# Kruskal’s Minimum Spanning Tree using STL in C++

Given an undirected, connected and weighted graph, find **M**inimum **S**panning **T**ree (MST) of the graph using Kruskal’s algorithm.

Input : Graph as an array of edges Output : Edges of MST are 6 - 7 2 - 8 5 - 6 0 - 1 2 - 5 2 - 3 0 - 7 3 - 4 Weight of MST is 37Note :There are two possible MSTs, the other MST includes edge 1-2 in place of 0-7.

We have discussed below Kruskal’s MST implementations.

Greedy Algorithms | Set 2 (Kruskal’s Minimum Spanning Tree Algorithm)

Below are the steps for finding MST using Kruskal’s algorithm

- Sort all the edges in non-decreasing order of their weight.
- Pick the smallest edge. Check if it forms a cycle with the spanning tree formed so far. If cycle is not formed, include this edge. Else, discard it.
- Repeat step#2 until there are (V-1) edges in the spanning tree.

Here are some key points which will be useful for us in implementing the Kruskal’s algorithm using STL.

- Use a vector of edges which consist of all the edges in the graph and each item of a vector will contain 3 parameters: source, destination and the cost of an edge between the source and destination.
vector<pair<int, pair<int, int> > > edges;

Here in the outer pair (i.e pair<int,pair<int,int> > ) the first element corresponds to the cost of a edge while the second element is itself a pair, and it contains two vertices of edge.

- Use the inbuilt std::sort to sort the edges in the non-decreasing order; by default the sort function sort in non-decreasing order.
- We use the Union Find Algorithm to check if it the current edge forms a cycle if it is added in the current MST. If yes discard it, else include it (union).

**Pseudo Code:**

// Initialize result mst_weight = 0 // Create V single item setsforeachvertex v parent[v] = v; rank[v] = 0;Sortall edges into non decreasing order by weight wforeach (u, v) taken from the sorted list Edo ifFIND-SET(u) !=FIND-SET(v)UNION(u, v)

Below is C++ implementation of above algorithm.

