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Question: In exercises 7-10, show that \(\left\{ {{u_1},{u_2}} \right\}\) or \(\left\{ {{u_1},{u_2},{u_3}} \right\}\) is an orthogonal basis for \({\mathbb{R}^2}\) or \({\mathbb{R}^3}\), respectively. Then express \(x\) as a linear combination of the \(u\)’s.

9. \({u_1} = \left( {\begin{array}{*{20}{c}}1\\0\\1\end{array}} \right)\), \({u_2} = \left( {\begin{array}{*{20}{c}}{ - 1}\\4\\1\end{array}} \right)\), \({u_3} = \left( {\begin{array}{*{20}{c}}2\\1\\{ - 2}\end{array}} \right)\) and \(x = \left( {\begin{array}{*{20}{c}}8\\{ - 4}\\{ - 3}\end{array}} \right)\)

Short Answer

Expert verified

The required linear combination is, \(x = \frac{5}{2}{u_1} - \frac{3}{2}{u_2} + 2{u_3}\).

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

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

\(\begin{array}{c}{u_1} \cdot {u_2} = \left( 1 \right)\left( { - 1} \right) + \left( 0 \right)\left( 4 \right) + \left( 1 \right)\left( 1 \right)\\ = - 1 + 0 + 1\\ = 0\end{array}\)

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

\(\begin{array}{c}{u_2} \cdot {u_3} = \left( { - 1} \right)\left( 2 \right) + \left( 4 \right)\left( 1 \right) + \left( 1 \right)\left( { - 2} \right)\\ = - 2 + 4 - 2\\ = 0\end{array}\)

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

\(\begin{array}{c}{u_1} \cdot {u_3} = \left( 1 \right)\left( 2 \right) + \left( 0 \right)\left( 1 \right) + \left( 1 \right)\left( { - 2} \right)\\ = 2 + 0 - 2\\ = 0\end{array}\)

Hence, all vectors are orthogonal to each other as the vectors are non-zero and linearly independent. Therefore, the given set form a basis for \({\mathbb{R}^3}\).

03

Express \(x\) as a linear combination

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

\(\begin{array}{c}x = \left( {\frac{{x \cdot {u_1}}}{{{u_1} \cdot {u_1}}}} \right){u_1} + \left( {\frac{{x \cdot {u_2}}}{{{u_2} \cdot {u_2}}}} \right){u_2} + \left( {\frac{{x \cdot {u_3}}}{{{u_3} \cdot {u_3}}}} \right){u_3}\\ = \left( {\frac{{\left( 8 \right)\left( 1 \right) + \left( { - 4} \right)\left( 0 \right) + \left( { - 3} \right)\left( 1 \right)}}{{\left( 1 \right)\left( 1 \right) + \left( 0 \right)\left( 0 \right) + \left( 1 \right)\left( 1 \right)}}} \right){u_1} + \left( {\frac{{\left( 8 \right)\left( { - 1} \right) + \left( { - 4} \right)\left( 4 \right) + \left( { - 3} \right)\left( 1 \right)}}{{\left( { - 1} \right)\left( { - 1} \right) + \left( 4 \right)\left( 4 \right) + \left( 1 \right)\left( 1 \right)}}} \right){u_2} + \left( {\frac{{\left( 8 \right)\left( 2 \right) + \left( { - 4} \right)\left( 2 \right) + \left( { - 3} \right)\left( { - 2} \right)}}{{\left( 2 \right)\left( 2 \right) + \left( 1 \right)\left( 1 \right) + \left( { - 2} \right)\left( { - 2} \right)}}} \right){u_3}\\ = \left( {\frac{{8 - 0 - 3}}{{1 + 1}}} \right){u_1} + \left( {\frac{{ - 8 - 16 - 3}}{{1 + 16 + 1}}} \right){u_2} + \left( {\frac{{16 - 4 + 6}}{{4 + 1 + 4}}} \right){u_3}\\ = \left( {\frac{5}{2}} \right){u_1} + \left( {\frac{{ - 27}}{{18}}} \right){u_2} + \left( {\frac{{18}}{9}} \right){u_3}\,\\ = \frac{5}{2}{u_1} - \frac{3}{2}{u_2} + 2{u_3}\end{array}\)

Hence, \(x = \frac{5}{2}{u_1} - \frac{3}{2}{u_2} + 2{u_3}\).

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Most popular questions from this chapter

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}4\\6\end{aligned}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{aligned}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}6\\{ - 2}\\3\end{aligned}} \right)\)

6. \(\left( {\frac{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm w}\nolimits} }}{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm x}\nolimits} }}} \right){\mathop{\rm x}\nolimits} \)

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

12. \(\left( {\begin{aligned}{{}{}}1&3&5\\{ - 1}&{ - 3}&1\\0&2&3\\1&5&2\\1&5&8\end{aligned}} \right)\)

Given data for a least-squares problem, \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\), the following abbreviations are helpful:

\(\begin{aligned}{l}\sum x = \sum\nolimits_{i = 1}^n {{x_i}} ,{\rm{ }}\sum {{x^2}} = \sum\nolimits_{i = 1}^n {x_i^2} ,\\\sum y = \sum\nolimits_{i = 1}^n {{y_i}} ,{\rm{ }}\sum {xy} = \sum\nolimits_{i = 1}^n {{x_i}{y_i}} \end{aligned}\)

The normal equations for a least-squares line \(y = {\hat \beta _0} + {\hat \beta _1}x\)may be written in the form

\(\begin{aligned}{{\hat \beta }_0} + {{\hat \beta }_1}\sum x = \sum y \\{{\hat \beta }_0}\sum x + {{\hat \beta }_1}\sum {{x^2}} = \sum {xy} {\rm{ (7)}}\end{aligned}\)

16. Use a matrix inverse to solve the system of equations in (7) and thereby obtain formulas for \({\hat \beta _0}\) , and that appear in many statistics texts.

In Exercises 3–6, verify that\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\]is an orthogonal set, and then find the orthogonal projection of\[{\bf{y}}\]onto Span\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\].

5.\[y = \left[ {\begin{aligned}{ - 1}\\2\\6\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}3\\{ - 1}\\2\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}1\\{ - 1}\\{ - 2}\end{aligned}} \right]\]

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be a linear transformation that preserves lengths; that is, \(\left\| {T\left( {\bf{x}} \right)} \right\| = \left\| {\bf{x}} \right\|\) for all x in \({\mathbb{R}^n}\).

  1. Show that T also preserves orthogonality; that is, \(T\left( {\bf{x}} \right) \cdot T\left( {\bf{y}} \right) = 0\) whenever \({\bf{x}} \cdot {\bf{y}} = 0\).
  2. Show that the standard matrix of T is an orthogonal matrix.
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