9.3 Boundary Elements of a Matrix in Go
9. Basic Matrix Algorithm Problems in Go
Full Course on Data Structures and Algorithms in C
By: Chrysanthus Date Published: 3 Feb 2026
The reader is advised to read all the lessons (tutorials) in this full course, in the order presented.
Problem
Given a matrix mat[][] of size n × m, the task is to traverse the boundary elements in clockwise order, starting from the top-left element.
Example 1:
Input: mat[][] = [[1, 2, 3, 4],
[5, 6, 7, 8],
[1, 2, 3, 4],
[5, 6, 7, 9]]
Output: [1, 2, 3, 4, 8, 4, 9, 7, 6, 5, 1, 5]
Example 2:
Input: mat[][] = [[1, 2],
[3, 4]]
Output: [1, 2, 4, 3]
Do it in O(n + m) time and not O(n x m) time.
Solution in O(n + m) Time
Algorithm
- Traverse the top row from left to right, excluding the last element on the right.
- Traverse the rightmost column from top to bottom, excluding the last element at the bottom.
- Traverse the bottom row from right to left, excluding the first element on the left.
- Traverse the leftmost column from bottom to top, excluding the first element at the top.
Program in O(n+m) Time
The program is (read through the code and comments):
package main
import (
"fmt"
)
func boundaryTraversal(mat *[4][4]int) { //use pointer to avoid unnecessary function recopying of Array
// Determine dimensions automatically
var n = len(*mat)
var m = len((*mat)[0])
// Traverse the top row
for j := 0; j < m-1; j++ {
fmt.Print((*mat)[0][j], ", ");
}
// Traverse the last column
for i := 0; i < n-1; i++ {
fmt.Print((*mat)[i][m-1], ", ");
}
// Traverse the bottom row
if (n > 1) { // Check to avoid duplicating row in single-row matrix
for j := m - 1; j > 0; j-- {
fmt.Print((*mat)[n-1][j], ", ");
}
}
// Traverse the first column
if (m > 1) { // Check to avoid duplicating column in single-column matrix
for i := n - 1; i > 0; i-- {
fmt.Print((*mat)[i][0], ", ");
}
}
fmt.Println();
}
func main() {
var mat = [4][4]int{{1, 2, 3, 4},
{5, 6, 7, 8},
{1, 2, 3, 4},
{5, 6, 7, 9}};
boundaryTraversal(&mat); //use pointer to avoid unnecessary function recopying of Array
}
The output is:
1, 2, 3, 4, 8, 4, 9, 7, 6, 5, 1, 5,
as expected.
The time complexity is actually O(n+n + (m-1)+(m-1)) = O(2n + 2(m-2)). If the coefficients (multiplicands) of 2 are ignored, as it is customary, then the time complexity would be quoted as O(n + (m-2)). Note: -1, -2, +1, +2, etc. are usually ignored as well. So the time complexity is quoted as O(n+m).
The space complexity is O(n x m), for the input matrix. The matrix in the calling function and the called function are the same (same reference).
Related Links
More Related LinksCousins
BACK NEXTComments
Note: You can use the Search Box above to find articles and discussions of interest.