Is Score After Flipping Matrix easy or hard?

Score After Flipping Matrix is classified as a Medium problem. The implementation is simple once you recognize the greedy insight that the first column must always be 1 and that column decisions depend on maximizing the number of 1s.

How to solve Score After Flipping Matrix in O(m*n)?

First ensure the most significant bit of every row is 1 by virtually flipping rows whose first column is 0. Then iterate through each column and count how many 1s would appear after that adjustment. Choose the larger of ones or zeros for that column and add its weighted binary contribution to the total score.

What is the best approach for Score After Flipping Matrix?

The optimal approach is a greedy strategy that ensures the first column of every row is 1 and then maximizes the number of 1s in each remaining column. By treating rows with leading 0 as virtually flipped and applying a column majority rule, you compute the maximum possible score in O(m*n) time and O(1) space.

What data structure is used in Score After Flipping Matrix?

The problem primarily uses a 2D array (matrix) to represent the grid. The algorithm relies on greedy analysis and simple counting rather than advanced data structures, though bit manipulation concepts help when calculating binary weights.

What is the time complexity of Score After Flipping Matrix?

The optimal solution runs in O(m*n) time where m is the number of rows and n is the number of columns. Each cell is inspected at most once while computing column contributions. The space complexity remains O(1) since the calculation can be done without extra data structures.

Score After Flipping Matrix Python or Java solution approach?

Python and Java implementations typically follow the same greedy pattern: ensure the first column is effectively all 1s, then evaluate each column using majority counts. The code iterates through the matrix and accumulates the weighted score based on binary positions.

Is Score After Flipping Matrix asked at Google, Amazon, or Meta?

Matrix greedy optimization problems like Score After Flipping Matrix frequently appear in technical interviews at companies such as Amazon, Google, and Meta. The problem tests reasoning about binary values, greedy choices, and efficient traversal of a matrix.

Description Solution Video 22:30

861. Score After Flipping Matrix

Medium80.3% acceptance3 companies

You are given an m x n binary matrix grid.

A move consists of choosing any row or column and toggling each value in that row or column (i.e., changing all 0's to 1's, and all 1's to 0's).

Every row of the matrix is interpreted as a binary number, and the score of the matrix is the sum of these numbers.

Return the highest possible score after making any number of moves (including zero moves).

Example 1:

Input: grid = [[0,0,1,1],[1,0,1,0],[1,1,0,0]]
Output: 39
Explanation: 0b1111 + 0b1001 + 0b1111 = 15 + 9 + 15 = 39

Example 2:

Input: grid = [[0]]
Output: 1

Constraints:

m == grid.length
n == grid[i].length
1 <= m, n <= 20
grid[i][j] is either 0 or 1.

Asked at 3 companies

Iit Bombay Amazon

Google

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22:30

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861. Score After Flipping Matrix

Asked at 3 companies

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