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

Short Answer

Expert verified

The factorization of the matrix is,\(A = \left( {\begin{aligned}{{}{r}}{\frac{1}{2}}&{ - \frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\{ - \frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\0&{\frac{1}{{\sqrt 2 }}}&0\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{ - \frac{1}{2}}\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}2&8&7\\0&{2\sqrt 2 }&{3\sqrt 2 }\\0&0&6\end{aligned}} \right)\)

Step by step solution

01

\(QR\) factorization of a Matrix

A matrix with order \(m \times n\) can be written as the multiplication of aupper triangular matrix \(R\) and a matrix \(Q\) which is formed by applying Gram–Schmidt orthogonalization process to the \({\rm{col}}\left( A \right)\).

The matrix \(R\) can be found by the formula \({Q^T}A = R\).

02

Finding the matrix \(R\)

From exercise 11 we have,

\(A = \left( {\begin{aligned}{{}{r}}1&3&5\\{ - 1}&{ - 3}&1\\0&2&3\\1&5&2\\1&5&8\end{aligned}} \right)\)

Again, with help of exercise 12 where we have calculated the orthogonal basis for columns of \(A\) we have

\(\left\{ {\left( {\begin{aligned}{{}{r}}1\\{ - 1}\\0\\1\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}{ - 1}\\1\\2\\1\\1\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}3\\3\\0\\{ - 3}\\3\end{aligned}} \right)} \right\}\)

Now normalizing them we get,

\(\begin{aligned}{}\left\{ {\frac{{{v_1}}}{{\left\| {{v_1}} \right\|}},\frac{{{v_2}}}{{\left\| {{v_2}} \right\|}},\frac{{{v_3}}}{{\left\| {{v_3}} \right\|}}} \right\} & = \left\{ {\frac{{\left( {\begin{aligned}{{}{r}}1\\{ - 1}\\0\\1\\1\end{aligned}} \right)}}{{\sqrt 4 }},\frac{{\left( {\begin{aligned}{{}{r}}{ - 1}\\1\\2\\1\\1\end{aligned}} \right)}}{{\sqrt 8 }},\frac{{\left( {\begin{aligned}{{}{r}}3\\3\\0\\{ - 3}\\3\end{aligned}} \right)}}{{\sqrt {4 \cdot 9} }}} \right\}\\\left\{ {\frac{{{v_1}}}{{\left\| {{v_1}} \right\|}},\frac{{{v_2}}}{{\left\| {{v_2}} \right\|}},\frac{{{v_3}}}{{\left\| {{v_3}} \right\|}}} \right\} & = \left\{ {\left( {\begin{aligned}{{}{r}}{\frac{1}{2}}\\{\frac{{ - 1}}{2}}\\0\\{\frac{1}{2}}\\{\frac{1}{2}}\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}{ - \frac{1}{{2\sqrt 2 }}}\\{\frac{1}{{2\sqrt 2 }}}\\{\frac{1}{{\sqrt 2 }}}\\{\frac{1}{{2\sqrt 2 }}}\\{\frac{1}{{2\sqrt 2 }}}\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}{\frac{1}{2}}\\{\frac{1}{2}}\\0\\{ - \frac{1}{2}}\\{\frac{1}{2}}\end{aligned}} \right)} \right\}\end{aligned}\)

Hence the matrix \(Q\) will be:

\(Q = \left( {\begin{aligned}{{}{r}}{\frac{1}{2}}&{ - \frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\{ - \frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\0&{\frac{1}{{\sqrt 2 }}}&0\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{ - \frac{1}{2}}\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\end{aligned}} \right)\)

Now, calculate \({Q^T}A = R\) by using \(A\) and \(Q\).

\(\begin{aligned}{}R & = {Q^T}A\\ & = \left( {\begin{aligned}{{}{r}}{\frac{1}{2}}&{ - \frac{1}{2}}&0&{\frac{1}{2}}&{\frac{1}{2}}\\{ - \frac{1}{{2\sqrt 2 }}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{{\sqrt 2 }}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{{2\sqrt 2 }}}\\{\frac{1}{2}}&{\frac{1}{2}}&0&{\frac{{ - 1}}{2}}&{\frac{1}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{r}}1&3&5\\{ - 1}&{ - 3}&1\\0&2&3\\1&5&2\\1&5&8\end{aligned}} \right)\\ & = \left( {\begin{aligned}{{}{}}2&8&7\\0&{2\sqrt 2 }&{3\sqrt 2 }\\0&0&6\end{aligned}} \right)\end{aligned}\)

Hence, the required factorization is,\(A = \left( {\begin{aligned}{{}{r}}{\frac{1}{2}}&{ - \frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\{ - \frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\\0&{\frac{1}{{\sqrt 2 }}}&0\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{ - \frac{1}{2}}\\{\frac{1}{2}}&{\frac{1}{{2\sqrt 2 }}}&{\frac{1}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}2&8&7\\0&{2\sqrt 2 }&{3\sqrt 2 }\\0&0&6\end{aligned}} \right)\).

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Most popular questions from this chapter

Let \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) be an orthonormal set. Verify the following equality by induction, beginning with \(p = 2\). If \({\bf{x}} = {c_1}{{\bf{v}}_1} + \ldots + {c_p}{{\bf{v}}_p}\), then

\({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + {\left| {{c_2}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\)

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.

(M) Use the method in this section to produce a \(QR\) factorization of the matrix in Exercise 24.

Let \({\mathbb{R}^{\bf{2}}}\) have the inner product of Example 1. Show that the Cauchy-Schwarz inequality holds for \({\bf{x}} = \left( {{\bf{3}}, - {\bf{2}}} \right)\) and \({\bf{y}} = \left( { - {\bf{2}},{\bf{1}}} \right)\). (Suggestion: Study \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^{\bf{2}}}\).)

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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