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To define rank of matrix, we should have prior knowledge of sub-matrices and minors of a matrix. Let A be a given matrix. Matrix obtained by deleting some rows and some columns of matrix A is known as the sub-matrix of A. A matrix is called a sub-matrix of itself as it is obtained by leaving zero number of rows and zero number of columns. Minor of the matrix is the determinant of the square matrix that is obtained by deleting one row and one column from some larger square matrix.

The rank of the matrix refers to the number of linearly independent rows or columns in the matrix. ρ(A) is used to denote the rank of matrix A. A matrix is said to be of rank zero when all of its elements become zero. The rank of the matrix is the dimension of the vector space obtained by its columns. The rank of a matrix cannot exceed more than the number of its rows or columns. The rank of the null matrix is zero.

The nullity of a matrix is defined as the number of vectors present in the null space of a given matrix. In other words, it can be defined as the dimension of the null space of matrix A called the nullity of A. Rank+Nullity is the number of all columns in matrix A.

Rank linear algebra refers to finding column rank or row rank collectively known as the rank of the matrix.

Zero matrices have no non-zero row. Hence it has an independent row (or column). So, the rank of the zero matrix is zero.

When the rank equals the smallest dimension it is called full rank matrix.

Let A = (a\[_{ij}\])\[_{m \times n}\] be a matrix. A positive integer r is said to be the rank of matrix A if

Matrix A have at least one r-rowed minor which is different from zero

Every (r + 1) row minor of matrix A is zero.

Let A = (a\[_{ij}\])\[_{m \times n}\] is a matrix and B is its sub-matrix of order r, then ∣β∣ the determinant is called an r-rowed minor of A.

Minor method

Echelon form

(i) If a matrix contains at least one non zero elements, then ρ (A) ≥ 1

(ii) The rank of the identity matrix I_{n} is n.

(iii) If the rank of matrix A is r, then there exists at least one minor of order r which does not vanish. Every minor of matrix A of order (r + 1) and higher-order (if any) vanishes.

(iv) If A is a matrix of m × n , then

ρ(A) ≤ min {m, n}

(v) A square matrix A of order n has to inverse

if and only if ρ(A) = n.

(i) The first element of every non zero row should be 1.

(ii) The row in which every element is zero, then that row should be below the non zero rows.

(iii) Total number of zeroes in the next non zero row should be more than the number of zeroes in the previous non zero row.

By elementary operations, we can easily bring the given matrix to the echelon form.

Note: The rank of a matrix does not change if we perform the following elementary row operations are applied to the matrix:

(a) Two rows are interchanged (R_{i} ↔ R_{j})

(b) A row is multiplied by a non-zero constant, (R_{i} ↔ kR_{i}) where k ≠ 0

(c) A constant multiple of another row is added to a given row (R_{i} ⟶ R_{i} + kR_{j}), where i ≠ j.

Ques: Find the Rank of a Matrix Using the Echelon Form.

\[\begin{bmatrix} 1 & 2 & 3 \\ 2 & 3 & 4\\ 3 & 5 & 7 \end{bmatrix}\]

Sol: First we will convert the given matrix into Echelon form and then find a number of non zero rows.

The order of A is 3 × 3. Hence ρ(A) ≤ 3

A = \[\begin{bmatrix} 1 & 2 & 3 \\ 2 & 3 & 4\\ 3 & 5 & 7 \end{bmatrix}\]

Convert R_{2} ⟶ R_{2} - 2R_{1} and R_{3} ⟶ R_{3} - 3R_{1}

~ \[\begin{bmatrix} 1 & 2 & 3 \\ 0 & -1 & -2\\ 0 & -1 & -2 \end{bmatrix}\]

Again R_{3} ⟶ R_{3} - R_{2}

~ \[\begin{bmatrix} 1 & 2 & 3 \\ 0 & -1 & -2\\ 0 & 0 & 0 \end{bmatrix}\]

Now, the above matrix is in echelon form. In this number non zero rows is 2. Hence rank of matrix 2.

From the above discussion, we can conclude that if we have to find the rank of a matrix by searching the highest order non-vanishing minor is quite tedious when the order of the matrix is quite large. There is another easy method to find the rank of a matrix even if the order of the matrix is quite high. This method is used to find the rank of an equivalent row-echelon form of the matrix. If a matrix is in row-echelon form, then all entries below the leading diagonal (it is the line joining the positions of the diagonal elements like a_{11}, a_{22}, a_{33} of the matrix) are zeros. So, to check whether a minor is zero or not is quite simple.

FAQ (Frequently Asked Questions)

1. How Do You Find the Rank of a Matrix?

Ans: Rank of a matrix can be found by counting the number of non-zero rows or non-zero columns. Therefore, if we have to find the rank of a matrix, we will transform the given matrix to its row echelon form and then count the number of non-zero rows.

2. Can the Rank of a Matrix be Zero?

Ans: Yes it can be zero because zero matrices have rank zero.

3. What is the Nullity of a Zero Matrix?

Ans: A matrix whose only entries are zero, then the column space would be only zero vectors. The rank is zero then. The nullity is the dimension of the nullspace, the subspace of the domain that consists of all vectors from the domain when the matrix is applied to it results in the zero vector.