How to Find Inverse Matrix?
There are many different methods for finding the inverse of a given matrix. Some of them are: inverse of a matrix by Gauss-Jordan elimination and
inverse of a matrix using minors, cofactors and adjugate. We will present the second one.
The minor of an element of any $n\times n$ matrix is a $(n-1)\times (n-1)$ matrix determinant. If we delete the row and column containing the element, then we get appropriate minor.
For example, the minor of the element $a$, element in the first row and first column, of the matrix
The matrix formed by all of the cofactors of a square matrix $A$ is called the
cofactor matrix. The $a_{ij}$ cofactor is derived by multiplying the minor by
$( - 1 )^{i + j}$. For example, the cofactor matrix of 3x3
matrix $A=\left(
\begin{array}{ccc}
a & b & c \\
d& e & f \\
g & h & i \\
\end{array}
\right)$ is $$\begin{align}\left(
\begin{array}{ccc}
( - 1 )^{1 + 1}\left|
\begin{array}{cc}
e & f \\
h &i \\
\end{array}
\right| & ( - 1 )^{1 + 2} \left|
\begin{array}{cc}
d & f \\
g &i \\
\end{array}
\right|& ( - 1 )^{1 + 3} \left|
\begin{array}{cc}
d & e \\
g &h \\
\end{array}
\right|\\
\
( - 1 )^{2 + 1}\left|
\begin{array}{cc}
b & c \\
h &i \\
\end{array}
\right|& ( - 1 )^{2 + 2}\left|
\begin{array}{cc}
a & c \\
g &i \\
\end{array}
\right| & ( - 1 )^{2 + 3}\left|
\begin{array}{cc}
a & b \\
g &h \\
\end{array}
\right| \\
\
( - 1 )^{3 + 1}\left|
\begin{array}{cc}
b & c \\
e &f \\
\end{array}
\right| & ( - 1 )^{3 + 2}\left|
\begin{array}{cc}
a & c \\
d &f \\
\end{array}
\right| & ( - 1 )^{3 + 3}\left|
\begin{array}{cc}
a & b \\
d &e \\
\end{array}
\right| \\
\end{array}
\right)\\
\newline =
\left(
\begin{array}{ccc}
+\left|
\begin{array}{cc}
e & f \\
h &i \\
\end{array}
\right| & - \left|
\begin{array}{cc}
d & f \\
g &i \\
\end{array}
\right|& + \left|
\begin{array}{cc}
d & e \\
g &h \\
\end{array}
\right|\\
\
-\left|
\begin{array}{cc}
b & c \\
h &i \\
\end{array}
\right|& +\left|
\begin{array}{cc}
a & c \\
g &i \\
\end{array}
\right| & -\left|
\begin{array}{cc}
a & b \\
g &h \\
\end{array}
\right| \\
\
+\left|
\begin{array}{cc}
b & c \\
e &f \\
\end{array}
\right| & -\left|
\begin{array}{cc}
a & c \\
d &f \\
\end{array}
\right| &+\left|
\begin{array}{cc}
a & b \\
d &e \\
\end{array}
\right| \\
\end{array}
\right)\end{align}$$
The
adjugate of a square matrix is the transpose of its cofactor matrix. The transpose of a matrix is a new matrix whose rows are the columns of the original. So,
$$adj(A)=\left(
\begin{array}{ccc}
+\left|
\begin{array}{cc}
e & f \\
h &i \\
\end{array}
\right| & -\left|
\begin{array}{cc}
b & c \\
h &i \\
\end{array}
\right| & +\left|
\begin{array}{cc}
b & c \\
e &f \\
\end{array}
\right| \\
\
-\left|
\begin{array}{cc}
d & f \\
g &i \\
\end{array}
\right|& +\left|
\begin{array}{cc}
a & c \\
g &i \\
\end{array}
\right| & -\left|
\begin{array}{cc}
a & c \\
d &f \\
\end{array}
\right| \\
\
+\left|
\begin{array}{cc}
d & e \\
g &h \\
\end{array}
\right| & -\left|
\begin{array}{cc}
a & b \\
g &h \\
\end{array}
\right| & +\left|
\begin{array}{cc}
a & b \\
d &e \\
\end{array}
\right| \\
\end{array}
\right)$$
For any nxn square matrix $A$, if $det(A)\ne0$, then the inverse matrix is determined by the formula
$$A^{-1}=\frac{1}{det(A)}adj(A)$$
To find the inverse matrix of the given matrix $A$, we need to check whether a matrix $A$ invertible.
