Question: In Exercises 17-22, determine which sets of vectors are orthonormal. If a set is only orthogonal, normalize the vectors to produce an orthonormal set.

17. \(\left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{1}{3}}\\{\frac{1}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{ - \frac{1}{2}}\\0\\{\frac{1}{2}}\end{array}} \right)\)

Short Answer

Expert verified

The set of vectors \(\left\{ {{\bf{u}},{\bf{v}}} \right\}\) is not orthonormal.

The orthonormal set is \(\left\{ {\frac{{\bf{u}}}{{\left\| {\bf{u}} \right\|}},\frac{{\bf{v}}}{{\left\| {\bf{v}} \right\|}}} \right\} = \left\{ {\left( {\begin{array}{*{20}{c}}{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{ - \frac{{\sqrt 2 }}{2}}\\0\\{\frac{{\sqrt 2 }}{2}}\end{array}} \right)} \right\}\).

Step by step solution

01

Definition of orthonormal sets

A set \(\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}, \ldots ,{{\bf{u}}_p}} \right\}\) is called anorthonormal setwhen it is an orthogonal set of unit vectors.

02

Check whether the set of vectors are orthogonal

Consider that, \({\bf{u}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{1}{3}}\\{\frac{1}{3}}\end{array}} \right),{\rm{ }}{\bf{v}} = \left( {\begin{array}{*{20}{c}}{ - \frac{1}{2}}\\0\\{\frac{1}{2}}\end{array}} \right)\).

Check whether the set of the vectors is orthogonal, as shown below:

\(\begin{array}{c}{\bf{u}} \cdot {\bf{v}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{1}{3}}\\{\frac{1}{3}}\end{array}} \right) \cdot \left( {\begin{array}{*{20}{c}}{ - \frac{1}{2}}\\0\\{\frac{1}{2}}\end{array}} \right)\\ = \frac{1}{3}\left( { - \frac{1}{2}} \right) + \frac{1}{3}\left( 0 \right) + \frac{1}{3}\left( {\frac{1}{2}} \right)\\ = - \frac{1}{6} + 0 + \frac{1}{6}\\ = 0\end{array}\)

It is observed that \(\left\{ {{\bf{u}},{\bf{v}}} \right\}\) is an orthogonal set because \({\bf{u}} \cdot {\bf{v}} = 0\).

03

Check whether the set of vectors is orthonormal

Check whether the set of vectors is orthonormal as shown below:

\(\begin{array}{c}{\left\| {\bf{u}} \right\|^2} = {\bf{u}} \cdot {\bf{u}}\\ = {\left( {\frac{1}{3}} \right)^2} + {\left( {\frac{1}{3}} \right)^2} + {\left( {\frac{1}{3}} \right)^2}\\ = \left( {\frac{1}{9}} \right) + \left( {\frac{1}{9}} \right) + \left( {\frac{1}{9}} \right)\\ = \frac{3}{9}\\ = \frac{1}{3}\\{\left\| {\bf{v}} \right\|^2} = {\bf{v}} \cdot {\bf{v}}\\ = {\left( { - \frac{1}{2}} \right)^2} + {\left( 0 \right)^2} + {\left( {\frac{1}{2}} \right)^2}\\ = \left( {\frac{1}{4}} \right) + 0 + \left( {\frac{1}{4}} \right)\\ = \frac{2}{4}\\ = \frac{1}{2}\end{array}\)

It is observed that \(\left\{ {{\bf{u}},{\bf{v}}} \right\}\) is not an orthonormal set.

04

Normalize the vectors to produce an orthonormal set

Normalize the vectors to produce the orthonormal set as shown below:

\(\begin{array}{c}\left\{ {\frac{{\bf{u}}}{{\left\| {\bf{u}} \right\|}},\frac{{\bf{v}}}{{\left\| {\bf{v}} \right\|}}} \right\} = \left\{ {\frac{1}{{\sqrt {\frac{1}{3}} }}\left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{1}{3}}\\{\frac{1}{3}}\end{array}} \right),\frac{1}{{\sqrt {\frac{1}{2}} }}\left( {\begin{array}{*{20}{c}}{ - \frac{1}{2}}\\0\\{\frac{1}{2}}\end{array}} \right)} \right\}\\ = \left\{ {\left( {\begin{array}{*{20}{c}}{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{ - \frac{{\sqrt 2 }}{2}}\\0\\{\frac{{\sqrt 2 }}{2}}\end{array}} \right)} \right\}\end{array}\)

Therefore, the orthonormal set is \(\left\{ {\frac{{\bf{u}}}{{\left\| {\bf{u}} \right\|}},\frac{{\bf{v}}}{{\left\| {\bf{v}} \right\|}}} \right\} = \left\{ {\left( {\begin{array}{*{20}{c}}{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\\{\frac{{\sqrt 3 }}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{ - \frac{{\sqrt 2 }}{2}}\\0\\{\frac{{\sqrt 2 }}{2}}\end{array}} \right)} \right\}\).

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Most popular questions from this chapter

Compute the least-squares error associated with the least square solution found in Exercise 3.

Let \({{\bf{u}}_1},......,{{\bf{u}}_p}\) be an orthogonal basis for a subspace \(W\) of \({\mathbb{R}^n}\), and let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be defined by \(T\left( x \right) = {\rm{pro}}{{\rm{j}}_W}x\). Show that \(T\) is a linear transformation.

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

10. \(\left( {\begin{aligned}{{}{}}{ - 1} & 6 & 6 \\ 3 & { - 8}&3\\1&{ - 2}&6\\1&{ - 4}&{ - 3}\end{aligned}} \right)\)

Exercises 19 and 20 involve a design matrix \(X\) with two or more columns and a least-squares solution \(\hat \beta \) of \({\bf{y}} = X\beta \). Consider the following numbers.

(i) \({\left\| {X\hat \beta } \right\|^2}\)—the sum of the squares of the “regression term.” Denote this number by \(SS\left( R \right)\).

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)—the sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)—the “total” sum of the squares of the -values. Denote this number by \(SS\left( T \right)\).

Every statistics text that discusses regression and the linear model \(y = X\beta + \in \) introduces these numbers, though terminology and notation vary somewhat. To simplify matters, assume that the mean of the -values is zero. In this case, \(SS\left( T \right)\) is proportional to what is called the variance of the set of \(y\)-values.

20. Show that \({\left\| {X\hat \beta } \right\|^2} = {\hat \beta ^T}{X^T}{\bf{y}}\). (Hint: Rewrite the left side and use the fact that \(\hat \beta \) satisfies the normal equations.) This formula for is used in statistics. From this and from Exercise 19, obtain the standard formula for \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

In Exercises 5-14, the space is \(C\left[ {0,2\pi } \right]\) with inner product (6).

7. Show that \({\left\| {\cos kt} \right\|^2} = \pi \) and \({\left\| {\sin kt} \right\|^2} = \pi \) for \(k > 0\).

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