`// C++ program for Kruskal's algorithm to find Minimum` `// Spanning Tree of a given connected, undirected and` `// weighted graph` `#include<bits/stdc++.h>` `using` `namespace` `std;` ` ` `// Creating shortcut for an integer pair` `typedef` `pair<` `int` `, ` `int` `> iPair;` ` ` `// Structure to represent a graph` `struct` `Graph` `{` ` ` `int` `V, E;` ` ` `vector< pair<` `int` `, iPair> > edges;` ` ` ` ` `// Constructor` ` ` `Graph(` `int` `V, ` `int` `E)` ` ` `{` ` ` `this` `->V = V;` ` ` `this` `->E = E;` ` ` `}` ` ` ` ` `// Utility function to add an edge` ` ` `void` `addEdge(` `int` `u, ` `int` `v, ` `int` `w)` ` ` `{` ` ` `edges.push_back({w, {u, v}});` ` ` `}` ` ` ` ` `// Function to find MST using Kruskal's` ` ` `// MST algorithm` ` ` `int` `kruskalMST();` `};` ` ` `// To represent Disjoint Sets` `struct` `DisjointSets` `{` ` ` `int` `*parent, *rnk;` ` ` `int` `n;` ` ` ` ` `// Constructor.` ` ` `DisjointSets(` `int` `n)` ` ` `{` ` ` `// Allocate memory` ` ` `this` `->n = n;` ` ` `parent = ` `new` `int` `[n+1];` ` ` `rnk = ` `new` `int` `[n+1];` ` ` ` ` `// Initially, all vertices are in` ` ` `// different sets and have rank 0.` ` ` `for` `(` `int` `i = 0; i <= n; i++)` ` ` `{` ` ` `rnk[i] = 0;` ` ` ` ` `//every element is parent of itself` ` ` `parent[i] = i;` ` ` `}` ` ` `}` ` ` ` ` `// Find the parent of a node 'u'` ` ` `// Path Compression` ` ` `int` `find(` `int` `u)` ` ` `{` ` ` `/* Make the parent of the nodes in the path` ` ` `from u--> parent[u] point to parent[u] */` ` ` `if` `(u != parent[u])` ` ` `parent[u] = find(parent[u]);` ` ` `return` `parent[u];` ` ` `}` ` ` ` ` `// Union by rank` ` ` `void` `merge(` `int` `x, ` `int` `y)` ` ` `{` ` ` `x = find(x), y = find(y);` ` ` ` ` `/* Make tree with smaller height` ` ` `a subtree of the other tree */` ` ` `if` `(rnk[x] > rnk[y])` ` ` `parent[y] = x;` ` ` `else` `// If rnk[x] <= rnk[y]` ` ` `parent[x] = y;` ` ` ` ` `if` `(rnk[x] == rnk[y])` ` ` `rnk[y]++;` ` ` `}` `};` ` ` ` ` `/* Functions returns weight of the MST*/` ` ` `int` `Graph::kruskalMST()` `{` ` ` `int` `mst_wt = 0; ` `// Initialize result` ` ` ` ` `// Sort edges in increasing order on basis of cost` ` ` `sort(edges.begin(), edges.end());` ` ` ` ` `// Create disjoint sets` ` ` `DisjointSets ds(V);` ` ` ` ` `// Iterate through all sorted edges` ` ` `vector< pair<` `int` `, iPair> >::iterator it;` ` ` `for` `(it=edges.begin(); it!=edges.end(); it++)` ` ` `{` ` ` `int` `u = it->second.first;` ` ` `int` `v = it->second.second;` ` ` ` ` `int` `set_u = ds.find(u);` ` ` `int` `set_v = ds.find(v);` ` ` ` ` `// Check if the selected edge is creating` ` ` `// a cycle or not (Cycle is created if u` ` ` `// and v belong to same set)` ` ` `if` `(set_u != set_v)` ` ` `{` ` ` `// Current edge will be in the MST` ` ` `// so print it` ` ` `cout << u << ` `" - "` `<< v << endl;` ` ` ` ` `// Update MST weight` ` ` `mst_wt += it->first;` ` ` ` ` `// Merge two sets` ` ` `ds.merge(set_u, set_v);` ` ` `}` ` ` `}` ` ` ` ` `return` `mst_wt;` `}` ` ` `// Driver program to test above functions` `int` `main()` `{` ` ` `/* Let us create above shown weighted` ` ` `and unidrected graph */` ` ` `int` `V = 9, E = 14;` ` ` `Graph g(V, E);` ` ` ` ` `// making above shown graph` ` ` `g.addEdge(0, 1, 4);` ` ` `g.addEdge(0, 7, 8);` ` ` `g.addEdge(1, 2, 8);` ` ` `g.addEdge(1, 7, 11);` ` ` `g.addEdge(2, 3, 7);` ` ` `g.addEdge(2, 8, 2);` ` ` `g.addEdge(2, 5, 4);` ` ` `g.addEdge(3, 4, 9);` ` ` `g.addEdge(3, 5, 14);` ` ` `g.addEdge(4, 5, 10);` ` ` `g.addEdge(5, 6, 2);` ` ` `g.addEdge(6, 7, 1);` ` ` `g.addEdge(6, 8, 6);` ` ` `g.addEdge(7, 8, 7);` ` ` ` ` `cout << ` `"Edges of MST are \n"` `;` ` ` `int` `mst_wt = g.kruskalMST();` ` ` ` ` `cout << ` `"\nWeight of MST is "` `<< mst_wt;` ` ` ` ` `return` `0;` `}` |

Output :

Edges of MST are 6 - 7 2 - 8 5 - 6 0 - 1 2 - 5 2 - 3 0 - 7 3 - 4 Weight of MST is 37

**Optimization:**

The above code can be optimized to stop the main loop of Kruskal when number of selected edges become V-1. We know that MST has V-1 edges and there is no point iterating after V-1 edges are selected. We have not added this optimization to keep code simple.

**References:**

Introduction to Algorithms by Cormen Leiserson Rivest and Stein(CLRS) 3

Time complexity and step by step illustration are discussed in previous post on Kruskal’s algorithm.

This article is contributed by **Chirag Agrawal**. If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

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