Line 21: Line 21:
 
<br>  
 
<br>  
  
:<math>
+
<math>\begin{align}
I_1 = \begin{bmatrix}
+
  & AB=BA={{I}_{n}} \\
1 \end{bmatrix}
+
&  \\  
,\  
+
& \overbrace{\left( \begin{matrix}
I_2 = \begin{bmatrix}
+
  {{a}_{11}} & {{a}_{12}} & \cdots  & {{a}_{1n}}  \\
1 & 0 \\
+
  {{a}_{21}} & {{a}_{22}} & \cdots  & {{a}_{2n}}  \\
0 & 1 \end{bmatrix}
+
  \vdots  & \vdots  & \ddots  & \vdots  \\
,\  
+
  {{a}_{n1}} & {{a}_{n2}} & \cdots  & {{a}_{nn}}  \\
I_3 = \begin{bmatrix}
+
\end{matrix} \right)}^{A}\overbrace{\left( \begin{matrix}
1 & 0 & 0 \\
+
  {{b}_{11}} & {{b}_{12}} & \cdots  & {{b}_{1n}}  \\
0 & 1 & 0 \\
+
  {{b}_{21}} & {{b}_{22}} & \cdots  & {{b}_{2n}}  \\
0 & 0 & 1 \end{bmatrix}
+
  \vdots  & \vdots  & \ddots  & \vdots  \\
,\ \cdots ,\  
+
  {{b}_{n1}} & {{b}_{n2}} & \cdots & {{b}_{nn}}  \\
I_n = \begin{bmatrix}
+
\end{matrix} \right)}^{B}=\overbrace{\left( \begin{matrix}
1 & 0 & \cdots & 0 \\
+
  1 & 0 & \cdots & 0 \\
0 & 1 & \cdots & 0 \\
+
  0 & 1 & \cdots & 0 \\
\vdots & \vdots & \ddots & \vdots \\
+
  \vdots & \vdots & \ddots & \vdots   \\
0 & 0 & \cdots & 1 \end{bmatrix}
+
  0 & 0 & \cdots & 1 \\
</math>
+
\end{matrix} \right)}^{{{I}_{n}}} \\
 +
\end{align}</math>
  
 
[[Category:MA265Fall2010Walther]]
 
[[Category:MA265Fall2010Walther]]

Revision as of 15:22, 27 November 2010

The Inverse of a Matrix

In linear algebra, the study of matrices is one of the fundamental basis of this subject. One of the concepts within this study, is the notion of an invertible or nonsingular matrix.


Definition

A square matrix is said to be invertible or nonsingular, if when multiplied by another similar matrix, the result yields the identity matrix.


Let A and B be n × n matrices.


A is invertible or nonsingular and B is its inverse if:


$ \begin{align} & AB=BA={{I}_{n}} \\ & \\ & \overbrace{\left( \begin{matrix} {{a}_{11}} & {{a}_{12}} & \cdots & {{a}_{1n}} \\ {{a}_{21}} & {{a}_{22}} & \cdots & {{a}_{2n}} \\ \vdots & \vdots & \ddots & \vdots \\ {{a}_{n1}} & {{a}_{n2}} & \cdots & {{a}_{nn}} \\ \end{matrix} \right)}^{A}\overbrace{\left( \begin{matrix} {{b}_{11}} & {{b}_{12}} & \cdots & {{b}_{1n}} \\ {{b}_{21}} & {{b}_{22}} & \cdots & {{b}_{2n}} \\ \vdots & \vdots & \ddots & \vdots \\ {{b}_{n1}} & {{b}_{n2}} & \cdots & {{b}_{nn}} \\ \end{matrix} \right)}^{B}=\overbrace{\left( \begin{matrix} 1 & 0 & \cdots & 0 \\ 0 & 1 & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & 1 \\ \end{matrix} \right)}^{{{I}_{n}}} \\ \end{align} $

Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett