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Let \(V\) be \({\mathbb{R}^n}\) with a basis \(B = \left\{ {{b_1},........{b_n}} \right\}\); let \(W\) be \({\mathbb{R}^n}\) with the standard basis, denoted here by \(\xi \); and consider the identity transformation \(I:V \to W\) , where \(I\left( {\rm{x}} \right) = {\rm{x}}\). Find the matrix for \(I\) relative to \(B\) and \(\xi \). What was this matrix called in section 4.4?

Short Answer

Expert verified

The matrix \(I\) relative to \(B\)and \(\xi \) is the set of vectors \(\left( {{{\bf{b}}_1}\,\,\,{{\bf{b}}_2}........\,{{\bf{b}}_n}} \right)\). The matrix \(\left( {{{\bf{b}}_1}\,\,\,{{\bf{b}}_2}........\,{{\bf{b}}_n}} \right)\) is called the change of coordinates matrix.

Step by step solution

01

Use the given information 

It is given that \(I:V \to W\), such that, \(I\left( {\bf{x}} \right) = {\bf{x}}\) . So, for each \({j^{th}}\) vector of the basis \(B\) and \(\xi \), the identity \(I\left( {\bf{x}} \right) = {\bf{x}}\) must be satisfied, such that \({\left( {I\left( {{{\bf{b}}_j}} \right)} \right)_\xi } = {{\bf{b}}_j}\). This implies that matrix \(I\) relative to \(B\)and \(\xi \) is the set of vectors \(\left( {{{\bf{b}}_1}\,\,\,{{\bf{b}}_2}........\,{{\bf{b}}_n}} \right)\).

02

Define the matrix

In section 4.4, the change of coordinates matrix is defined, which changes the \(B\)-coordinates of a vector \(x\) into the standard coordinates for \(x\), where the change is carried out in \({\mathbb{R}^n}\)for the basis \(B = \left( {{{\bf{b}}_1}\,\,\,{{\bf{b}}_2}........\,{{\bf{b}}_n}} \right)\). So, \(\left( {{{\bf{b}}_1}\,\,\,{{\bf{b}}_2}........\,{{\bf{b}}_n}} \right)\) is called the change of coordinates matrix.

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Most popular questions from this chapter

Question 18: It can be shown that the algebraic multiplicity of an eigenvalue \(\lambda \) is always greater than or equal to the dimension of the eigenspace corresponding to \(\lambda \). Find \(h\) in the matrix \(A\) below such that the eigenspace for \(\lambda = 5\) is two-dimensional:

\[A = \left[ {\begin{array}{*{20}{c}}5&{ - 2}&6&{ - 1}\\0&3&h&0\\0&0&5&4\\0&0&0&1\end{array}} \right]\]

Show that \(I - A\) is invertible when all the eigenvalues of \(A\) are less than 1 in magnitude. (Hint: What would be true if \(I - A\) were not invertible?)

Question: In Exercises \({\bf{5}}\) and \({\bf{6}}\), the matrix \(A\) is factored in the form \(PD{P^{ - {\bf{1}}}}\). Use the Diagonalization Theorem to find the eigenvalues of \(A\) and a basis for each eigenspace.

5. \(\left( {\begin{array}{*{20}{c}}2&2&1\\1&3&1\\1&2&2\end{array}} \right) = \left( {\begin{array}{*{20}{c}}1&1&2\\1&0&{ - 1}\\1&{ - 1}&0\end{array}} \right)\left( {\begin{array}{*{20}{c}}5&0&0\\0&1&0\\0&0&1\end{array}} \right)\left( {\begin{array}{*{20}{c}}{\frac{1}{4}}&{\frac{1}{2}}&{\frac{1}{4}}\\{\frac{1}{4}}&{\frac{1}{2}}&{ - \frac{3}{4}}\\{\frac{1}{4}}&{ - \frac{1}{2}}&{\frac{1}{4}}\end{array}} \right)\)

Let\(B = \left\{ {{{\bf{b}}_1},{{\bf{b}}_2},{{\bf{b}}_3}} \right\}\) be a basis for a vector space\(V\). Find \(T\left( {3{{\bf{b}}_1} - 4{{\bf{b}}_2}} \right)\) when \(T\) isa linear transformation from \(V\) to \(V\) whose matrix relative to \(B\) is

\({\left( T \right)_B} = \left( {\begin{aligned}0&{}&{ - 6}&{}&1\\0&{}&5&{}&{ - 1}\\1&{}&{ - 2}&{}&7\end{aligned}} \right)\)

Question: Find the characteristic polynomial and the eigenvalues of the matrices in Exercises 1-8.

  1. \(\left[ {\begin{array}{*{20}{c}}2&7\\7&2\end{array}} \right]\)
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