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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).

5. Compute \(\left\| p \right\|\] and \[\left\| q \right\|\), for p and q in Exercise 3.

Short Answer

Expert verified

The values are \(\left\| p \right\| = \sqrt {50} \), and \(\left\| q \right\| = \sqrt {27} \).

Step by step solution

01

Write the results from Exercise 3

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

\(\begin{array}p\left( 0 \right) &= 4 + 0\\ &= 4\end{array}\)

\(\begin{array}p\left( 1 \right) &= 4 + 1\\ &= 5\end{array}\)

And,

\(\begin{array}q\left( { - 1} \right) &= 5 - 4{\left( { - 1} \right)^2}\\ &= 5 - 4\\ &= 1\end{array}\)

\(\begin{array}q\left( 0 \right) &= 5 - 4{\left( 0 \right)^2}\\ &= 5\end{array}\)

\(\begin{array}q\left( 1 \right) &= 5 - 4{\left( 1 \right)^2}\\ &= 5 - 4\\ &= 1\end{array}\)

02

Find the value of \(\left\| p \right\|\)

The value of \[\left\| p \right\|\] can be calculated as follows:

\(\begin{array}\left\| p \right\| &= \sqrt {\left\langle {p,p} \right\rangle } \\ &= \sqrt {p\left( { - 1} \right)p\left( { - 1} \right) + p\left( 0 \right)p\left( 0 \right) + p\left( 1 \right)p\left( 1 \right)} \\ &= \sqrt {\left( 3 \right)\left( 3 \right) + \left( 4 \right)\left( 4 \right) + \left( 5 \right)\left( 5 \right)} \\ &= \sqrt {50} \end{array}\)

Thus, the value of \(\left\| p \right\|\) is \(\sqrt {50} \).

03

Find the value of \(\left\| q \right\|\)

The value of \[\left\| q \right\|\] can be calculated as follows:

\(\begin{array}\left\| q \right\| &= \sqrt {\left\langle {q,q} \right\rangle } \\ &= \sqrt {q\left( { - 1} \right)q\left( { - 1} \right) + q\left( 0 \right)q\left( 0 \right) + q\left( 1 \right)q\left( 1 \right)} \\ &= \sqrt {\left( 1 \right)\left( 1 \right) + \left( 5 \right)\left( 5 \right) + \left( 1 \right)\left( 1 \right)} \\ &= \sqrt {27} \end{array}\)

Thus, the value of \(\left\| q \right\|\) is \(\sqrt {27} \).

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Most popular questions from this chapter

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\[{\bf{y}}\]onto Span\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\].

5.\[y = \left[ {\begin{aligned}{ - 1}\\2\\6\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}3\\{ - 1}\\2\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}1\\{ - 1}\\{ - 2}\end{aligned}} \right]\]

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. \(\left( {0,1} \right),\left( {1,1} \right),\left( {2,2} \right),\left( {3,2} \right)\)

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.

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

12. \(\left( {\begin{aligned}{{}{}}1&3&5\\{ - 1}&{ - 3}&1\\0&2&3\\1&5&2\\1&5&8\end{aligned}} \right)\)

In Exercises 11 and 12, find the closest point to\[{\bf{y}}\]in the subspace\[W\]spanned by\[{{\bf{v}}_1}\], and\[{{\bf{v}}_2}\].

11.\[y = \left[ {\begin{aligned}3\\1\\5\\1\end{aligned}} \right]\],\[{{\bf{v}}_1} = \left[ {\begin{aligned}3\\1\\{ - 1}\\1\end{aligned}} \right]\],\[{{\bf{v}}_2} = \left[ {\begin{aligned}1\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

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