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Exercises 21–24 refer to \(V = C\left( {0,1} \right)\) with the inner product given by an integral, as in Example 7.

24. Compute \(\left\| g \right\|\) for \(g\) in Exercise 22.

Short Answer

Expert verified

The required value is \(\left\| g \right\| = \frac{1}{{\sqrt {105} }}\).

Step by step solution

01

Use the given information

For the pair of vectors\(\left\langle {f,g} \right\rangle \), the inner product is given by \(\left\langle {f,g} \right\rangle = \int_0^1 {f\left( t \right)g\left( t \right)dt} \).

It is given that \(g\left( t \right) = {t^3} - {t^2}\), then \(\left\langle {g,g} \right\rangle = \int_0^1 {g\left( t \right)g\left( t \right)dt} \)and\(\left\| g \right\| = \sqrt {\left\langle {g,g} \right\rangle } \).

02

Find the inner product

Plug the expression for\(g\left( t \right)\)into inner product\(\left\langle {g,g} \right\rangle = \int_0^1 {g\left( t \right)g\left( t \right)dt} \), as follows:

\(\begin{aligned}{}\left\langle {g,g} \right\rangle &= \int_0^1 {g\left( t \right)g\left( t \right)dt} \\ &= \int_0^1 {\left( {{t^3} - {t^2}} \right)\left( {{t^3} - {t^2}} \right)dt} \\ &= \int_0^1 {\left( {{t^6} - 2{t^5} + {t^4}} \right)\,dt} \\ &= \left( {\frac{{{t^7}}}{7} - \frac{{2{t^6}}}{6} + \frac{{{t^5}}}{5}} \right)_0^1\\ &= \left( {\frac{1}{7} - \frac{1}{3} + \frac{1}{5} - 0} \right)\\ &= \frac{1}{{105}}\end{aligned}\)

03

Compute \(\left\| g \right\|\) 

Plugthe expression for\(\left\langle {g,g} \right\rangle \)into\(\left\| g \right\| = \sqrt {\left\langle {g,g} \right\rangle } \)and simplify as follows:

\(\begin{aligned}{}\left\| g \right\| &= \sqrt {\left\langle {g,g} \right\rangle } \\ &= \sqrt {\frac{1}{{105}}} \\ &= \frac{1}{{\sqrt {105} }}\end{aligned}\)

Hence, the required value is, \(\left\| g \right\| = \frac{1}{{\sqrt {105} }}\).

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Most popular questions from this chapter

In Exercises 5 and 6, describe all least squares solutions of the equation \(A{\bf{x}} = {\bf{b}}\).

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

Given data for a least-squares problem, \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\), the following abbreviations are helpful:

\(\begin{aligned}{l}\sum x = \sum\nolimits_{i = 1}^n {{x_i}} ,{\rm{ }}\sum {{x^2}} = \sum\nolimits_{i = 1}^n {x_i^2} ,\\\sum y = \sum\nolimits_{i = 1}^n {{y_i}} ,{\rm{ }}\sum {xy} = \sum\nolimits_{i = 1}^n {{x_i}{y_i}} \end{aligned}\)

The normal equations for a least-squares line \(y = {\hat \beta _0} + {\hat \beta _1}x\)may be written in the form

\(\begin{aligned}{{\hat \beta }_0} + {{\hat \beta }_1}\sum x = \sum y \\{{\hat \beta }_0}\sum x + {{\hat \beta }_1}\sum {{x^2}} = \sum {xy} {\rm{ (7)}}\end{aligned}\)

16. Use a matrix inverse to solve the system of equations in (7) and thereby obtain formulas for \({\hat \beta _0}\) , and that appear in many statistics texts.

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 3-6, verify that\(\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\)is an orthogonal set, and then find the orthogonal projection of y onto\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\).

3.\[y = \left[ {\begin{aligned}{ - {\bf{1}}}\\{\bf{4}}\\{\bf{3}}\end{aligned}} \right]\],\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{\bf{1}}\\{\bf{1}}\\{\bf{0}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{ - {\bf{1}}}\\{\bf{1}}\\{\bf{0}}\end{aligned}} \right]\)

In Exercises 3–6, verify that\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\]is an orthogonal set, and then find the orthogonal projection of\[y\]onto Span\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\].

6.\[{\rm{y}} = \left[ {\begin{aligned}6\\4\\1\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}{ - 4}\\{ - 1}\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}0\\1\\1\end{aligned}} \right]\]

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