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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\[W\].

10.\[y = \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}1\\0\\1\\1\end{aligned}} \right]\],\[{{\bf{u}}_3} = \left[ {\begin{aligned}0\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

Short Answer

Expert verified

The vector\[{\bf{y}}\]is the sum of the vectors in\[W\]as\[{\bf{\hat y}} + {\bf{z}} = \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right] + \left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\].

A vector orthogonal to \[W\] is \[\left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\].

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

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

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

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

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

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\[{\bf{y}} = \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right]\].

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_1} = \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right] \cdot \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\\ = 3\left( 1 \right) + 4\left( 1 \right) + 5\left( 0 \right) + 6\left( { - 1} \right)\\ = 1\end{aligned}\]

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_2} &= \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right] \cdot \left[ {\begin{aligned}1\\0\\1\\1\end{aligned}} \right]\\ = 3\left( 1 \right) + 4\left( 0 \right) + 5\left( 1 \right) + 6\left( 1 \right)\\ &= 14\end{aligned}\]

\[\begin{aligned}{\bf{y}} \cdot {{\bf{u}}_3} &= \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right] \cdot \left[ {\begin{aligned}0\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\\ &= 3\left( 0 \right) + 4\left( { - 1} \right) + 5\left( 1 \right) + 6\left( { - 1} \right)\\ &= - 5\end{aligned}\]

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

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

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

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

\[\begin{aligned}{\bf{\hat y}} = \frac{1}{3}{{\bf{u}}_1} + \frac{{14}}{3}{{\bf{u}}_2} - \frac{5}{3}{{\bf{u}}_3}\\ &= \frac{1}{3}\left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right] + \frac{{14}}{3}\left[ {\begin{aligned}1\\{ - 1}\\1\\{ - 1}\end{aligned}} \right] - \frac{5}{3}\left[ {\begin{aligned}0\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\\ &= \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right]\end{aligned}\]

Hence, \[{\bf{\hat y}} = \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right]\].

05

Find a vector orthogonal to \[W\]

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

\[\begin{aligned}{\bf{z}} &= {\bf{y}} - {\bf{\hat y}}\\ &= \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right] - \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right]\\ &= \left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\end{aligned}\]

So, \[{\bf{z}} = \left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\].

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\[{\bf{z}} \in {w^ \bot }\].

\[\begin{aligned}{c}{\bf{y}} = {\bf{\hat y}} + {\bf{z}}\\ = \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right] + \left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\end{aligned}\]

Hence, \[{\bf{y}}\] as the sum of a vector in \[W\] is \[{\bf{\hat y}} + {\bf{z}} = \left[ {\begin{aligned}5\\2\\3\\6\end{aligned}} \right] + \left[ {\begin{aligned}{ - 2}\\2\\2\\0\end{aligned}} \right]\].

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Most popular questions from this chapter

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

\(\left[ {\begin{align} 2\\{-5}\\{-3}\end{align}} \right]\), \(\left[ {\begin{align}0\\0\\0\end{align}} \right]\), \(\left[ {\begin{align} 4\\{ - 2}\\6\end{align}} \right]\)

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

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

Let \(\overline x = \frac{1}{n}\left( {{x_1} + \cdots + {x_n}} \right)\), and \(\overline y = \frac{1}{n}\left( {{y_1} + \cdots + {y_n}} \right)\). Show that the least-squares line for the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) must pass through \(\left( {\overline x ,\overline y } \right)\). That is, show that \(\overline x \) and \(\overline y \) satisfies the linear equation \(\overline y = {\hat \beta _0} + {\hat \beta _1}\overline x \). (Hint: Derive this equation from the vector equation \({\bf{y}} = X{\bf{\hat \beta }} + \in \). Denote the first column of \(X\) by 1. Use the fact that the residual vector \( \in \) is orthogonal to the column space of \(X\) and hence is orthogonal to 1.)

Let \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) be an orthonormal set. Verify the following equality by induction, beginning with \(p = 2\). If \({\bf{x}} = {c_1}{{\bf{v}}_1} + \ldots + {c_p}{{\bf{v}}_p}\), then

\({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + {\left| {{c_2}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\)

In Exercises 1-4, find a least-sqaures solution of \(A{\bf{x}} = {\bf{b}}\) by (a) constructing a normal equations for \({\bf{\hat x}}\) and (b) solving for \({\bf{\hat x}}\).

2. \(A = \left( {\begin{aligned}{{}{}}{\bf{2}}&{\bf{1}}\\{ - {\bf{2}}}&{\bf{0}}\\{\bf{2}} {\bf{3}}\end{aligned}} \right)\), \(b = \left( {\begin{aligned}{{}{}}{ - {\bf{5}}}\\{\bf{8}}\\{\bf{1}}\end{aligned}} \right)\)

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