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Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be a linear transformation that preserves lengths; that is, \(\left\| {T\left( {\bf{x}} \right)} \right\| = \left\| {\bf{x}} \right\|\) for all x in \({\mathbb{R}^n}\).

  1. Show that T also preserves orthogonality; that is, \(T\left( {\bf{x}} \right) \cdot T\left( {\bf{y}} \right) = 0\) whenever \({\bf{x}} \cdot {\bf{y}} = 0\).
  2. Show that the standard matrix of T is an orthogonal matrix.

Short Answer

Expert verified
  1. It is proved that \(T\) preserves orthogonality.
  2. It is proved that the standard matrix of T is an orthogonal matrix.

Step by step solution

01

The Pythagoras Theorem

The vectors \({\bf{u}}\) and \({\bf{v}}\) are said to beorthogonal such that if \({\left\| {{\bf{u}} + {\bf{v}}} \right\|^2} = {\left\| {\bf{u}} \right\|^2} + {\left\| {\bf{v}} \right\|^2}\).

02

Show that T preserves orthogonality

Consider that \({\bf{x}} \cdot {\bf{y}} = 0\). According to the Pythagoras Theorem, \({\left\| {\bf{x}} \right\|^2} + {\left\| {\bf{y}} \right\|^2} = {\left\| {{\bf{x}} + {\bf{y}}} \right\|^2}\). The linear transformation T retains lengths and is linear.

\({\left\| {T\left( {\bf{x}} \right)} \right\|^2} + {\left\| {T\left( {\bf{y}} \right)} \right\|^2} = {\left\| {T\left( {{\bf{x}} + {\bf{y}}} \right)} \right\|^2} = {\left\| {T\left( {\bf{x}} \right) + T\left( {\bf{y}} \right)} \right\|^2}\)

The above equation demonstrates that \(T\left( {\bf{x}} \right)\) and \(T\left( {\bf{y}} \right)\) is orthogonal, according to the Pythagoras Theorem.

Therefore, \(T\) preserves orthogonality.

Hence, it is proved that \(T\) preserves orthogonality.

03

Show that the standard matrix of T is orthogonal

It is known that thesquare invertible matrix\(U\)is an orthogonal matrix, such that, \({U^{ - 1}} = {U^T}\). This square matrix contains orthonormal columns. The square matrix with orthonormal columns is clearly orthogonal.

The standard matrix of \(T\) is represented by \(\left[ {\begin{array}{*{20}{c}}{T\left( {{e_1}} \right)}& \ldots &{T\left( {{e_n}} \right)}\end{array}} \right]\), where \({e_1}, \ldots ,{e_n}\) are the columns of the identity matrix. Then, the orthonormal set is \(\left[ {\begin{array}{*{20}{c}}{T\left( {{e_1}} \right)}& \ldots &{T\left( {{e_n}} \right)}\end{array}} \right]\) since \(T\) retains both orthogonality and lengths (since the columns of the identity matrix produce an orthonormal set). Furthermore, the square matrix contains orthonormal columns that is known as the orthogonal matrix.

Hence, it is proved that the standard matrix of T is orthogonal.

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Most popular questions from this chapter

In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

7.\[y = \left[ {\begin{aligned}1\\3\\5\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\3\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}5\\1\\4\end{aligned}} \right]\]

Let \(X\) be the design matrix in Example 2 corresponding to a least-square fit of parabola to data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\). Suppose \({x_1}\), \({x_2}\) and \({x_3}\) are distinct. Explain why there is only one parabola that best, in a least-square sense. (See Exercise 5.)

In Exercises 1-4, find the equation \(y = {\beta _0} + {\beta _1}x\) of the least-square line that best fits the given data points.

  1. \(\left( {0,1} \right),\left( {1,1} \right),\left( {2,2} \right),\left( {3,2} \right)\)

Let \({\mathbb{R}^{\bf{2}}}\) have the inner product of Example 1, and let \({\bf{x}} = \left( {{\bf{1}},{\bf{1}}} \right)\) and \({\bf{y}} = \left( {{\bf{5}}, - {\bf{1}}} \right)\).

a. Find\(\left\| {\bf{x}} \right\|\),\(\left\| {\bf{y}} \right\|\), and\({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^{\bf{2}}}\).

b. Describe all vectors\(\left( {{z_{\bf{1}}},{z_{\bf{2}}}} \right)\), that are orthogonal to y.

Find the distance between \({\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{10}\\{ - 3}\end{aligned}} \right)\) and \({\mathop{\rm y}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\{ - 5}\end{aligned}} \right)\).

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