Data Structures & Algorithms
Kruskal's Algorithm
Master Kruskal's algorithm for finding the Minimum Spanning Tree (MST) in your coding interviews. Our guide covers the greedy approach, Union-Find data structure, and offers AI-powered practice.
Kruskal's Algorithm Implementation
Here is a Python implementation of Kruskal's algorithm. It uses the Union-Find data structure to efficiently detect cycles.
class DSU:
def __init__(self, n):
self.parent = list(range(n))
def find(self, i):
if self.parent[i] == i:
return i
self.parent[i] = self.find(self.parent[i])
return self.parent[i]
def union(self, i, j):
root_i = self.find(i)
root_j = self.find(j)
if root_i != root_j:
self.parent[root_i] = root_j
return True
return False
def kruskal(graph, num_vertices):
# graph is a list of edges in the format (u, v, w)
graph.sort(key=lambda item: item[2])
dsu = DSU(num_vertices)
mst = []
total_weight = 0
for u, v, w in graph:
if dsu.union(u, v):
mst.append((u, v, w))
total_weight += w
return mst, total_weight
AI Coach Tip: Kruskal's algorithm's efficiency heavily relies on the Union-Find data structure with path compression and union by rank/size optimizations. The time complexity is dominated by sorting the edges, making it O(E log E), where E is the number of edges. This is often better than Prim's algorithm for sparse graphs.
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