- If $det (A)\ne0$, the matrix $A$ is invertible;
- If $det (A)=0$, the matrix $A$ is not invertible or singular.
By help of the nxn inverse matrix calculator we can easily calculate the inverse matrix of the given matrix.
If we assume that the matrix $A$ is invertible, i.e. $det(A)\ne 0$, in the following we will give a stepwise guide for calculation the inverse matrix of the given matrix:
- Find the determinant of the matrix $A$, $det(A)$;
- Find the minors of the matrix $A$;
- Find the cofactor matrix,
- Find the adjugate;
- Inverse matrix of the matrix $A$ is the scalar multiplication of the adjugate by $\frac 1{det(A)}$.
For example, let us find the inverse matrix of the matrix $A=\left(
\begin{array}{ccc}
10 & 20 & 10 \\
4 & 5 & 6 \\
2 & 3 & 5 \\
\end{array}
\right)$
Firstly, using the 3x3 matrix determinant formula, we obtain $det(A)=-70$. The cofactor matrix is
$$\begin{align}\left(
\begin{array}{ccc}
( - 1 )^{1 + 1}\left|
\begin{array}{cc}
5 & 6 \\
3 &5 \\
\end{array}
\right| & ( - 1 )^{1 + 2} \left|
\begin{array}{cc}
4 & 6 \\
2 &5 \\
\end{array}
\right|& ( - 1 )^{1 + 3} \left|
\begin{array}{cc}
4 & 5 \\
2 &3 \\
\end{array}
\right|\\
\\
( - 1 )^{2 + 1}\left|
\begin{array}{cc}
20 & 10 \\
3 &5 \\
\end{array}
\right|& ( - 1 )^{2 + 2}\left|
\begin{array}{cc}
10 & 10 \\
2 &5 \\
\end{array}
\right| & ( - 1 )^{2 + 3}\left|
\begin{array}{cc}
10 & 20 \\
2 &3 \\
\end{array}
\right| \\
\\
( - 1 )^{3 + 1}\left|
\begin{array}{cc}
20 & 10 \\
5 &6 \\
\end{array}
\right| & ( - 1 )^{3 + 2}\left|
\begin{array}{cc}
10 & 10 \\
4 &6 \\
\end{array}
\right| & ( - 1 )^{3 + 3}\left|
\begin{array}{cc}
10 & 20 \\
4 &5 \\
\end{array}
\right| \\
\end{array}
\right)\\
\newline =
\left(
\begin{array}{ccc}
7 & -8& 2
\\
-70& 30&10
\\
70 & -20 &-30\\
\end{array}
\right)\end{align}$$
If we transpose the cofactor matrix, we get
$$\left(
\begin{array}{ccc}
7 & -70& 70
\\
-8& 30&-20
\\
2 & 10 &-30\\
\end{array}
\right)$$
Finally, the inverse matrix $A^{-1}$ of the matrix $A$ is
$$A^{-1}=\frac 1{-70}\left(
\begin{array}{ccc}
7 & -70& 70
\\
-8& 30&-20
\\
2 & 10 &-30\\
\end{array}
\right)=\left(
\begin{array}{ccc}
-\frac 1{10} & 1& 1
\\
\frac4{35}& -\frac37&\frac27
\\
-\frac{1}{35} & -\frac17 &\frac37\\
\end{array}
\right)$$
The nxn inverse matrix work with steps shows the complete step-by-step calculation for finding a determinant of 4x4, 3x3 or 2x2 matrix $A$ using the matrix inverse formula. For any other matrices, just supply real numbers as elements of matrix and click on the GENERATE WORK button. The grade school students and people who study math use this nxn inverse matrix calculator to generate the work, verify the results of matrix inverse derived by hand, or do their homework problems efficiently. The grade school students can also use this calculator for solving system of linear equations.