Flip Columns For Maximum Number of Equal Rows | 2 Approaches | Leetcode 1072 | codestorywithMIK
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This is the 124th Video of our Playlist "Array 1D/2D : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a simple 2D Array based problem : Flip Columns For Maximum Number of Equal Rows | 2 Approaches | Leetcode 1072 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : Flip Columns For Maximum Number of Equal Rows | 2 Approaches | Leetcode 1072 | codestorywithMIK
Company Tags : will update
My solutions on Github(C++ & JAVA) - https://github.com/MAZHARMIK/Interview_DS_Algo/blob/master/Arrays/2-D Array/Flip Columns For Maximum Number of Equal Rows.cpp
Leetcode Link : https://leetcode.com/problems/flip-columns-for-maximum-number-of-equal-rows
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Summary :
1. Row Matching with inverted (First Approach):
Logic: Compares each row in the matrix to a reference row (currRow) and its inverted version (where 0s are flipped to 1s and 1s to 0s). Counts how many rows match either of these.
Implementation:
For each row, create an inverted version.
Count rows in the matrix that match either the reference row or its inverted version.
Update the maximum count of matching rows.
Complexity:
Time: O(m2×n), as each row is compared with every other row.
Space: O(n), for storing the inverted row.
Advantage: Straightforward logic based on direct comparison of rows.
2. Row Patterns using unordered_map or HashMap (Second Approach):
Logic: Encodes each row into a pattern (S for "same as the first column" and B for "different from the first column"). Tracks the frequency of each pattern using a hash map.
Implementation:
Convert each row into a string pattern based on its relation to the first column.
Use a hash map to count occurrences of each pattern.
The maximum frequency gives the result.
Complexity:
Time: O(m×n), as each row is processed and stored once.
Space: O(m×n), for storing the patterns in the hash map.
Advantage: Efficient due to hashing and avoids redundant comparisons.
✨ Timelines✨
00:00 - Introduction
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