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

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

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

Derive the normal equations (7) from the matrix form given in this section.

In Exercises 17 and 18, all vectors and subspaces are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

17. a.If \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},{{\bf{v}}_3}} \right\}\) is an orthogonal basis for\(W\), then multiplying

\({v_3}\)by a scalar \(c\) gives a new orthogonal basis \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2},c{{\bf{v}}_3}} \right\}\).

b. The Gram–Schmidt process produces from a linearly independent

set \(\left\{ {{{\bf{x}}_1}, \ldots ,{{\bf{x}}_p}} \right\}\)an orthogonal set \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) with the property that for each \(k\), the vectors \({{\bf{v}}_1}, \ldots ,{{\bf{v}}_k}\) span the same subspace as that spanned by \({{\bf{x}}_1}, \ldots ,{{\bf{x}}_k}\).

c. If \(A = QR\), where \(Q\) has orthonormal columns, then \(R = {Q^T}A\).

A certain experiment produces the data \(\left( {1,1.8} \right),\left( {2,2.7} \right),\left( {3,3.4} \right),\left( {4,3.8} \right),\left( {5,3.9} \right)\). Describe the model that produces a least-squares fit of these points by a function of the form

\(y = {\beta _1}x + {\beta _2}{x^2}\)

Such a function might arise, for example, as the revenue from the sale of \(x\) units of a product, when the amount offered for sale affects the price to be set for the product.

a. Give the design matrix, the observation vector, and the unknown parameter vector.

b. Find the associated least-squares curve for the data.

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. \(\left( { - 1,0} \right),\left( {0,1} \right),\left( {1,2} \right),\left( {2,4} \right)\)

Suppose the x-coordinates of the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) are in mean deviation form, so that \(\sum {{x_i}} = 0\). Show that if \(X\) is the design matrix for the least-squares line in this case, then \({X^T}X\) is a diagonal matrix.

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