Geert Arien

Different methods for matrix inversion

The inverse of a matrix is an important operation that is applicable only to square matrices. Geometrically the inverse of a matrix is useful because it allows us to compute the reverse of a transformation, i.e. a transformation that undoes another transformation. There are several ways to calculate the inverse of a matrix. We’ll be taking a look at two well known methods, Gauss-Jordan elimination and the adjoint method, and one lesser known method, the partition method. We’ll show how to implement each method in C++ for arbitrary \( 3 \times 3 \) matrices.

We test the performance of our algorithms with an array of arbitrary \( 3 \times 3 \) matrices for which we’ll compute the inverse. An important point to consider is that only invertible/nonsingular matrices have an inverse. We can calculate and check the determinant of a matrix to see if it’s invertible or not, as the determinant of a singular matrix is zero and the determinant of a nonsingular matrix is nonzero. We’ll use a brute force method to generate our arbitrary matrices, which means we’ll keep generating random matrices until we have enough matrices for which the determinant is nonzero (conveniently ignoring the fact that we’ll generate a few ill conditioned matrices that are nearly singular).

We start by creating a function that takes three arguments, a function pointer to a function that returns (a random) integer and two floating-point numbers \(min\) and \(max\). This function returns a random \( 3 \times 3 \) matrix for which each element lies in the range \([min, max]\):

#include <cmath>

Matrix generate_random_matrix(int random(), float min, float max)
  float m[3][3];

  for (int i = 0; i < 3; i++)
    for (int j = 0; j < 3; j++)
      m[i][j] = min + static_cast <float> (random())
                / (static_cast <float> (RAND_MAX / (max - min)));

  return Matrix(m[0][0], m[0][1], m[0][2], m[1][0], m[1][1],
                m[1][2], m[2][0], m[2][1], m[2][2]);

Next we call this function from our main program and check, using a tolerance value, if the returned Matrix has a determinant which is nonzero. We keep repeating this process until we have 1.000.000 invertible matrices. To calculate the determinant of a \( 3 \times 3 \) matrix, we use the following formula:

For the random integers we pass the rand() function from <cstdlib> to our random matrix function. rand() is a pseudo-random number generator so we need to seed it to make sure we generate a unique sequence of random numbers. We do this by calling srand() with the current time as parameter.

#include <vector>
using std::vector;

#include <ctime>
#include <stdlib.h>
#include "matrix.h"

int main()
  vector<Matrix> matrices;

  srand(static_cast <unsigned> (time(0)));

  while (matrices.size() != 1000000)
	  Matrix m = generate_random_matrix(rand(), -10.0f, 10.0f);

    if (abs(m.determinant()) > 0.001f)


Now that we have our matrices, we can take a look at the different ways to calculate the inverse.

Gauss-Jordan Elimination

Gauss-Jordan Elimination is an extension of Gaussian Elimination, an algorithm for solving systems of linear equations. Both algorithms make use of row operations to solve the system, however the difference between the two is that Gaussian Elimination helps to put a matrix in row echelon form, while Gauss-Jordan Elimination puts a matrix in reduced row echelon form. Gauss-Jordan Elimination is a much less efficient method for solving systems of linear equations, compared to Gaussian Elimination, however it’s an excellent method for calculating the inverse of a matrix.

As an example we’ll calculate the inverse of the \( 2 \times 2 \) matrix \( A \).

First we adjoin the identity matrix to the right side of \( A \) then we’ll apply row operations until the matrix on the left side is reduced to the identity matrix.

The inverse of matrix is \( A \) is the \( 2 \times 2 \) matrix on the right side.

For our implementation we have split the algorithm into four distinct parts:

  1. Adding a unit matrix to the right side of our matrix.
  2. Partial pivoting to reduce round-off errors, which is a negative side-effect of the way numbers are stored on a computer.
  3. Performing row operations to reduce our matrix to a diagonal matrix.
  4. Reducing the diagonal matrix to a unit matrix.

We make the Gauss-Jordan inversion method a member of our matrix class and pass and return the output matrix as a reference, that way we avoid creating and copying matrix objects during the function call.

