We learned how important are matrices and determinants and also studied about their wide applications. The knowledge of Minors and Cofactors is compulsory in the computation of adjoint of a matrix and hence in its inverse as well as in the computation of determinant of a square matrix. This technique of computing determinant is known as Cofactor Expansion. Let’s get started!

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## Minor of a Determinant

A minor is defined as a value computed from the determinant of a square matrix which is obtained after crossing out a row and a column corresponding to the element that is under consideration. Minor of an element a_{ij} of a determinant is the determinant obtained by deleting its i^{th} row and j^{th} column in which element a_{ij} lies. Minor of an element a_{ij} is denoted by M_{ij}.

**Browse more Topics under Determinants**

- Determinant of a Matrix
- Properties of Determinants
- Area of a Triangle Using Determinants
- Adjoint and Inverse of a Matrix
- Solution of System of Linear Equations using Inverse of a Matrix

## Cofactor of a Determinant

The cofactor is defined as the signed minor. Cofactor of an element a_{ij}, denoted by A_{ij} is defined by A = (–1)^{i+j} M, where M is minor of a_{ij}.

### Note

- We note that if the sum i+j is even, then A
_{i}_{j}= M_{ij}, and that if the sum is odd, then A_{i}_{j}= −M_{ij}. - Hence, the only difference between the related minor entries and cofactors may be a sign change or nothing at all.
- Whether
_{ }or A_{i}_{j}= M_{ij }or A_{i}_{j}= −M_{ij} - has a pattern for square matrices as illustrated:

_{12}= −M

_{12}. Of course, if you forget, you can always use the formula C

_{ij }= (−1)

^{i+j }M

_{ij},

_{12}=(−1)

^{1+2 }M

_{ij }= (−1)

^{3 }M

_{ij }= −M

_{ij}

## Example

Find the minors and cofactors of all the elements of the determinant $$\begin{vmatrix} 1 & -2 \\ 4 & 3 \end{vmatrix}$$

_{ij}is M

_{ij}.

Here a

_{11}= 1. So M

_{11}= Minor of a

_{11}= 3

M

_{12}= Minor of the element a

_{12}= 4

M

_{21}= Minor of the element a

_{2}

_{1}= –2

M

_{22}= Minor of the element a

_{22}= 1

Now, cofactor of a_{ij} is A_{ij}. So,

A_{11 } = (–1)^{1+1}, M_{11 } = (–1)^{2} (3) = 3

A_{12} = (–1)^{1+2}, M_{12} = (–1)^{3} (4) = –4

A_{21} = (–1)^{2+1}, M_{21} = (–1)^{3} (–2) = 2

A_{22} = (–1)^{2+2}, M_{22} = (–1)^{4} (1) = 1

## Solved Examples for You

**Question 1: Let A=[aij]n×n be a square matirx and let cij be cofactor of aij in A. If C=[cij], then**

**|A| = |C|****|C| = |A|**^{n-1}**|C| = |A|**^{n-2}**none of these**

**Answer :** We know that adjA = C^{T} where C is the cofactor matrix of A.

Also |Adj A|=|A|^{n−1}Now |C^{T}| = |Adj A|

=|A|^{n-1} where n it the order of the square matrix.

**Question 2: The minors and cofactors of -4 and 9 in determinant $$\begin{vmatrix} -1 & 2 & 3 \\ -4 & 5 & -6 \\ -7 & -8 & 9 \end{vmatrix}$$ are respectively**

**42, 42; 3, 3****42, -42; 3, 3****42, -42; 3, -3****42, 3; 42, 3**

**Answer : **Minor of -4 is $$\begin{vmatrix} 2 & 3 \\ -8 & 9 \end{vmatrix} = 42$$^{1+2}(42) = −42

Minor of 9 is ^{3+3 }.(3) = 3. Therefore, the answer is option B

**Question 3: What is meant by cofactor of a matrix?**

**Answer:** A cofactor refers to the number you attain on removing the column and row of a particular element existing in a matrix.

**Question 4: What is meant by a minor matrix?**

**Answer:** A minor refers to the square matrix’s determinant whose formation takes place by deleting one column and one row from some larger square matrix.

**Question 5: Can we say that the adjoint is the same as the reverse?**

**Answer:** The adjoint of a matrix is also known as the adjugate of a matrix. It refers to the transpose of the cofactor matrix of that particular matrix. For a matrix A, the denotation of adjoint is as adj (A). On the other hand, the inverse of a matrix A refers to a matrix which on multiplication by matrix A, results in an identity matrix.

**Question 6: What is meant by rank of a matrix?**

**Answer:** The rank of a matrix refers to the maximum number of linearly independent column vectors that exist in the matrix. Furthermore, it is also the maximum number of linearly independent row vectors that exist in the matrix.