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Exercise 3-8 refer to \({{\bf{P}}_{\bf{2}}}\) with the inner product given by evaluation at \( - {\bf{1}}\), 0, and 1. (See Example 2).

4. Compute \(\left\langle {p,q} \right\rangle \), where \(p\left( t \right) = {\bf{3}}t - {t^{\bf{2}}}\), \(q\left( t \right) = {\bf{3}} + {\bf{2}}{t^{\bf{2}}}\).

Short Answer

Expert verified

The inner product is \( - 10\).

Step by step solution

01

Find the values of polynomials

The values of polynomial \(p\left( t \right) = 3t - {t^2}\) are:

\(\begin{aligned}p\left( { - 1} \right) &= 3\left( { - 1} \right) - {\left( { - 1} \right)^2}\\ &= - 3 - 1\\ &= - 4\end{aligned}\)

\(\begin{aligned}p\left( 0 \right) &= 3\left( 0 \right) - {\left( 0 \right)^2}\\ &= 0\end{aligned}\)

\(\begin{aligned}p\left( 1 \right) &= 3\left( 1 \right) - {\left( 1 \right)^2}\\ &= 3 - 1\\ &= 2\end{aligned}\)

The values of polynomial \(q\left( t \right) = 3 + 2{t^2}\) are:

\(\begin{aligned}q\left( { - 1} \right) &= 3 + 2{\left( { - 1} \right)^2}\\ &= 5\end{aligned}\)

\(\begin{aligned}q\left( 0 \right) &= 3 + 2{\left( 0 \right)^2}\\ &= 3\end{aligned}\)

\(\begin{aligned}q\left( 1 \right) &= 3 + 2{\left( 1 \right)^2}\\ &= 5\end{aligned}\)

02

Find the value of inner product

The inner product for \(\left\langle {p,q} \right\rangle \) is defined as:

\(\left\langle {p,q} \right\rangle = p\left( {{t_0}} \right)q\left( {{t_0}} \right) + p\left( {{t_1}} \right)q\left( {{t_1}} \right) + p\left( {{t_2}} \right)q\left( {{t_2}} \right)\)

Substitute \({t_0} = - 1\), \({t_1} = 0\) and \({t_2} = 1\).

\(\begin{aligned}\left\langle {p,q} \right\rangle &= p\left( { - 1} \right)q\left( { - 1} \right) + p\left( 0 \right)q\left( 0 \right) + p\left( 1 \right)q\left( 1 \right)\\ &= \left( { - 4} \right)\left( 5 \right) + \left( 0 \right)\left( 3 \right) + \left( 2 \right)\left( 5 \right)\\ &= - 20 + 0 + 10\\ &= - 10\end{aligned}\)

Thus, the inner product is \( - 10\).

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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 9-12, find a unit vector in the direction of the given vector.

11. \(\left( {\begin{aligned}{*{20}{c}}{\frac{7}{4}}\\{\frac{1}{2}}\\1\end{aligned}} \right)\)

a. Rewrite the data in Example 1 with new \(x\)-coordinates in mean deviation form. Let \(X\) be the associated design matrix. Why are the columns of \(X\) orthogonal?

b. Write the normal equations for the data in part (a), and solve them to find the least-squares line, \(y = {\beta _0} + {\beta _1}x*\), where \(x* = x - 5.5\).

Let \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) be an orthonormal set in \({\mathbb{R}^n}\). Verify the following inequality, called Bessel’s inequality, which is true for each x in \({\mathbb{R}^n}\):

\({\left\| {\bf{x}} \right\|^2} \ge {\left| {{\bf{x}} \cdot {{\bf{v}}_1}} \right|^2} + {\left| {{\bf{x}} \cdot {{\bf{v}}_2}} \right|^2} + \ldots + {\left| {{\bf{x}} \cdot {{\bf{v}}_p}} \right|^2}\)

A certain experiment produce the data \(\left( {1,7.9} \right),\left( {2,5.4} \right)\) and \(\left( {3, - .9} \right)\). Describe the model that produces a least-squares fit of these points by a function of the form

\(y = A\cos x + B\sin x\)

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