Question 20: Let \({{\mathop{\rm u}\nolimits} _1}\) and \({{\mathop{\rm u}\nolimits} _2}\) be as in Exercise 19, and let \({{\mathop{\rm u}\nolimits} _4} = \left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right)\). It can be shown that \({{\mathop{\rm u}\nolimits} _4}\) is not in the subspace \(W\) spanned by \({{\mathop{\rm u}\nolimits} _1}\), and \({{\mathop{\rm u}\nolimits} _2}\). Use this fact to construct a nonzero vector v in \({\mathbb{R}^3}\) that is orthogonal to \({{\mathop{\rm u}\nolimits} _1}\), and \({{\mathop{\rm u}\nolimits} _2}\).

Short Answer

Expert verified

The vector \({\bf{v}}\) is \({\bf{v}} = \left( {\begin{array}{*{20}{c}}0\\{\frac{4}{5}}\\{\frac{2}{5}}\end{array}} \right)\).

Step by step solution

01

The Orthogonal Decomposition Theorem

Suppose that \(W\) is a subspace of \({\mathbb{R}^n}\). Then each \({\bf{y}}\) in \({\mathbb{R}^n}\) can be expressed uniquely in the form

\({\bf{y}} = \widehat {\bf{y}} + {\bf{z}}\) … (1)

With \(\widehat {\bf{y}}\) is in \(W\) and \({\bf{z}}\) is in \({W^ \bot }\). In particular, when \(\left\{ {{{\mathop{\rm u}\nolimits} _1}, \ldots ,{{\mathop{\rm u}\nolimits} _p}} \right\}\) is anorthogonal basis of \(W\), then;

\(\widehat {\bf{y}} = \frac{{{\mathop{\rm y}\nolimits} \cdot {{\mathop{\rm u}\nolimits} _1}}}{{{{\mathop{\rm u}\nolimits} _1} \cdot {{\mathop{\rm u}\nolimits} _1}}}{{\mathop{\rm u}\nolimits} _1} + \ldots + \frac{{{\mathop{\rm y}\nolimits} \cdot {{\mathop{\rm u}\nolimits} _p}}}{{{{\mathop{\rm u}\nolimits} _p} \cdot {{\mathop{\rm u}\nolimits} _p}}}{{\mathop{\rm u}\nolimits} _p}\) … (2)

And, \({\bf{z}} = {\bf{y}} - \widehat {\bf{y}}\).

02

Construct a nonzero vector v in \({\mathbb{R}^3}\)

Consider that \({{\bf{u}}_1} = \left( {\begin{array}{*{20}{c}}1\\1\\{ - 2}\end{array}} \right),{\rm{ }}{{\bf{u}}_2} = \left( {\begin{array}{*{20}{c}}5\\{ - 1}\\2\end{array}} \right)\), and \({{\bf{u}}_3} = \left( {\begin{array}{*{20}{c}}0\\0\\1\end{array}} \right)\). Let, \({{\bf{u}}_4} = \left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right)\).

According to the Orthogonal Decomposition Theorem, the sum of two vectors in \(W = {\mathop{\rm Span}\nolimits} \left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\) is \({{\bf{u}}_4}\) and a vectorv orthogonal to \(W\).

Construct a nonzero vector \({\mathop{\rm v}\nolimits} \) in \({\mathbb{R}^3}\) as shown below:

\(\begin{array}{c}{\bf{v}} = {{\bf{u}}_4} - {{\mathop{\rm proj}\nolimits} _W}{{\bf{u}}_4}\\ = {{\bf{u}}_4} - \left( {\frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \frac{{{\bf{y}} \cdot {{\bf{u}}_2}}}{{{{\bf{u}}_2} \cdot {{\bf{u}}_2}}}{{\bf{u}}_2}} \right)\\ = {{\bf{u}}_4} - \left( {\frac{1}{6}{{\bf{u}}_1} - \frac{1}{{30}}{{\bf{u}}_2}} \right)\\ = \left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right) - \left( {\frac{1}{6}\left( {\begin{array}{*{20}{c}}1\\1\\{ - 2}\end{array}} \right) - \frac{1}{{30}}\left( {\begin{array}{*{20}{c}}5\\{ - 1}\\2\end{array}} \right)} \right)\\ = \left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right) - \left( {\left( {\begin{array}{*{20}{c}}{\frac{1}{6}}\\{\frac{1}{6}}\\{ - \frac{2}{6}}\end{array}} \right) - \left( {\begin{array}{*{20}{c}}{\frac{5}{{30}}}\\{ - \frac{1}{{30}}}\\{\frac{2}{{30}}}\end{array}} \right)} \right)\\ = \left( {\begin{array}{*{20}{c}}0\\1\\0\end{array}} \right) - \left( {\begin{array}{*{20}{c}}0\\{\frac{1}{5}}\\{ - \frac{2}{5}}\end{array}} \right)\\ = \left( {\begin{array}{*{20}{c}}0\\{\frac{4}{5}}\\{\frac{2}{5}}\end{array}} \right)\end{array}\)

Therefore, each multiple of the vector \({\bf{v}}\) is also in \({W^ \bot }\).

Thus, the nonzero vector \({\bf{v}}\) is \({\bf{v}} = \left( {\begin{array}{*{20}{c}}0\\{\frac{4}{5}}\\{\frac{2}{5}}\end{array}} \right)\).

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Most popular questions from this chapter

Suppose \(A = QR\), where \(R\) is an invertible matrix. Showthat \(A\) and \(Q\) have the same column space.

A healthy child’s systolic blood pressure (in millimetres of mercury) and weight (in pounds) are approximately related by the equation

\({\beta _0} + {\beta _1}\ln w = p\)

Use the following experimental data to estimate the systolic blood pressure of healthy child weighing 100 pounds.

\(\begin{array} w&\\ & {44}&{61}&{81}&{113}&{131} \\ \hline {\ln w}&\\vline & {3.78}&{4.11}&{4.39}&{4.73}&{4.88} \\ \hline p&\\vline & {91}&{98}&{103}&{110}&{112} \end{array}\)

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

A certain experiment produces the data \(\left( {1,1.8} \right),\left( {2,2.7} \right),\left( {3,3.4} \right),\left( {4,3.8} \right),\left( {5,3.9} \right)\). Describe the model that produces a least-squares fit of these points by a function of the form

\(y = {\beta _1}x + {\beta _2}{x^2}\)

Such a function might arise, for example, as the revenue from the sale of \(x\) units of a product, when the amount offered for sale affects the price to be set for the product.

a. Give the design matrix, the observation vector, and the unknown parameter vector.

b. Find the associated least-squares curve for the data.

A simple curve that often makes a good model for the variable costs of a company, a function of the sales level \(x\), has the form \(y = {\beta _1}x + {\beta _2}{x^2} + {\beta _3}{x^3}\). There is no constant term because fixed costs are not included.

a. Give the design matrix and the parameter vector for the linear model that leads to a least-squares fit of the equation above, with data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\).

b. Find the least-squares curve of the form above to fit the data \(\left( {4,1.58} \right),\left( {6,2.08} \right),\left( {8,2.5} \right),\left( {10,2.8} \right),\left( {12,3.1} \right),\left( {14,3.4} \right),\left( {16,3.8} \right)\) and \(\left( {18,4.32} \right)\), with values in thousands. If possible, produce a graph that shows the data points and the graph of the cubic approximation.

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