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In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

9.\[y = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\]

Short Answer

Expert verified

The vector\[{\bf{y}}\]is the sum of the vectors in\[W\]as\[{\bf{\hat y}} + {\bf{z}} = \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right] + \left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\].

A vector orthogonal to \[W\] is \[\left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\].

Step by step solution

01

Write the definition

Orthogonal set: If\[{{\bf{u}}_i} \cdot {{\bf{u}}_j} = 0\]for\[i \ne j\], then the set of vectors\[\left\{ {{{\bf{u}}_1}, \ldots ,{{\bf{u}}_p}} \right\} \in {\mathbb{R}^n}\]is said to be orthogonal.

02

Check the set of given vectors is orthogonal or not

Given vectors are,\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\]and\[{{\bf{u}}_3} = \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\].

Find\[{{\bf{u}}_1} \cdot {{\bf{u}}_2}\],\[{{\bf{u}}_2} \cdot {{\bf{u}}_3}\]and\[{{\bf{u}}_1} \cdot {{\bf{u}}_3}\].

\[\begin{aligned}{{\bf{u}}_1} \cdot {{\bf{u}}_2} &= \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\\ &= 1\left( 1 \right) + 1\left( 3 \right) + 0\left( 1 \right) + 1\left( { - 2} \right)\\ &= 0\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_2} \cdot {{\bf{u}}_3} &= \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\\ &= - 1\left( { - 1} \right) + 3\left( 0 \right) + 1\left( 1 \right) + \left( { - 2} \right)1\\ &= 0\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_1} \cdot {{\bf{u}}_3} &= \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\\ &= 1\left( { - 1} \right) + 1\left( 0 \right) + 0\left( 1 \right) + 1\left( 1 \right)\\ &= 0\end{aligned}\]

As \[{{\bf{u}}_1} \cdot {{\bf{u}}_2} = 0\], \[{{\bf{u}}_2} \cdot {{\bf{u}}_3} = 0\] and \[{{\bf{u}}_1} \cdot {{\bf{u}}_3} = 0\], so the set of vectors \[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2},{{\bf{u}}_3}} \right\}\] is orthogonal.

03

The Orthogonal Decomposition Theorem

If \[\left\{ {{{\bf{u}}_1}, \ldots ,{{\bf{u}}_p}} \right\}\] is any orthogonal basis of \[w\], then \[{\bf{\hat y}} = \frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \cdots + \frac{{{\bf{y}} \cdot {{\bf{u}}_p}}}{{{{\bf{u}}_p} \cdot {{\bf{u}}_p}}}{{\bf{u}}_p}\], where \[w\] is a subspace of \[{\mathbb{R}^n}\] and \[{\bf{\hat y}} \in w\], for which every \[{\bf{y}}\] can be written as \[{\bf{y}} = {\bf{\hat y}} + {\bf{z}}\] when \[{\bf{z}} \in {w^ \bot }\].

04

Find the Orthogonal projection of \[{\bf{y}}\] onto Span \[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2},{{\bf{u}}_3}} \right\}\]

Find\[{\bf{y}} \cdot {{\bf{u}}_1}\],\[{\bf{y}} \cdot {{\bf{u}}_2}\],\[{\bf{y}} \cdot {{\bf{u}}_3}\],\[{{\bf{u}}_1} \cdot {{\bf{u}}_1}\],\[{{\bf{u}}_2} \cdot {{\bf{u}}_2}\], and\[{{\bf{u}}_3} \cdot {{\bf{u}}_3}\]first, where\[{\bf{y}} = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right]\].

\[\begin{aligned}{c}{\bf{y}} \cdot {{\bf{u}}_1} = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right] \cdot \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right]\\ = 4\left( 1 \right) + 3\left( 1 \right) + 3\left( 0 \right) + \left( { - 1} \right)1\\ = 6\end{aligned}\]

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_2} = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\\ = 4\left( { - 1} \right) + 3\left( 3 \right) + 3\left( 1 \right) + \left( { - 1} \right)\left( { - 2} \right)\\ = 10\end{aligned}\]

