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Question: Let\[y = \left[ {\begin{aligned}5\\{ - 9}\\5\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right]\]. Find the distance from\[y\]to plane in\[{\mathbb{R}^3}\]spanned by\[{{\bf{u}}_1}\], and\[{{\bf{u}}_2}\].

Short Answer

Expert verified

The distance is \[2\sqrt {10} \].

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

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

\[\begin{aligned}{{\bf{u}}_1} \cdot {{\bf{u}}_2} &= \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right]\\ &= - 3\left( { - 3} \right) + \left( { - 5} \right)\left( 2 \right) + \left( 1 \right)\left( 1 \right)\\ &= 0\end{aligned}\]

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

03

The Best Approximation Theorem

Here, \[{\bf{\hat y}}\] is said to be the closest point in \[w\] to \[{\bf{y}}\] for which \[\left\| {{\bf{y}} - {\bf{\hat y}}} \right\| < \left\| {{\bf{y}} - {\bf{v}}} \right\|\] for all \[{\bf{v}}\] in \[w\] distinct from \[{\bf{\hat y}}\], 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 distance

The distance from\[{\bf{y}}\]to the closest point is also the distance between the point\[{\bf{y}}\]in\[{\mathbb{R}^3}\]to subspace\[w\], so the distance can be found by\[\left\| {{\bf{y}} - {\bf{\hat y}}} \right\|\].

Find\[{\bf{y}} \cdot {{\bf{u}}_1}\],\[{\bf{y}} \cdot {{\bf{u}}_2}\],\[{{\bf{u}}_1} \cdot {{\bf{u}}_1}\], and\[{{\bf{u}}_2} \cdot {{\bf{u}}_2}\]first, where\[{\bf{y}} = \left[ {\begin{aligned}5\\{ - 9}\\5\end{aligned}} \right]\].

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_1} &= \left[ {\begin{aligned}5\\{ - 9}\\5\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right]\\ &= 5\left( { - 3} \right) + \left( { - 9} \right)\left( { - 5} \right) + 5\left( 1 \right)\\ &= 35\end{aligned}\]

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_2} &= \left[ {\begin{aligned}5\\{ - 9}\\5\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right]\\ &= 5\left( { - 3} \right) + \left( { - 9} \right)\left( 2 \right) + 5\left( 1 \right)\\ &= - 28\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_1} \cdot {{\bf{u}}_1} &= \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right] \cdot \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right]\\ &= - 3\left( { - 3} \right) + \left( { - 5} \right)\left( { - 5} \right) + \left( 1 \right)\left( 1 \right)\\ &= 35\end{aligned}\]

\[\begin{aligned}{{\bf{u}}_2} \cdot {{\bf{u}}_2} &= \left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right] \cdot {{\bf{u}}_2}\left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right]\\ &= - 3\left( { - 3} \right) + \left( 2 \right)\left( 2 \right) + 1\left( 1 \right)\\ &= 14\end{aligned}\]

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

\[\begin{aligned}{c}{\bf{\hat y}} = \frac{{35}}{{35}}{{\bf{u}}_1} - \frac{{28}}{{14}}{{\bf{u}}_2}\\ = {{\bf{u}}_1} - 2{{\bf{u}}_2}\\ = \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right] - 2\left[ {\begin{aligned}{ - 3}\\2\\1\end{aligned}} \right]\\ = \left[ {\begin{aligned}{ - 3}\\{ - 5}\\1\end{aligned}} \right] - \left[ {\begin{aligned}{ - 6}\\4\\2\end{aligned}} \right]\\ = \left[ {\begin{aligned}3\\{ - 9}\\{ - 1}\end{aligned}} \right]\end{aligned}\]

Hence,theclosest point is\[\left[ {\begin{aligned}3\\{ - 9}\\{ - 1}\end{aligned}} \right]\].

Now, find the distance by using\[\left\| {{\bf{y}} - {\bf{\hat y}}} \right\|\].

\[\begin{aligned}\left\| {{\bf{y}} - {\bf{\hat y}}} \right\| &= \left\| {\left[ {\begin{aligned}5\\{ - 9}\\5\end{aligned}} \right] - \left[ {\begin{aligned}3\\{ - 9}\\{ - 1}\end{aligned}} \right]} \right\|\\ &= \left\| {\left[ {\begin{aligned}2\\0\\6\end{aligned}} \right]} \right\|\\ &= \sqrt {{2^2} + 0 + {6^2}} \\ &= \sqrt {40} \\ &= 2\sqrt {10} \end{aligned}\]

So, the distance is\[2\sqrt {10} \].

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Most popular questions from this chapter

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鈥揝chmidt 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 \(R = {Q^T}A\).

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) \({\left\| {X\hat \beta } \right\|^2}\)鈥攖he sum of the squares of the 鈥渞egression term.鈥 Denote this number by \(SS\left( R \right)\).

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

(iii) \({\left\| {\bf{y}} \right\|^2}\)鈥攖he 鈥渢otal鈥 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 \(SS\left( E \right)\):

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

Question: In exercises 1-6, determine which sets of vectors are orthogonal.

\(\left[ {\begin{align}1\\{ - 2}\\1\end{align}} \right]\), \(\left[ {\begin{align}0\\1\\2\end{align}} \right]\), \(\left[ {\begin{align}{ - 5}\\{ - 2}\\1\end{align}} \right]\)

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

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

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

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