,y) + w(u,v) ≤ w(T)
Thus, T ́ must be a MST
And since A U { (u,v) } \subseteq T ́, (u,v) is safe for A

Corollary

Let A be a subset of E that is included in some minimum spanning tree for G, and let C = (Vc_c, Ec_c) be a connected component (tree) in the forest GA_A = (V, A). If (u,v) is a light edge connecting C to some other component in GA_A, then (u,v) is safe for A

Proof) The cut (Vc, V – Vc) respects A, and (u,v) is a light edge for this cut. Therefore, (u,v) is safe for A.

🖥️ Kruskal’s algorithm

  • Sort edges into nondecreasing(increasing) order by w.
  • The algorithm maintains A, a forest of trees ➡️ 마지막에 big one tree
  • Repeatedly merges two components into one by choosing the light edge that connects them
    i.e.,
    1. Choose the light edge crossing the cut between them.
    2. (If it forms a cycle, the edge is discarded)
  • What is the design strategy of Kruskal’s algorithm?
    • Greedy

pseudo-code

Example

🖥️ Prim's algorithm

  • Builds one tree, so A is always a tree (cf. kruskal is forest)
  • Start from an arbitrary “root” r
  • At each step, find a light edge crossing cut (VA_A, V – VA_A), where VA_A = vertices that A is incident on
  • π\pi[v] = parent of v, NIL if it has no parent or v = r.
  • To find a light edge quickly
    • use a priority queue Q

Outline

pseudo code

Exercise

🖥️ Exercise in class

Data structure used for Prim's algorithm

A .Priority Queue ➡️ Greedy 유추가능
Greedy는 PQ만 사용? ➡️ No, sorted arr vs. PQ. : key값이 바뀌면 array는 다시 sort(nlgn), but PQ는 lgn

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