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_3} = \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\\ = 4\left( { - 1} \right) + 3\left( 0 \right) + 3\left( 1 \right) + \left( { - 1} \right)1\\ = - 2\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_1} \cdot {{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right]\\ = 1\left( 1 \right) + \left( 1 \right)\left( 1 \right) + 0\left( 0 \right) + \left( 1 \right)\left( 1 \right)\\ = 3\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_2} \cdot {{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right]\\ = - 1\left( { - 1} \right) + \left( 3 \right)\left( 3 \right) + 1\left( 1 \right) + \left( { - 2} \right)\left( { - 2} \right)\\ = 15\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_3} \cdot {{\bf{u}}_3} = \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\\ = - 1\left( { - 1} \right) + \left( 0 \right)\left( 0 \right) + 1\left( 1 \right) + \left( 1 \right)\left( 1 \right)\\ = 3\end{aligned}\]

Now, find the projection of\[{\bf{y}}\]onto Span\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2},{{\bf{u}}_3}} \right\}\]by substituting the obtained values into\[{\bf{\hat y}} = \frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \cdots + \frac{{{\bf{y}} \cdot {{\bf{u}}_p}}}{{{{\bf{u}}_p} \cdot {{\bf{u}}_p}}}{{\bf{u}}_p}\].

\[\begin{aligned}{\bf{\hat y}} = \frac{6}{3}{{\bf{u}}_1} + \frac{{10}}{{15}}{{\bf{u}}_2} - \frac{2}{3}{{\bf{u}}_3}\\ = 2\left[ {\begin{aligned}1\\1\\0\\1\end{aligned}} \right] + \frac{2}{3}\left[ {\begin{aligned}{ - 1}\\3\\1\\{ - 2}\end{aligned}} \right] - \left[ {\begin{aligned}{ - 1}\\0\\1\\1\end{aligned}} \right]\\ = \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right]\end{aligned}\]

Hence, \[{\bf{\hat y}} = \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right]\].

05

Find a vector orthogonal to \[W\]

Find\[{\bf{z}}\], which is orthogonal to\[W\]by using\[{\bf{z}} = {\bf{y}} - {\bf{\hat y}}\].

\[\begin{aligned}{\bf{z}} &= {\bf{y}} - {\bf{\hat y}}\\ &= \left[ {\begin{aligned}4\\3\\3\\{ - 1}\end{aligned}} \right] - \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right]\\ &= \left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\end{aligned}\]

So,\[{\bf{z}} = \left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\].

06

Write \[y\] as a sum of a vector in \[W\]

Write\[{\bf{y}} = {\bf{\hat y}} + {\bf{z}}\], where\[{\bf{\hat y}} \in w\]and\[{\bf{z}} \in {w^ \bot }\].

\[\begin{aligned}{\bf{y}} &= {\bf{\hat y}} + {\bf{z}}\\ &= \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right] + \left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\end{aligned}\]

Hence,\[{\bf{y}}\]as the sum of a vector in\[W\]is\[{\bf{\hat y}} + {\bf{z}} = \left[ {\begin{aligned}2\\4\\0\\0\end{aligned}} \right] + \left[ {\begin{aligned}2\\{ - 1}\\3\\{ - 1}\end{aligned}} \right]\].

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Most popular questions from this chapter

In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

8.\[y = \left[ {\begin{aligned}{ - 1}\\4\\3\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\{\bf{1}}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\{ - 2}\end{aligned}} \right]\]

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.

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.

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

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

12. \[y = \left[ {\begin{aligned}3\\{ - 1}\\1\\{13}\end{aligned}} \right]\], \[{{\bf{v}}_1} = \left[ {\begin{aligned}1\\{ - 2}\\{ - 1}\\2\end{aligned}} \right]\], \[{{\bf{v}}_2} = \left[ {\begin{aligned}{ - 4}\\1\\0\\3\end{aligned}} \right]\]

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