#include "matrix.h"

Matrix& Matrix::calculate_inverse_gause(Matrix& out) const
  int i, j, k;
  float copied[3][6] = { 0 }, d;

  for (i = 0; i < 3; i++)
    for (j = 0; j < 3; j++)
      copied[i][j] = data[i][j];

  copied[0][3] = 1;
  copied[1][4] = 1;
  copied[2][5] = 1;

  /************** partial pivoting **************/
  for (i = 2; i > 0; i--)
    if (copied[i - 1][1] < copied[i][1])
      for (j = 0; j < 6; j++)
        d = copied[i][j];
        copied[i][j] = copied[i - 1][j];
        copied[i - 1][j] = d;

  /********** reducing to diagonal  matrix ***********/
  for (i = 0; i < 3; i++)
    for (j = 0; j < 3; j++)
      if (j != i && copied[j][i] != 0)
        d = copied[j][i] / copied[i][i];
        for (k = 0; k < 6; k++)
          copied[j][k] -= copied[i][k] * d;

  /************** reducing to unit matrix *************/[0][0] = copied[0][3] / copied[0][0];[0][1] = copied[0][4] / copied[0][0];[0][2] = copied[0][5] / copied[0][0];[1][0] = copied[1][3] / copied[1][1];[1][1] = copied[1][4] / copied[1][1];[1][2] = copied[1][5] / copied[1][1];[2][0] = copied[2][3] / copied[2][2];[2][1] = copied[2][4] / copied[2][2];[2][2] = copied[2][5] / copied[2][2];

  return out;

One of the advantages of the Gauss-Jordan method is that it features fewer arithmetic operations compared to other methods. The disadvantage is that it features quite a lot of if statements and loops that cannot be unrolled. That’s why this method is preferred when working with large matrices or matrices with a structure that can be exploited.

Classical Adjoint method

The classical adjoint of a matrix \( A \) is the transpose of the matrix of cofactors of \( A \). Once we have the adjoint we can compute the inverse of \( A \) by dividing the adjoint matrix by the determinant of \(A\).

We can compute the cofactors of a matrix by computing the corresponding minor for each element of the original matrix, negating every other element.

Lets take a look at an example. We want to compute the inverse of the \( 3 \times 3 \) matrix \( A \).

First we compute the cofactors of matrix \( A \).

The classical adjoint is the transpose of the matrix of cofactors.

Now we can compute the inverse by dividing the classical adjoint of \( A \) by the determinant of \( A \).

The implementation of the classical adjoint method is very straight forward. All we have to do is solve the equation for each element of the inverse matrix.

#include "matrix.h"

Matrix& Matrix::calculate_inverse_adjoint(Matrix& out) const
  float det_inv = 1 / calculate_determinant();[0][0] =  (data[1][1] * data[2][2]
                     - data[1][2] * data[2][1]) * det_inv;[0][1] = -(data[0][1] * data[2][2]
                     - data[0][2] * data[2][1]) * det_inv;[0][2] =  (data[0][1] * data[1][2]
                     - data[0][2] * data[1][1]) * det_inv;[1][0] = -(data[1][0] * data[2][2]
                     - data[1][2] * data[2][0]) * det_inv;[1][1] =  (data[0][0] * data[2][2]
                     - data[0][2] * data[2][0]) * det_inv;[1][2] = -(data[0][0] * data[1][2]
                     - data[0][2] * data[1][0]) * det_inv;[2][0] =  (data[1][0] * data[2][1]
                     - data[1][1] * data[2][0]) * det_inv;[2][1] = -(data[0][0] * data[2][1]
                     - data[0][1] * data[2][0]) * det_inv;[2][2] =  (data[0][0] * data[1][1]
                     - data[0][1] * data[1][0]) * det_inv;

  return out;

The classical adjoint method provides for a branchless implementation, this is a big advantage when working on today’s superscalar computer hardware. It’s the go to method when working with matrices of smaller order such as \( 2 \times 2 \), \( 3 \times 3 \) and \( 4 \times 4 \) matrices. As such, it is the method of choice in most geometric applications.

Partition method

The partition (or escalator) method is a less famous recursive method for computing the inverse of a square matrix. The partition method is based on the fact that if the inverse of square matrix \(A_{n}\) of order \(n\) is known, then the inverse of the matrix \(A_{n+1}\) can be obtained by adding \((n+1)th\) row and \((n+1)th\) column to \(A_{n}\). We do this by, as the name of the method suggests, partitioning our matrix into 4 smaller submatrices.


