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In exercises 7-10, show that {u1, u2} or {u1,u2,u3} is an orthogonal basis for \({\mathbb{R}^2}\) or \({\mathbb{R}^3}\), respectively. Then express x as a linear combination of the u.

7. \[{u_1} = \left[ {\begin{align}2\\{ - 3}\end{align}} \right]\], \[{u_2} = \left[ {\begin{align}6\\4\end{align}} \right]\], and \[x = \left[ {\begin{align}9\\{ - 7}\end{align}} \right]\]

Short Answer

Expert verified

The required linear combination is, \[x = 3{u_1} + \frac{1}{2}{u_2}\].

Step by step solution

01

Linear combination definition

Let the set of vectors \({u_1},.....,{u_p}\) be an orthogonal basis for a subspace \(W\) of \({\mathbb{R}^n}\) and the linear combination is given by \(y = {c_1}{u_1} + ..... + {c_p}{u_p}\) , then the weights in the linear combination are given as \({c_j} = \frac{{y \cdot {u_j}}}{{{u_j} \cdot {u_j}}}\), for each \(y\) in \(W\).

02

Check for orthogonality of given vectors

Find \({u_1} \cdot {u_2}\) as follows:

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

Hence, the vectors are orthogonal to each other, as the vectors are non-zero and linearly independent. Therefore, the given set form a basis for \({\mathbb{R}^2}\).

03

Express x as a linear combination

The vector x can be expressed as a linear combination as follows:

\[\begin{align}{c}x = \left( {\frac{{x \cdot {u_1}}}{{{u_1} \cdot {u_1}}}} \right){u_1} + \left( {\frac{{x \cdot {u_2}}}{{{u_2} \cdot {u_2}}}} \right){u_2}\\ = \left( {\frac{{\left( 9 \right)\left( 2 \right) + \left( { - 7} \right)\left( { - 3} \right)}}{{\left( 2 \right)\left( 2 \right) + \left( { - 3} \right)\left( { - 3} \right)}}} \right){u_1} + \left( {\frac{{\left( 9 \right)\left( 6 \right) + \left( { - 7} \right)\left( 4 \right)}}{{\left( 6 \right)\left( 6 \right) + \left( 4 \right)\left( 4 \right)}}} \right){u_2}\\ = \left( {\frac{{18 + 21}}{{4 + 9}}} \right){u_1} + \left( {\frac{{54 - 28}}{{36 + 16}}} \right){u_2}\\ = \left( {\frac{{18 + 2}}{{4 + 9}}} \right){u_1} + \left( {\frac{{54 - 28}}{{36 + 16}}} \right){u_2}\,\\ = 3{u_1} + \frac{1}{2}{u_2}\end{align}\]

Hence, \[x = 3{u_1} + \frac{1}{2}{u_2}\].

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Most popular questions from this chapter

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

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

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.)

In Exercises 9-12, find a unit vector in the direction of the given vector.

10. \(\left( {\begin{aligned}{*{20}{c}}{ - 6}\\4\\{ - 3}\end{aligned}} \right)\)

Find a \(QR\) factorization of the matrix in Exercise 12.

Question: In Exercises 3-6, verify that\(\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\)is an orthogonal set, and then find the orthogonal projection of y onto\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\).

4.\(y = \left[ {\begin{aligned}{\bf{6}}\\{\bf{3}}\\{ - {\bf{2}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{\bf{3}}\\{\bf{4}}\\{\bf{0}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{ - {\bf{4}}}\\{\bf{3}}\\{\bf{0}}\end{aligned}} \right]\)

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