September 10, 2023 2 min to read

Diagonal Difference - HackerRank Problem Solving

Diagonal Difference is easy-level Python problem that requires basic knowledge of Array. In this post, we will provide a Python solution for Diagonal Difference.

Problem Statement and Explanation

Given a square matrix, calculate the absolute difference between the sums of its diagonals. Suppose, we have the following matrix arr:

1 2 3
4 5 6
7 8 9

Diagonal of the above matrix are:

1 5 9

3 5 7

Their absolute difference is:

|15 - 17| = 2

Input Format

• n: an integer, the number of rows and columns in the square matrix arr
• arr: a two-dimensional array of integers

Output Format

• int: the absolute diagonal difference

Diagonal Difference Solution in Python

Explanation of Solution in Python

The function diagonalDifference() takes a square matrix arr as input and returns the absolute difference between the sums of the two diagonals of the matrix.

• A for loop is used to iterate over all the rows in the matrix.
• In each iteration, the current element at index arr[i][i] is added to d1. This is because the element at index arr[i][i] is located on the main diagonal of the matrix.
• The current element at index arr[i][n-i-1] is added to d2. This is because the element at index arr[i][n-i-1] is located on the secondary diagonal of the matrix.
• After the for loop, the absolute difference between d1 and d2 is returned.

Diagonal Difference Solution in C++

Explanation of Solution in C++

• The variable s1 stores the sum of the elements on the main diagonal of the matrix. The main diagonal is the diagonal that runs from the top left corner to the bottom right corner of the matrix.
• The variable s2 stores the sum of the elements on the secondary diagonal of the matrix. The secondary diagonal is the diagonal that runs from the top right corner to the bottom left corner of the matrix.
• The variable n stores the number of rows in the matrix.
• A for loop is used to iterate over all the rows in the matrix. In each iteration, the current element is added to either s1 or s2, depending on whether it is on the main diagonal or the secondary diagonal.
• Finally, the absolute difference between s1 and s2 is returned.

Time complexity: The time complexity is the amount of time it takes to execute the code. In this case, the code iterates over all the rows in the matrix, so the time complexity is O(n), where n is the number of rows in the matrix.

Space complexity: The space complexity is the amount of memory that the code uses. In this case, the code only uses three variables, so the space complexity is O(1).