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

\(\left[ {\begin{array}{*{20}{c}}3\\{-2}\\1\\3\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}{-1}\\3\\{-3}\\4\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\8\\7\\0\end{array}} \right]\)

Short Answer

Expert verified

The given set is orthogonal.

Step by step solution

01

Definition of an orthogonal set

If \({u_i} \cdot {u_j}\) 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 for orthogonality of vectors

Let the given vectors be, \({u_1} = \left[ {\begin{align}3\\{ - 2}\\1\\3\end{align}} \right]\), \({u_2} = \left[ {\begin{align}{-1}\\{3}\\{-3}\\4\end{align}} \right]\) and \({u_3} = \left[ {\begin{align}3\\8\\7\\0\end{align}} \right]\).

First, find \({u_1} \cdot {u_2}\):

\(\begin{align}{c}{u_1} \cdot {u_2} = \left( 3 \right)\left( { - 1} \right) + \left( { - 2} \right)\left( 3 \right) + \left( 1 \right)\left( { - 3} \right) + \left( 3 \right)\left( 4 \right)\\ = - 3 - 6 - 3 + 12\\ = 0\end{align}\)

Now, find \({u_2} \cdot {u_3}\)::

\(\begin{align}{c}{u_2} \cdot {u_3} = \left( { - 1} \right)\left( 3 \right) + \left( 3 \right)\left( 8 \right) + \left( { - 3} \right)\left( 7 \right) + \left( 4 \right)\left( 0 \right)\\ = - 3 + 24 - 21 + 0\\ = 0\end{align}\)

And find \({u_1} \cdot {u_3}\)::

\(\begin{align}{c}{u_1} \cdot {u_3} = \left( 3 \right)\left( 3 \right) + \left( { - 2} \right)\left( 8 \right) + \left( 1 \right)\left( 7 \right) + \left( 3 \right)\left( 0 \right)\\ = 9 - 16 + 7 + 0\\ = 0\end{align}\)

Since all the pairs are orthogonal hence, the given set is orthogonal.

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Most popular questions from this chapter

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 \({\bf{\hat x}}\).

3. \(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{ - {\bf{2}}}\\{ - {\bf{1}}}&{\bf{2}}\\{\bf{0}}&{\bf{3}}\\{\bf{2}}&{\bf{5}}\end{aligned}} \right)\), \({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{3}}\\{\bf{1}}\\{ - {\bf{4}}}\\{\bf{2}}\end{aligned}} \right)\)

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

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 u and v be linearly independent vectors in \({\mathbb{R}^n}\) that are not orthogonal. Describe how to find the best approximation to z in \({\mathbb{R}^n}\) by vectors of the form \({{\bf{x}}_1}{\mathop{\rm u}\nolimits} + {{\bf{x}}_2}{\mathop{\rm u}\nolimits} \) without first constructing an orthogonal basis for \({\mathop{\rm Span}\nolimits} \left\{ {{\bf{u}},{\bf{v}}} \right\}\).

(M) Use the method in this section to produce a \(QR\) factorization of the matrix in Exercise 24.

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