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Question: In Exercises 1 and 2, you may assume that\(\left\{ {{{\bf{u}}_{\bf{1}}},...,{{\bf{u}}_{\bf{4}}}} \right\}\)is an orthogonal basis for\({\mathbb{R}^{\bf{4}}}\).

1.\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{*{20}{c}}{\bf{0}}\\{\bf{1}}\\{ - {\bf{4}}}\\{ - {\bf{1}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{*{20}{c}}{\bf{3}}\\{\bf{5}}\\{\bf{1}}\\{\bf{1}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{3}}} = \left[ {\begin{aligned}{*{20}{c}}{\bf{1}}\\{\bf{0}}\\{\bf{1}}\\{ - {\bf{4}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{4}}} = \left[ {\begin{aligned}{*{20}{c}}{\bf{5}}\\{ - {\bf{3}}}\\{ - {\bf{1}}}\\{\bf{1}}\end{aligned}} \right]\),\({\bf{x}} = \left[ {\begin{aligned}{*{20}{c}}{{\bf{10}}}\\{ - {\bf{8}}}\\{\bf{2}}\\{\bf{0}}\end{aligned}} \right]\)

Write x as the sum of two vectors, one in\({\bf{Span}}\left\{ {{{\bf{u}}_1},{{\bf{u}}_2},{{\bf{u}}_3}} \right\}\)and the other in\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{4}}}} \right\}\).

Short Answer

Expert verified

The vector is \({\bf{x}} = \left[ {\begin{aligned}{*{20}{c}}0\\{ - 2}\\4\\{ - 2}\end{aligned}} \right] + \left[ {\begin{aligned}{*{20}{c}}{10}\\{ - 6}\\{ - 2}\\2\end{aligned}} \right]\).\(\)

Step by step solution

01

Find the orthogonal projection of x

The orthogonal projection of xcan be calculated as follows:

\[\begin{aligned}{c}{\bf{\hat x}} = \frac{{{\bf{x}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \frac{{{\bf{x}} \cdot {{\bf{u}}_2}}}{{{{\bf{u}}_2} \cdot {{\bf{u}}_2}}}{{\bf{u}}_2} + \frac{{{\bf{x}} \cdot {{\bf{u}}_3}}}{{{{\bf{u}}_3} \cdot {{\bf{u}}_3}}}{{\bf{u}}_3}\\ = \frac{{\left[ {\begin{aligned}{*{20}{c}}{10}&{ - 8}&2&0\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}0\\1\\{ - 4}\\{ - 1}\end{aligned}} \right]}}{{\left[ {\begin{aligned}{*{20}{c}}0&1&{ - 4}&{ - 1}\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}0\\1\\{ - 4}\\{ - 1}\end{aligned}} \right]}}{{\bf{u}}_1} + \frac{{\left[ {\begin{aligned}{*{20}{c}}{10}&{ - 8}&2&0\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}3\\5\\1\\1\end{aligned}} \right]}}{{\left[ {\begin{aligned}{*{20}{c}}3&5&1&1\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}3\\5\\1\\1\end{aligned}} \right]}}{{\bf{u}}_2} + \frac{{\left[ {\begin{aligned}{*{20}{c}}{10}&{ - 8}&2&0\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}1\\0\\1\\{ - 4}\end{aligned}} \right]}}{{\left[ {\begin{aligned}{*{20}{c}}1&0&1&{ - 4}\end{aligned}} \right]\left[ {\begin{aligned}{*{20}{c}}1\\0\\1\\{ - 4}\end{aligned}} \right]}}{{\bf{u}}_3}\\ = - \frac{8}{9}\left[ {\begin{aligned}{*{20}{c}}0\\1\\{ - 4}\\{ - 1}\end{aligned}} \right] - \frac{2}{9}\left[ {\begin{aligned}{*{20}{c}}3\\5\\1\\1\end{aligned}} \right] + \frac{6}{9}\left[ {\begin{aligned}{*{20}{c}}1\\0\\1\\{ - 4}\end{aligned}} \right]\\ = \left[ {\begin{aligned}{*{20}{c}}0\\{ - 2}\\4\\{ - 2}\end{aligned}} \right]\end{aligned}\]

02

Find the orthogonal component

The orthogonal component can be calculated as follows:

\(\begin{aligned}{l}z = {\bf{x}} - {\bf{\hat x}}\\ = \left[ {\begin{aligned}{*{20}{c}}{10}\\{ - 8}\\2\\0\end{aligned}} \right] - \left[ {\begin{aligned}{*{20}{c}}0\\{ - 2}\\4\\{ - 2}\end{aligned}} \right]\\ = \left[ {\begin{aligned}{*{20}{c}}{10}\\{ - 6}\\{ - 2}\\2\end{aligned}} \right]\end{aligned}\)

The component x as the sum of given vectors is \({\bf{x}} = \left[ {\begin{aligned}{*{20}{c}}0\\{ - 2}\\4\\{ - 2}\end{aligned}} \right] + \left[ {\begin{aligned}{*{20}{c}}{10}\\{ - 6}\\{ - 2}\\2\end{aligned}} \right]\).

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Most popular questions from this chapter

Let S(t)be the number of daylight hours on the tth day of the year 2012 in Rome, Italy. We are given the following data for S(t):

We wish to fit a trigonometric function of the form

f(t)=a+bsin(2蟿蟿366t)+ccos(2蟿蟿366t)

To these data. Find the best approximation of this form, using least squares. How many daylight hours does your model predict for the longest day of the year 2012? (The actual value is 15 hours, 13 minutes, 39 seconds.)

To make a trend analysis of six evenly spaced data points, one can use orthogonal polynomials with respect to evaluation at the points \(t = - 5, - 3, - 1,\,\,1,\,\,3,{\rm{ and }}5\).

  1. Show that the first three orthogonal polynomials are

\({p_0}\left( t \right) = 1,\,\,\,\,\,\,{p_1}\left( t \right) = t,{\rm{ and }}{p_2}\left( t \right) = \frac{3}{8}{t^2} - \frac{{35}}{8}\)

(The polynomial \({p_2}\) has been scaled so that its values at the evaluation points are small integers.)

  1. Fit a quadratic trend function to the data \(\left( { - 5,1} \right),\left( { - 3,1} \right),\left( { - 1,4} \right),\left( {1,4} \right),\left( {3,6} \right),\left( {5,8} \right)\).

Let Abe annmmatrix. Is the formula(kerA)=im(AT)necessarily true? Explain.

Consider a consistent system Ax=b.

(a) Show that this system has a solution x0 in (kerA) .

(b) Show that the systemAx=b has only one solution in (kerA) .

(c) Ifx0 is the solution in (kerA) androle="math" localid="1660124695419" x1is another solution of the system Ax=b , show that||x0||<||x1|| . The vectorx0 is called the minimal solution of the linear system Ax=b .

Consider the linear systemAx=b , where

A=[1326]and b=[1020].

a. Draw a sketch showing the following subsets of 2:

  • The kernel ofA , and(kerA)
  • The image of AT
  • The solution setSof the system Ax=b

b.What relationship do you observe between(kerA) and im(AT)? Explain.

c. What relationship do you observe betweenrole="math" localid="1660916844921" ker(A) and S? Explain.

d. Find the unique vectorx0 in the intersection ofS and(kerA) . Show x0on your sketch.

e. What can you say about the length of x0compared with the length of all other vectors in S?

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