Chapter 5: Q8E (page 224)
Using paper and pencil, perform the Gram-Schmidt process on the sequences of vectors given in Exercises 1 through 14.
8.
Short Answer
The orthonormal vectors of the sequence .
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Chapter 5: Q8E (page 224)
Using paper and pencil, perform the Gram-Schmidt process on the sequences of vectors given in Exercises 1 through 14.
8.
The orthonormal vectors of the sequence .
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Consider the linear system , where
and .
a. Draw a sketch showing the following subsets of :
b.What relationship do you observe between and ? Explain.
c. What relationship do you observe betweenrole="math" localid="1660916844921" and ? Explain.
d. Find the unique vector in the intersection of and . Show on your sketch.
e. What can you say about the length of compared with the length of all other vectors in ?
To make a trend analysis of six evenly spaced data points, one can use orthogonal polynomials with respect to evaluation at the points \(t = - 5, - 3, - 1,\,\,1,\,\,3,{\rm{ and }}5\).
\({p_0}\left( t \right) = 1,\,\,\,\,\,\,{p_1}\left( t \right) = t,{\rm{ and }}{p_2}\left( t \right) = \frac{3}{8}{t^2} - \frac{{35}}{8}\)
(The polynomial \({p_2}\) has been scaled so that its values at the evaluation points are small integers.)
Find the least-squares line \(y = {\beta _0} + {\beta _1}x\) that best fits the data \(\left( { - 2,0} \right),\left( { - 1,0} \right),\left( {0,2} \right),\left( {1,4} \right),{\rm{ and }}\left( {2,4} \right)\), assuming that the first and last data points are less reliable. Weight them half as much as the three interior points.
Find an orthonormal basis of the kernel of the matrix .
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\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{4}}}} \right\}\).
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