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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.

Short Answer

Expert verified

(a) \(\left\| {\bf{x}} \right\| = 3\),\(\left\| {\bf{y}} \right\| = \sqrt {105} \)and \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2} = 225\)

(b) The set of vectors \(\left( {{z_1},{z_2}} \right)\) are in the form of \(\frac{{{z_1}}}{1} = \frac{{{z_2}}}{4}\).

Step by step solution

01

Find answer for part (a)

Find the value of \(\left\| {\bf{x}} \right\|\).

\(\begin{aligned}\left\| {\bf{x}} \right\| &= \sqrt {\left\langle {{\bf{x}},{\bf{x}}} \right\rangle } \\ &= \sqrt {\left\langle {\left( {1,1} \right),\left( {1,1} \right)} \right\rangle } \\ &= \sqrt {4\left( 1 \right)\left( 1 \right) + 5\left( 1 \right)\left( 1 \right)} \\ &= \sqrt 9 \\ &= 3\end{aligned}\)

Thus, the value of \(\left\| {\bf{x}} \right\|\) is 3.

Find the value of \(\left\| {\bf{y}} \right\|\).

\(\begin{aligned}\left\| {\bf{y}} \right\| &= \sqrt {\left\langle {{\bf{y}},{\bf{y}}} \right\rangle } \\ &= \sqrt {\left\langle {\left( {5, - 1} \right),\left( {5, - 1} \right)} \right\rangle } \\ &= \sqrt {4\left( 5 \right)\left( 5 \right) + 5\left( { - 1} \right)\left( { - 1} \right)} \\ &= \sqrt {105} \end{aligned}\)

Thus, the value of \(\left\| {\bf{y}} \right\|\) is \(\sqrt {105} \).

Find the value of \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2}\).

\(\begin{aligned}{\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2} &= {\left| {\left\langle {\left( {1,1} \right),\left( {5, - 1} \right)} \right\rangle } \right|^2}\\ &= {\left| {4\left( 1 \right)\left( 5 \right) + 5\left( 1 \right)\left( { - 1} \right)} \right|^2}\\ &= {\left| {15} \right|^2}\\ &= 225\end{aligned}\)

Thus, the value of \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^2}\) is 225.

02

Find answer for part (b)

The vectors \(\left( {{z_1},{z_2}} \right)\) are orthogonal to \(y\), if and only if \(\left\langle {z,{\bf{y}}} \right\rangle = 0\). Therefore,

\(\begin{aligned}\left\langle {z,{\bf{y}}} \right\rangle &= 0\\\left\langle {\left( {{z_1},{z_2}} \right),\left( {5, - 1} \right)} \right\rangle &= 0\\4\left( {{z_1}} \right)\left( 5 \right) + 5\left( { - 1} \right)\left( {{z_2}} \right) &= 0\\20{z_1} &= 5{z_2}\\\frac{{{z_1}}}{1} &= \frac{{{z_2}}}{4}\end{aligned}\)

Thus, the set of vectors \(\left( {{z_1},{z_2}} \right)\) are in the form of \(\frac{{{z_1}}}{1} = \frac{{{z_2}}}{4}\).

So, all the multiples of the vector \(\left( {\begin{aligned}1\\4\end{aligned}} \right)\) are orthogonal to the vector y.

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Most popular questions from this chapter

A simple curve that often makes a good model for the variable costs of a company, a function of the sales level \(x\), has the form \(y = {\beta _1}x + {\beta _2}{x^2} + {\beta _3}{x^3}\). There is no constant term because fixed costs are not included.

a. Give the design matrix and the parameter vector for the linear model that leads to a least-squares fit of the equation above, with data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\).

b. Find the least-squares curve of the form above to fit the data \(\left( {4,1.58} \right),\left( {6,2.08} \right),\left( {8,2.5} \right),\left( {10,2.8} \right),\left( {12,3.1} \right),\left( {14,3.4} \right),\left( {16,3.8} \right)\) and \(\left( {18,4.32} \right)\), with values in thousands. If possible, produce a graph that shows the data points and the graph of the cubic approximation.

Show that if \(U\) is an orthogonal matrix, then any real eigenvalue of \(U\) must be \( \pm 1\).

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 \({\bf{\hat x}}\).

2. \(A = \left( {\begin{aligned}{{}{}}{\bf{2}}&{\bf{1}}\\{ - {\bf{2}}}&{\bf{0}}\\{\bf{2}} {\bf{3}}\end{aligned}} \right)\), \(b = \left( {\begin{aligned}{{}{}}{ - {\bf{5}}}\\{\bf{8}}\\{\bf{1}}\end{aligned}} \right)\)

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

2. \(\left( {\begin{aligned}{{}{}}0\\4\\2\end{aligned}} \right),\left( {\begin{aligned}{{}{}}5\\6\\{ - 7}\end{aligned}} \right)\)

[M] Let \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) be the fourth-order and fifth order Fourier approximations in \(C\left[ {{\bf{0}},{\bf{2}}\pi } \right]\) to the square wave function in Exercise 10. Produce separate graphs of \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) on the interval \(\left[ {{\bf{0}},{\bf{2}}\pi } \right]\), and produce graph of \({f_{\bf{5}}}\) on \(\left[ { - {\bf{2}}\pi ,{\bf{2}}\pi } \right]\).

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