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.

22. \(\left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt {18} }}}\\{\frac{4}{{\sqrt {18} }}}\\{\frac{1}{{\sqrt {18} }}}\end{array}} \right),{\rm{ }}\left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt 2 }}}\\0\\{ - \frac{1}{{\sqrt 2 }}}\end{array}} \right),{\rm{ }}\left( {\begin{array}{*{20}{c}}{ - \frac{2}{3}}\\{\frac{1}{3}}\\{ - \frac{2}{3}}\end{array}} \right)\)

Short Answer

Expert verified

The set of vectors \(\left\{ {{\bf{u}},{\bf{v}},{\bf{w}}} \right\}\) is an orthonormal set.

Step by step solution

01

Definition of orthonormal sets

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

02

Check whether the set of vectors are orthogonal

Consider that, \({\bf{u}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt {18} }}}\\{\frac{4}{{\sqrt {18} }}}\\{\frac{1}{{\sqrt {18} }}}\end{array}} \right),{\rm{ }}{\bf{v}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt 2 }}}\\0\\{ - \frac{1}{{\sqrt 2 }}}\end{array}} \right),\) and \({\bf{w}} = \left( {\begin{array}{*{20}{c}}{ - \frac{2}{3}}\\{\frac{1}{3}}\\{ - \frac{2}{3}}\end{array}} \right)\).

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

\(\begin{array}{c}{\bf{u}} \cdot {\bf{v}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt {18} }}}\\{\frac{4}{{\sqrt {18} }}}\\{\frac{1}{{\sqrt {18} }}}\end{array}} \right) \cdot \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt 2 }}}\\0\\{ - \frac{1}{{\sqrt 2 }}}\end{array}} \right)\\ = \left( {\frac{1}{{\sqrt {18} }}} \right)\left( {\frac{1}{{\sqrt 2 }}} \right) + \frac{4}{{\sqrt {18} }}\left( 0 \right) + \frac{1}{{\sqrt {18} }}\left( { - \frac{1}{{\sqrt 2 }}} \right)\\ = \frac{1}{6} - 0 - \frac{1}{6}\\ = 0\end{array}\)

\(\begin{array}{c}{\bf{u}} \cdot {\bf{w}} = \left( {\begin{array}{*{20}{c}}{\frac{1}{{\sqrt {18} }}}\\{\frac{4}{{\sqrt {18} }}}\\{\frac{1}{{\sqrt {18} }}}\end{array}} \right) \cdot \left( {\begin{array}{*{20}{c}}{ - \frac{2}{3}}\\{\frac{1}{3}}\\{ - \frac{2}{3}}\end{array}} \right)\\ = \left( {\frac{1}{{\sqrt {18} }}} \right)\left( { - \frac{2}{3}} \right) + \frac{4}{{\sqrt {18} }}\left( {\frac{1}{3}} \right) + \frac{1}{{\sqrt {18} }}\left( { - \frac{2}{3}} \right)\\ = - \frac{{\sqrt 2 }}{9} + \frac{{2\sqrt 2 }}{9} - \frac{{\sqrt 2 }}{9}\\ = 0\end{array}\)

And,

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

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

03

Check whether the set of vectors is orthonormal

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

\(\begin{array}{c}{\left\| {\bf{u}} \right\|^2} = {\bf{u}} \cdot {\bf{u}}\\ = {\left( {\frac{1}{{\sqrt {18} }}} \right)^2} + {\left( {\frac{4}{{\sqrt {18} }}} \right)^2} + {\left( {\frac{1}{{\sqrt {18} }}} \right)^2}\\ = \left( {\frac{1}{{18}}} \right) + \left( {\frac{{16}}{{18}}} \right) + \left( {\frac{1}{{18}}} \right)\\ = 1\end{array}\)

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

And,

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

It is observed that \(\left\{ {{\bf{u}},{\bf{v}},{\bf{w}}} \right\}\) is an orthonormal set.

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Most popular questions from this chapter

Suppose radioactive substance A and B have decay constants of \(.02\) and \(.07\), respectively. If a mixture of these two substances at a time \(t = 0\) contains \({M_A}\) grams of \(A\) and \({M_B}\) grams of \(B\), then a model for the total amount of mixture present at time \(t\) is

\(y = {M_A}{e^{ - .02t}} + {M_B}{e^{ - .07t}}\) (6)

Suppose the initial amounts \({M_A}\) and are unknown, but a scientist is able to measure the total amounts present at several times and records the following points \(\left( {{t_i},{y_i}} \right):\left( {10,21.34} \right),\left( {11,20.68} \right),\left( {12,20.05} \right),\left( {14,18.87} \right)\) and \(\left( {15,18.30} \right)\).

a.Describe a linear model that can be used to estimate \({M_A}\) and \({M_B}\).

b. Find the least-squares curved based on (6).

Given \(A = QR\) as in Theorem 12, describe how to find an orthogonal\(m \times m\)(square) matrix \({Q_1}\) and an invertible \(n \times n\) upper triangular matrix \(R\) such that

\(A = {Q_1}\left[ {\begin{aligned}{{}{}}R\\0\end{aligned}} \right]\)

The MATLAB qr command supplies this “full” QR factorization

when rank \(A = n\).

In Exercises 1-4, find a least-sqaures solution of \(A{\bf{x}} = {\bf{b}}\) by (a) constructing a normal equations for \({\bf{\hat x}}\) and (b) solving for \({\bf{\hat x}}\).

3. \(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{ - {\bf{2}}}\\{ - {\bf{1}}}&{\bf{2}}\\{\bf{0}}&{\bf{3}}\\{\bf{2}}&{\bf{5}}\end{aligned}} \right)\), \({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{3}}\\{\bf{1}}\\{ - {\bf{4}}}\\{\bf{2}}\end{aligned}} \right)\)

Question: In Exercises 1 and 2, you may assume that\(\left\{ {{{\bf{u}}_{\bf{1}}},...,{{\bf{u}}_{\bf{4}}}} \right\}\)is an orthogonal basis for\({\mathbb{R}^{\bf{4}}}\).

2.\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{\bf{1}}\\{\bf{2}}\\{\bf{1}}\\{\bf{1}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{ - {\bf{2}}}\\{\bf{1}}\\{ - {\bf{1}}}\\{\bf{1}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{3}}} = \left[ {\begin{aligned}{\bf{1}}\\{\bf{1}}\\{ - {\bf{2}}}\\{ - {\bf{1}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{4}}} = \left[ {\begin{aligned}{ - {\bf{1}}}\\{\bf{1}}\\{\bf{1}}\\{ - {\bf{2}}}\end{aligned}} \right]\),\({\bf{x}} = \left[ {\begin{aligned}{\bf{4}}\\{\bf{5}}\\{ - {\bf{3}}}\\{\bf{3}}\end{aligned}} \right]\)

Write v as the sum of two vectors, one in\({\bf{Span}}\left\{ {{{\bf{u}}_1}} \right\}\)and the other in\({\bf{Span}}\left\{ {{{\bf{u}}_2},{{\bf{u}}_3},{{\bf{u}}_{\bf{4}}}} \right\}\).

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 .

(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 \(y\)-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 -values.

19. Justify the equation \(SS\left( T \right) = SS\left( R \right) + SS\left( E \right)\). (Hint: Use a theorem, and explain why the hypotheses of the theorem are satisfied.) This equation is extremely important in statistics, both in regression theory and in the analysis of variance.

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