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In Exercises 13 and 14, find the best approximation to\[{\bf{z}}\]by vectors of the form\[{c_1}{{\bf{v}}_1} + {c_2}{{\bf{v}}_2}\].

13.\[z = \left[ {\begin{aligned}3\\{ - 7}\\2\\3\end{aligned}} \right]\],\[{{\bf{v}}_1} = \left[ {\begin{aligned}2\\{ - 1}\\{ - 3}\\1\end{aligned}} \right]\],\[{{\bf{v}}_2} = \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\]

Short Answer

Expert verified

The best approximation to \[{\bf{z}}\] is \[\frac{2}{3}{{\bf{v}}_1} - \frac{7}{3}{{\bf{v}}_2} = \left[ {\begin{aligned}{ - 1}\\{ - 3}\\{ - 2}\\3\end{aligned}} \right]\].

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

Find\[{{\bf{v}}_1} \cdot {{\bf{v}}_2}\].

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

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

03

The Best Approximation Theorem

Here, \[{\bf{\hat y}}\] is said to be the closest point in \[w\] to \[{\bf{y}}\], if \[{\bf{\hat y}}\] be the orthogonal projection of \[{\bf{y}}\] onto \[w\], where \[w\] is a subspace of \[{\mathbb{R}^n}\], and \[{\bf{y}} \in w\]. \[{\bf{\hat y}}\] can be found as,

\[{\bf{\hat y}} = \frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \cdots + \frac{{{\bf{y}} \cdot {{\bf{u}}_p}}}{{{{\bf{u}}_p} \cdot {{\bf{u}}_p}}}{{\bf{u}}_p}\]

04

Find the best approximation

Find\[{\bf{z}} \cdot {{\bf{v}}_1}\],\[{\bf{z}} \cdot {{\bf{v}}_2}\],\[{{\bf{v}}_1} \cdot {{\bf{v}}_1}\], and\[{{\bf{v}}_2} \cdot {{\bf{v}}_2}\]first, where\[{\bf{z}} = \left[ {\begin{aligned}3\\{ - 7}\\2\\3\end{aligned}} \right]\].

\[\begin{aligned}{\bf{z}} \cdot {{\bf{v}}_1} &= \left[ {\begin{aligned}3\\{ - 7}\\2\\3\end{aligned}} \right] \cdot \left[ {\begin{aligned}2\\{ - 1}\\{ - 3}\\1\end{aligned}} \right]\\ &= 3\left( 2 \right) + \left( { - 7} \right)\left( { - 1} \right) + 2\left( { - 3} \right) + 3\left( 1 \right)\\ &= 10\end{aligned}\]

\[\begin{aligned}{\bf{z}} \cdot {{\bf{v}}_2} &= \left[ {\begin{aligned}3\\{ - 7}\\2\\3\end{aligned}} \right] \cdot \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\\ &= 3\left( 1 \right) + \left( { - 7} \right)\left( 1 \right) + 2\left( 0 \right) + 3\left( { - 1} \right)\\ &= - 7\end{aligned}\]

\[\begin{aligned}{{\bf{v}}_1} \cdot {{\bf{v}}_1} &= \left[ {\begin{aligned}2\\{ - 1}\\{ - 3}\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}2\\{ - 1}\\{ - 3}\\1\end{aligned}} \right]\\ &= 2\left( 2 \right) + \left( { - 1} \right)\left( { - 1} \right) + \left( { - 3} \right)\left( { - 3} \right) + \left( 1 \right)\left( 1 \right)\\ &= 15\end{aligned}\]

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

Now, find the best approximation to\[{\bf{z}}\], that is\[{\bf{\hat z}}\]by using the formula\[{\bf{\hat y}} = \frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \cdots + \frac{{{\bf{y}} \cdot {{\bf{u}}_p}}}{{{{\bf{u}}_p} \cdot {{\bf{u}}_p}}}{{\bf{u}}_p}\].

\[\begin{aligned}{\bf{\hat z}} &= \frac{{10}}{{15}}{{\bf{v}}_1} - \frac{7}{3}{{\bf{v}}_2}\\ &= \frac{2}{5}{{\bf{v}}_1} - \frac{7}{3}{{\bf{v}}_2}\\ &= \frac{2}{5}\left[ {\begin{aligned}1\\{ - 2}\\{ - 1}\\2\end{aligned}} \right] - \frac{7}{3}\left[ {\begin{aligned}{ - 4}\\1\\0\\3\end{aligned}} \right]\\ &= \left[ {\begin{aligned}{ - 1}\\{ - 3}\\{ - 2}\\3\end{aligned}} \right]\end{aligned}\]

Hence,thebest approximation to\[{\bf{z}}\]is\[\frac{2}{5}{{\bf{v}}_1} - \frac{7}{3}{{\bf{v}}_2} = \left[ {\begin{aligned}{ - 1}\\{ - 3}\\{ - 2}\\3\end{aligned}} \right]\].

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Most popular questions from this chapter

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

7.\[y = \left[ {\begin{aligned}1\\3\\5\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\3\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}5\\1\\4\end{aligned}} \right]\]

Find the distance between \({\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{10}\\{ - 3}\end{aligned}} \right)\) and \({\mathop{\rm y}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\{ - 5}\end{aligned}} \right)\).

Exercises 19 and 20 involve a design matrix \(X\) with two or more columns and a least-squares solution \(\hat \beta \) of \({\bf{y}} = X\beta \). Consider the following numbers.

(i) \({\left\| {X\hat \beta } \right\|^2}\)โ€”the sum of the squares of the โ€œregression term.โ€ Denote this number by \(SS\left( R \right)\).

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)โ€”the sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)โ€”the โ€œtotalโ€ sum of the squares of the -values. Denote this number by \(SS\left( T \right)\).

Every statistics text that discusses regression and the linear model \(y = X\beta + \in \) introduces these numbers, though terminology and notation vary somewhat. To simplify matters, assume that the mean of the -values is zero. In this case, \(SS\left( T \right)\) is proportional to what is called the variance of the set of \(y\)-values.

20. Show that \({\left\| {X\hat \beta } \right\|^2} = {\hat \beta ^T}{X^T}{\bf{y}}\). (Hint: Rewrite the left side and use the fact that \(\hat \beta \) satisfies the normal equations.) This formula for is used in statistics. From this and from Exercise 19, obtain the standard formula for \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

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.

5. \(\left( {\begin{aligned}{{}{}}1\\{ - 4}\\0\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{}}7\\{ - 7}\\{ - 4}\\1\end{aligned}} \right)\)

In Exercises 13 and 14, the columns of Q were obtained by applying the Gram-Schmidt process to the columns of A. Find an upper triangular matrix R such that \(A = QR\). Check your work.

13. \(A = \left( {\begin{aligned}{{}{}}5&9\\1&7\\{ - 3}&{ - 5}\\1&5\end{aligned}} \right),{\rm{ }}Q = \left( {\begin{aligned}{{}{}}{\frac{5}{6}}&{ - \frac{1}{6}}\\{\frac{1}{6}}&{\frac{5}{6}}\\{ - \frac{3}{6}}&{\frac{1}{6}}\\{\frac{1}{6}}&{\frac{3}{6}}\end{aligned}} \right)\)

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