We can calculate the inverse of matrix \(A_{n}\) using the classical adjoint or Gauss-Jordan method. Once we have the inverse of matrix \(A_{n}\) we can compute the inverse of the other submatrices of matrix \(A_{n+1}\) using the following formulas.

For a more thorough explanation, please check out this excellent article about the partition method. Now, let’s take a look at an example.

First we compute the inverse of \(A\) by using the classical adjoint method.

Now that we have the inverse of \(A\) we can compute the inverse of the matrix \(A_{3}\) using the above mentioned formulas.

Finally, putting everything together, we have the inverse matrix for the matrix \(A_{3}\).

For our implementation we follow the same procedure as in our example. First we compute the inverse of submatrix \(A\), using the classical adjoint method. Next we solve the equation for each element of the inverse matrix \(A_{3}^{-1}\).

#include "matrix.h"

Matrix& Matrix::calculate_inverse_partition(Matrix& out) const
  float det_A_inv = 1 / (data[0][0] * data[1][1]
                         - data[0][1] * data[1][0]);
  float inv_A[2][2];

  inv_A[0][0] =  data[1][1] * det_A_inv;
  inv_A[0][1] = -data[0][1] * det_A_inv;
  inv_A[1][0] = -data[1][0] * det_A_inv;
  inv_A[1][1] =  data[0][0] * det_A_inv;

  // d[2][2] = 1 / (data[2][2]
                        - (data[0][2] * data[2][0] * inv_A[0][0]
                           + data[0][2] * data[2][1] * inv_A[1][0]
                           + data[1][2] * data[2][0] * inv_A[0][1]
                           + data[1][2] * data[2][1] * inv_A[1][1]));

  // Y[0][2] =[2][2] * (inv_A[0][0] * data[0][2]
                                      + inv_A[0][1] * data[1][2]);[1][2] =[2][2] * (inv_A[1][0] * data[0][2]
                                      + inv_A[1][1] * data[1][2]);

  // Z[2][0] =[2][2] * (data[2][0] * inv_A[0][0]
                                      + data[2][1] * inv_A[1][0]);[2][1] =[2][2] * (data[2][0] * inv_A[0][1]
                                      + data[2][1] * inv_A[1][1]);

  // X[0][0] = inv_A[0][0]
                   - inv_A[0][0] * data[0][2] *[2][0]
                   - inv_A[0][1] * data[1][2] *[2][0];[0][1] = - inv_A[0][0] * data[0][2] *[2][1]
                   + inv_A[0][1]
                   - inv_A[0][1] * data[1][2] *[2][1];[1][0] = inv_A[1][0]
                   - inv_A[1][0] * data[0][2] *[2][0]
                   - inv_A[1][1] * data[1][2] *[2][0];[1][1] = -inv_A[1][0] * data[0][2] *[2][1]
                   + inv_A[1][1]
                   - inv_A[1][1] * data[1][2] *[2][1];

  return out;

The partition method is fairly complex compared to the other methods we have covered. The main advantage that it provides is that it allows us to compute the inverse of a matrix by recursively computing the inverses of smaller submatrices. The method can also be combined with any other matrix inversion method, as we have in our example and implementation with the classical adjoint method.


Now that we have implemented our 3 different methods, let’s take a look at how well they perform. We benchmark our implementation for a set of 1.000.000, 100.000 and 1.000 matrices, and show the time taken to invert them in microseconds (μs).

# of matrices Gauss-Jordan Classical adjoint Partition
1.000.000 506.193 μs 408.457 μs 398.056 μs
100.000 51.908 μs 42.772 μs 41.519 μs
1.000 544 μs 427 μs 421 μs

As we can see, all three methods show similar performance. Our Gauss-Jordan implementation is slightly slower in our benchmarks, yet this is by no means proof that this method is slower for \(3 \times 3\) matrices, as our implementations leaves plenty of room for optimization.

We can conclude that for matrices of smaller order, such as \(3 \times 3\) and \(4 \times 4\) matrices, any of these three methods are viable and performant. The classical adjoint method has the advantage that it’s structure is more optimized to benefit from parallel computing, while the Gauss-Jordan and partitioning method are more suited for matrices of larger order.

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