Determine which of the matrices in Exercises 7–12 are orthogonal. If orthogonal, find the inverse.

9. \(\left[ {\begin{aligned}{{}}{ - 4/5}&{\,\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right]\)

Short Answer

Expert verified

\(P\) is an orthogonal matrix and\({P^{ - 1}} = \left[ {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right]\).

Step by step solution

01

Find the characteristic equation

A matrix\(P\) with, \(n \times n\) dimension, is orthogonal if it satisfies the equation\({P^T}P = {I_n}\).

It is given that\(P = \left( {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right)\). Find the matrix\({P^T}P\)as shown below:

\(\begin{aligned}{}{P^T}P &= \left( {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right)\left( {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right)\\& = \left( {\begin{aligned}{{}}1&0\\0&1\end{aligned}} \right)\\ &= {I_2}\end{aligned}\)

02

Find the inverse

As\({P^T}P = {I_2}\), so it can be concluded that\(P\)is an orthogonal matrix. Also, the inverse of matrix\(P\)is\({P^T}\).

Find\({P^T}\)as follows:

\(\begin{aligned}{}{P^{ - 1}} &= {P^T}\\ &= \left[ {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right]\end{aligned}\)

Thus, \(P\) is an orthogonal matrix and\({P^{ - 1}} = \left[ {\begin{aligned}{{}}{ - 4/5}&{\,\,3/5}\\{3/5}&{\,\,4/5}\end{aligned}} \right]\).

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Most popular questions from this chapter

Orhtogonally diagonalize the matrices in Exercises 37-40. To practice the methods of this section, do not use an eigenvector routine from your matrix program. Instead, use the program to find the eigenvalues, and for each eigenvalue \(\lambda \), find an orthogonal basis for \({\bf{Nul}}\left( {A - \lambda I} \right)\), as in Examples 2 and 3.

40. \(\left( {\begin{aligned}{{}}{\bf{8}}&{\bf{2}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{8}}&{\bf{2}}&{ - {\bf{6}}}&{\bf{9}}\\{\bf{2}}&{\bf{2}}&{\bf{8}}&{ - {\bf{6}}}&{\bf{9}}\\{ - {\bf{6}}}&{ - {\bf{6}}}&{ - {\bf{6}}}&{{\bf{24}}}&{\bf{9}}\\{\bf{9}}&{\bf{9}}&{\bf{9}}&{\bf{9}}&{ - {\bf{21}}}\end{aligned}} \right)\)

Let B be an \(n \times n\) symmetric matrix such that \({B^{\bf{2}}} = B\). Any such matrix is called a projection matrix (or an orthogonal projection matrix.) Given any y in \({\mathbb{R}^n}\), let \({\bf{\hat y}} = B{\bf{y}}\)and\({\bf{z}} = {\bf{y}} - {\bf{\hat y}}\).

a) Show that z is orthogonal to \({\bf{\hat y}}\).

b) Let W be the column space of B. Show that y is the sum of a vector in W and a vector in \({W^ \bot }\). Why does this prove that By is the orthogonal projection of y onto the column space of B?

Find the matrix of the quadratic form. Assume x is in \({\mathbb{R}^2}\).

a. \(3x_1^2 + 2x_2^2 - 5x_3^2 - 6{x_1}{x_2} + 8{x_1}{x_3} - 4{x_2}{x_3}\)

b. \(6{x_1}{x_2} + 4{x_1}{x_3} - 10{x_2}{x_3}\)

Question: Find the principal components of the data for Exercise 2.

Let u be a unit vector in \({\mathbb{R}^n}\), and let \(B = {\bf{u}}{{\bf{u}}^T}\).

  1. Given any x in \({\mathbb{R}^n}\), compute Bx and show that Bx is the orthogonal projection of x onto u, as described in Section 6.2.
  2. Show that B is a symmetric matrix and \({B^{\bf{2}}} = B\).
  3. Show that u is an eigenvector of B. What is the corresponding eigenvalue?
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