Jul 1, 2025 · Jun 30, 2025
diff --git a/src/graph/mst_prim.md b/src/graph/mst_prim.md
 ```

 The adjacency matrix `adj[][]` of size $n \times n$ stores the weights of the edges, and it uses the weight `INF` if there doesn't exist an edge between two vertices.
 The algorithm uses two arrays: the flag `selected[]`, which indicates which vertices we already have selected, and the array `min_e[]` which stores the edge with minimal weight toan selected vertex for each not-yet-selected vertex (it stores the weight and the end vertex).
 The algorithm uses two arrays: the flag `selected[]`, which indicates which vertices we already have selected, and the array `min_e[]` which stores the edge with minimal weight toa selected vertex for each not-yet-selected vertex (it stores the weight and the end vertex).
 The algorithm does $n$ steps, in each iteration the vertex with the smallest edge weight is selected, and the `min_e[]` of all other vertices gets updated.

 ### Sparse graphs: $O(m \log n)$
Original file line number	Diff line number	Diff line change
Expand Up		@@ -147,7 +147,7 @@ void prim() {
		```

		The adjacency matrix `adj[][]` of size $n \times n$ stores the weights of the edges, and it uses the weight `INF` if there doesn't exist an edge between two vertices.
		The algorithm uses two arrays: the flag `selected[]`, which indicates which vertices we already have selected, and the array `min_e[]` which stores the edge with minimal weight toan selected vertex for each not-yet-selected vertex (it stores the weight and the end vertex).
		The algorithm uses two arrays: the flag `selected[]`, which indicates which vertices we already have selected, and the array `min_e[]` which stores the edge with minimal weight toa selected vertex for each not-yet-selected vertex (it stores the weight and the end vertex).
		The algorithm does $n$ steps, in each iteration the vertex with the smallest edge weight is selected, and the `min_e[]` of all other vertices gets updated.

		### Sparse graphs: $O(m \log n)$
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