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Minimum flip required to make Binary Matrix symmetric

Symmetric matrices are matrices that are equal to their transpose. Suppose A is a symmetric matrix, then A = AT. These matrices frequently occur in linear algebra and have applications in various fields like physics, computer science, statistics, etc.

In some cases, we may have a binary matrix that is non-symmetric. A binary matrix contains only 0s and 1s as its elements. The problem is to find the minimum number of flips required to convert a given binary matrix into a symmetric matrix.

The minimum flip problem has been studied extensively in the literature. Different solutions have been proposed using techniques like greedy algorithms, dynamic programming, graph theory, etc. This article will look at an elegant solution using basic Python concepts.

The key steps are to iterate through the upper triangular part of the Matrix, compare elements symmetrically across the diagonal and count the mismatches. Some optimization is done to reduce redundant comparisons.

This results in an efficient O(n^2) algorithm to find the minimum flips. We will also look at a sample implementation in Python to demonstrate the approach.

Finding optimal flipping strategies has applications in varied domains. This article will provide intuition into this interesting problem and how it can be solved using simple yet effective techniques.

What is a Binary Matrix?

A binary matrix is one in which there are just two potential values for each element: 0 or 1. It is comparable to a standard matrix, except that each place in the Matrix can only contain a 0 or a 1.

For example, consider the following 3x3 Matrix:

This is a binary matrix as it only contains 0s and 1s as elements. Each row and column represents a binary number.

Some key properties of binary matrices:

  • The dimensions specify the number of rows and columns, just like a general matrix.
  • Standard matrix operations like addition, multiplication, etc, can be performed on binary matrices by treating 0 and 1 as numerical values.
  • Binary matrices are commonly used to represent Boolean functions, sets, and graphs and for operations like counting, sorting, graphs, etc.
  • Algorithms like matrix flipping, rearrangements, rotations, etc, are frequently applied to binary matrices.
  • Binary matrices have applications in computer science fields like game theory, cryptography, machine learning etc.

Approach 1: Simple Method

Symmetric matrices have applications in various scientific and engineering domains. Converting an arbitrary binary matrix into a symmetric matrix using a minimum number of flips is a critical computational problem.

A simple solution is to take the transpose of the given Matrix and compare it with the original element-wise. The number of unequal element pairs provides the minimum with flips required to make the Matrix symmetric.

This reduces the problem of finding mismatching elements across the diagonal rather than brute forcing all possible flip combinations.

While simple, this naive solution has an O(n^2) time complexity to fill the transpose and compare elements. The space complexity is O(n^2) to store the transpose.

This provides a baseline for the matrix flip problem without trying all exponential combinations. It can serve as a starting point before exploring optimizations and advanced algorithms.

We will walk through the Python code step-by-step to build intuition. Later, we will discuss ways to improve the performance and efficiency of finding the required minimum flips.




  1. Define the min_flips function, which takes the matrix mat and dimension n as parameters.
  2. Create a transpose matrix trans of size n x n initialized to all 0s.
  3. Populate the transpose Matrix by flipping indices of mat (trans[i][j] = mat[j][i]).
  4. Initialize a flips counter to 0 to store the number of flips required.
  5. Iterate over the Matrix and its transpose.
  6. Compare each element mat[i][j] with its corresponding element in transpose trans[i][j].
  7. If the elements are not equal, increment the flips counter.
  8. Return the flips count divided by 2 (to account for symmetrically flipped elements).
  9. Define a sample 3x3 binary matrix mat for testing.
  10. Call min_flips() passing mat and its dimension n.
  11. Print the result returned by min_flips, which is the minimum flips required.

We compare the Matrix with its transpose to find unequal element pairs. Counting these gives us the number of flips to make the Matrix symmetric.

The time complexity is O(n^2) to fill the transpose and compare elements. Space complexity is O(n^2) for the transpose Matrix.

Approach 2: Efficient way

Symmetric matrices have broad applications in linear algebra and optimization problems. Converting an arbitrary binary matrix into a symmetric matrix using minimal flips is a common problem. A brute force approach generates all possible flip combinations, which is exponential.

An optimized solution utilizes the inherent symmetry property of the problem. For a symmetric matrix A, the condition A[i][j] = A[j][i] must be satisfied. Instead of changing all elements, we only need to consider flips along the upper triangular portion above the diagonal.

The algorithm works as follows:

  1. Initialize flips counter to 0
  2. Iterate through the upper triangular portion:
    • For each row i = 0 to N-1
      • Column j goes from i+1 to N
  3. If A[i][j] != A[j][i], increment flips counter
  4. Return total flips count

This iterates only over N(N-1)/2 elements instead of complete N^2 elements. The symmetry condition is checked, and flips are counted.

This optimized approach reduces the time complexity from O(2^N) in brute force to O(N^2). Only O(1) extra space is needed.


  1. Define a function min_flips(), which takes the matrix mat and its order o as parameters.
  2. Initialize a variable flips to store the number of flips required.
  3. Use two nested loops to iterate over the upper triangular part of the Matrix.
    • The outer loop goes from 0 to o-1
    • The inner loop goes from i+1 to o (for symmetry)
  4. Compare elements mat[i][j] and mat[j][i] in the upper triangular portion.
  5. If the elements are not equal, increment the flips count.
  6. After complete traversal, return the total flips count.
  7. Take a 3x3 sample binary matrix mat as input.
  8. Call min_flips() passing the Matrix and its order 3.
  9. Print the result returned, which is the minimum flips needed.

In this approach, we only traverse the upper half of the Matrix instead of the entire Matrix. This avoids redundant comparisons and improves efficiency.


In conclusion, the problem of finding the minimum number of flips required to make a binary matrix symmetric has essential applications in fields like mathematics, computer science and network theory. We looked at different techniques to solve this problem efficiently.

The brute force approach tries all possible flip combinations, which is exponential in time complexity. A better solution utilizes the inherent symmetry in the problem - we only need to consider mismatches along the upper triangle above the diagonal.

This symmetry-breaking insight leads to an optimized O(N^2) algorithm. We implemented it in Python by iterating over the upper triangular portion and flipping unequal element pairs. The code demonstrates how we can avoid redundant comparisons and unnecessary flips.

To summarize, leveraging the symmetric property helps create an efficient solution compared to the naive brute force technique. This demonstrates the value of applying problem-specific insights to develop faster algorithms.

The matrix flip problem is an excellent example of understanding techniques like symmetry breaking, pruning, memoization, bit manipulations, etc., that can be applied to a variety of optimization challenges. This lays the foundation to tackle more advanced problems efficiently.

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