# Solving Leetcode Interviews in Seconds with AI: Lucky Numbers in a Matrix


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "1380" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like [Chatmagic](https://www.chatmagic.app), we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

	# Problem Statement
	> Given an m x n matrix of distinct numbers, return all lucky numbers in the matrix in any order. A lucky number is an element of the matrix such that it is the minimum element in its row and maximum in its column.   Example 1:  Input: matrix = [[3,7,8],[9,11,13],[15,16,17]] Output: [15] Explanation: 15 is the only lucky number since it is the minimum in its row and the maximum in its column.  Example 2:  Input: matrix = [[1,10,4,2],[9,3,8,7],[15,16,17,12]] Output: [12] Explanation: 12 is the only lucky number since it is the minimum in its row and the maximum in its column.  Example 3:  Input: matrix = [[7,8],[1,2]] Output: [7] Explanation: 7 is the only lucky number since it is the minimum in its row and the maximum in its column.    Constraints:  m == mat.length n == mat[i].length 1 <= n, m <= 50 1 <= matrix[i][j] <= 105. All elements in the matrix are distinct.  

	# Explanation
	Here's the breakdown of the solution:

*   **Find Row Minima:** Iterate through each row and find the minimum element in that row along with its column index.
*   **Find Column Maxima:** Iterate through each column and find the maximum element in that column.
*   **Identify Lucky Numbers:** Compare the row minima with the column maxima. If a number is both the minimum in its row and the maximum in its column, it's a lucky number.

*   **Time Complexity:** O(m\*n), where m is the number of rows and n is the number of columns.
*   **Space Complexity:** O(m + n) to store the row minima and column maxima.

	
	# Code
	```python
	def luckyNumbers (matrix):
    row_mins = []
    col_maxs = []
    result = []

    # Find row minima and their column indices
    for row in matrix:
        min_val = float('inf')
        for val in row:
            min_val = min(min_val, val)
        row_mins.append(min_val)

    # Find column maxima
    for j in range(len(matrix[0])):
        max_val = float('-inf')
        for i in range(len(matrix)):
            max_val = max(max_val, matrix[i][j])
        col_maxs.append(max_val)

    # Identify lucky numbers
    for i in range(len(matrix)):
      for j in range(len(matrix[0])):
        if matrix[i][j] == row_mins[i] and matrix[i][j] == col_maxs[j]:
          result.append(matrix[i][j])

    return result
	```