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Question 16: Let \({\mathop{\rm y}\nolimits} = \left( {\begin{array}{*{20}{c}}{ - 3}\\9\end{array}} \right)\), and \({\mathop{\rm u}\nolimits} = \left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right)\). Compute the distance from y to the line through u and the origin.

Short Answer

Expert verified

The distance from y to the line throughu and the origin is \(3\sqrt 5 \) units.

Step by step solution

01

Definition of the orthogonal projection

The equation \({\bf{y}} = \widehat {\bf{y}} + {\bf{z}}\) is satisfied with \({\bf{z}}\) orthogonal to \({\bf{u}}\) such that if \(\alpha = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}\), and \(\widehat {\bf{y}} = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}{\bf{u}}\). Then, the vector \(\widehat {\mathop{\rm y}\nolimits} \) is known as theorthogonal projection of y onto uand, the vector \({\bf{z}}\) is known as thecomponentof \({\mathop{\rm y}\nolimits} \) orthogonal to \({\bf{u}}\).

Typically, \(\widehat {\bf{y}}\) is represented by \({{\mathop{\rm proj}\nolimits} _L}{\bf{y}}\) and is called the orthogonal projection of \({\mathop{\rm y}\nolimits} \) onto L. Then, \(\widehat {\bf{y}} = {{\mathop{\rm proj}\nolimits} _L}{\bf{y}} = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}{\bf{u}}\).

02

Compute the component of y orthogonal to u

Compute \({\bf{y}} \cdot {\bf{u}}\) and \({\bf{u}} \cdot {\bf{u}}\) as shown below:

\(\begin{array}{c}{\bf{y}} \cdot {\bf{u}} = \left( {\begin{array}{*{20}{c}}{ - 3}\\9\end{array}} \right) \cdot \left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right)\\ = - 3 + 18\\ = 15\end{array}\)

And,

\(\begin{array}{c}{\bf{u}} \cdot {\bf{u}} = \left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right) \cdot \left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right)\\ = 1 + 4\\ = 5\end{array}\)

Compute \({\bf{y}} - \widehat {\bf{y}}\) as shown below:

\(\begin{array}{c}{\bf{y}} - \widehat {\bf{y}} = \frac{{{\bf{y}} \cdot {\bf{u}}}}{{{\bf{u}} \cdot {\bf{u}}}}{\bf{u}}\\ = \left( {\begin{array}{*{20}{c}}{ - 3}\\9\end{array}} \right) - \frac{{15}}{5}\left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - 3}\\9\end{array}} \right) - 3\left( {\begin{array}{*{20}{c}}1\\2\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - 3}\\9\end{array}} \right) - \left( {\begin{array}{*{20}{c}}3\\6\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}{ - 6}\\3\end{array}} \right)\end{array}\)

Therefore, the component of \({\bf{y}}\) orthogonal to \({\bf{u}}\) is \({\bf{y}} - \widehat {\bf{y}} = \left( {\begin{array}{*{20}{c}}{ - 6}\\3\end{array}} \right)\).

03

Compute the distance from y to the line through u and the origin

The distance from y to the line through u and the origin is the length of the perpendicular line segment from y to the orthogonal projection \(\widehat {\bf{y}}\), that is \(\left\| {{\bf{y}} - \widehat {\bf{y}}} \right\|\).

Compute \(\left\| {{\bf{y}} - \widehat {\bf{y}}} \right\|\) as shown below:

\(\begin{array}{c}\left\| {{\bf{y}} - \widehat {\bf{y}}} \right\| = \sqrt {{{\left( { - 6} \right)}^2} + {{\left( 3 \right)}^2}} \\ = \sqrt {\left( {36} \right) + \left( 9 \right)} \\ = \sqrt {45} \\ = 3\sqrt 5 \end{array}\)

Thus, the distance from \({\bf{y}}\) to the line through u and the origin is \(3\sqrt 5 \) units.

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Most popular questions from this chapter

In Exercises 13 and 14, the columns of Q were obtained by applying the Gram-Schmidt process to the columns of A. Find an upper triangular matrix R such that \(A = QR\). Check your work.

13. \(A = \left( {\begin{aligned}{{}{}}5&9\\1&7\\{ - 3}&{ - 5}\\1&5\end{aligned}} \right),{\rm{ }}Q = \left( {\begin{aligned}{{}{}}{\frac{5}{6}}&{ - \frac{1}{6}}\\{\frac{1}{6}}&{\frac{5}{6}}\\{ - \frac{3}{6}}&{\frac{1}{6}}\\{\frac{1}{6}}&{\frac{3}{6}}\end{aligned}} \right)\)

Use the inner product axioms and other results of this section to verify the statements in Exercises 15–18.

15.\(\left\langle {{\rm{u,}}\,c{\rm{v}}} \right\rangle = c\left\langle {{\rm{u,}}\,{\rm{v}}} \right\rangle \) for all scalars \(c\).

Suppose the x-coordinates of the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) are in mean deviation form, so that \(\sum {{x_i}} = 0\). Show that if \(X\) is the design matrix for the least-squares line in this case, then \({X^T}X\) is a diagonal matrix.

Question: In Exercises 17-22, determine which sets of vectors are orthonormal. If a set is only orthogonal, normalize the vectors to produce an orthonormal set.

20. \(\left( {\begin{array}{*{20}{c}}{ - \frac{2}{3}}\\{\frac{1}{3}}\\{\frac{2}{3}}\end{array}} \right),\left( {\begin{array}{*{20}{c}}{\frac{1}{3}}\\{\frac{2}{3}}\\0\end{array}} \right)\)

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

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