Is Lucky Numbers in a Matrix easy or hard?

Lucky Numbers in a Matrix is categorized as an Easy problem on LeetCode with a high acceptance rate around 79%. The challenge focuses on correctly identifying row minimum and column maximum relationships rather than complex algorithms.

Lucky Numbers in a Matrix Python/Java solution

Python and Java implementations typically iterate through rows to find the minimum element and then validate whether it is the maximum in its column. The logic translates directly across languages using simple loops and constant extra space.

How to solve Lucky Numbers in a Matrix in O(m*n)?

Compute two arrays: one storing the minimum element of each row and another storing the maximum element of each column. Then iterate through the matrix and check if matrix[i][j] equals both rowMin[i] and colMax[j]. Each element is processed a constant number of times, resulting in O(m * n) time complexity.

What is the best approach for Lucky Numbers in a Matrix?

The most common solution scans each row to find its minimum and then verifies whether that value is the maximum in its column. This approach runs in O(m * (n + m)) time with O(1) extra space. Another clean solution precomputes row minimums and column maximums and checks their intersection in O(m * n) time.

Is Lucky Numbers in a Matrix asked at Google/Amazon/Meta?

Matrix traversal and row/column property problems appear frequently in technical interviews at companies like Amazon and Google. While this exact problem may vary, the underlying pattern of computing row minimums and column maximums is a common interview concept.

What data structure is used in Lucky Numbers in a Matrix?

The problem primarily uses a 2D array (matrix). Some optimized solutions also use auxiliary arrays to store row minimums and column maximums, which help reduce repeated scans and keep the algorithm linear.

What is the time complexity of Lucky Numbers in a Matrix?

The typical iterative solution runs in O(m * (n + m)) time because each row scan takes O(n) and each column validation takes O(m). With a precomputation approach using row minimum and column maximum arrays, the runtime becomes O(m * n) with O(m + n) extra space.

Description Hints 2 Solution Video 16:21

1380. Lucky Numbers in a Matrix

Easy79.9% acceptance3 companies

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.

Asked at 3 companies

Cisco Oracle Amazon

Topics

Array Matrix

Hints open one at a time

Hint 1

Find out and save the minimum of each row and maximum of each column in two lists.

Hint 2

Then scan through the whole matrix to identify the elements that satisfy the criteria.

16:21

Still stuck?Read 3 approaches, or watch a walkthrough.

Open solution

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1class Solution:
2    def luckyNumbers(self, matrix: List[List[int]]) -> List[int]:
3        

Starter codePython 3Spaces: 4

Examples

Case 1Case 2Case 3

Input

[[3,7,8],[9,11,13],[15,16,17]]

Input

[[1,10,4,2],[9,3,8,7],[15,16,17,12]]

Input

[[7,8],[1,2]]

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