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Find a \(QR\) factorization of the matrix in Exercise 11.

Short Answer

Expert verified

The factorization of the matrix is,\(A = \left( {\begin{aligned}{{}{r}}{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{{ - 1}}{{\sqrt 5 }}}&0&0\\{\frac{{ - 1}}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{{ - 1}}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{{ - 1}}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{\sqrt 5 }&{ - \sqrt 5 }&{4\sqrt 5 }\\0&6&{ - 2}\\0&0&4\end{aligned}} \right)\).

Step by step solution

01

\(QR\) factorization of a Matrix

A matrix with order \(m \times n\) can be written as the multiplication of an upper triangularmatrix \(R\) and a matrix \(Q\) which is formed by applying Gram鈥揝chmidt orthogonalizationprocess 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,

\(A = \left( {\begin{aligned}{{}{r}}1&2&5\\{ - 1}&1&{ - 4}\\{ - 1}&4&{ - 3}\\1&{ - 4}&7\\1&2&1\end{aligned}} \right)\)

Again, with help of exercise 11 where we have calculated the orthogonal basisfor columnsof \(A\) we have,

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

Now normalizing them we get

\(\begin{aligned}{}\left\{ {\frac{{{v_1}}}{{\left\| {{v_1}} \right\|}},\frac{{{v_2}}}{{\left\| {{v_2}} \right\|}},\frac{{{v_3}}}{{\left\| {{v_3}} \right\|}}} \right\} & = \left\{ {\frac{{\left( {\begin{aligned}{{}{r}}1\\{ - 1}\\{ - 1}\\1\\1\end{aligned}} \right)}}{{\sqrt 5 }},\frac{{\left( {\begin{aligned}{{}{r}}3\\0\\3\\{ - 3}\\3\end{aligned}} \right)}}{{\sqrt {4 \cdot 9} }},\frac{{\left( {\begin{aligned}{{}{r}}2\\0\\2\\2\\{ - 2}\end{aligned}} \right)}}{{\sqrt {16} }}} \right\}\\\left\{ {\frac{{{v_1}}}{{\left\| {{v_1}} \right\|}},\frac{{{v_2}}}{{\left\| {{v_2}} \right\|}},\frac{{{v_3}}}{{\left\| {{v_3}} \right\|}}} \right\} & = \left\{ {\left( {\begin{aligned}{{}{r}}{\frac{1}{{\sqrt 5 }}}\\{\frac{{ - 1}}{{\sqrt 5 }}}\\{\frac{{ - 1}}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{{\sqrt 5 }}}\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}{\frac{1}{2}}\\0\\{\frac{1}{2}}\\{ - \frac{1}{2}}\\{\frac{1}{2}}\end{aligned}} \right),\left( {\begin{aligned}{{}{r}}{\frac{1}{2}}\\0\\{\frac{1}{2}}\\{\frac{1}{2}}\\{ - \frac{1}{2}}\end{aligned}} \right)} \right\}\end{aligned}\)

Hence, the matrix \(Q\) will be:

\(Q = \left( {\begin{aligned}{{}{r}}{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{{ - 1}}{{\sqrt 5 }}}&0&0\\{\frac{{ - 1}}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{{ - 1}}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{{ - 1}}{2}}\end{aligned}} \right)\)

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

\(\begin{aligned}{}R & = {Q^T}A\\ & = \left( {\begin{aligned}{{}{r}}{\frac{1}{{\sqrt 5 }}}&{\frac{1}{{\sqrt 5 }}}&{\frac{1}{{\sqrt 5 }}}&{\frac{1}{{\sqrt 5 }}}&{\frac{1}{{\sqrt 5 }}}\\{\frac{1}{2}}&0&{\frac{1}{2}}&{\frac{{ - 1}}{2}}&{\frac{1}{2}}\\{\frac{1}{2}}&0&{\frac{1}{2}}&{\frac{1}{2}}&{\frac{{ - 1}}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{r}}1&2&5\\{ - 1}&1&{ - 4}\\{ - 1}&4&{ - 3}\\1&{ - 4}&7\\1&2&1\end{aligned}} \right)\\ & = \left( {\begin{aligned}{{}{r}}{\sqrt 5 }&{ - \sqrt 5 }&{4\sqrt 5 }\\0&6&{ - 2}\\0&0&4\end{aligned}} \right)\end{aligned}\)

Hence, the required factorization is,\(A = \left( {\begin{aligned}{{}{r}}{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{{ - 1}}{{\sqrt 5 }}}&0&0\\{\frac{{ - 1}}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{{ - 1}}{2}}&{\frac{1}{2}}\\{\frac{1}{{\sqrt 5 }}}&{\frac{1}{2}}&{\frac{{ - 1}}{2}}\end{aligned}} \right)\left( {\begin{aligned}{{}{}}{\sqrt 5 }&{ - \sqrt 5 }&{4\sqrt 5 }\\0&6&{ - 2}\\0&0&4\end{aligned}} \right)\).

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Most popular questions from this chapter

In Exercises 9-12, find (a) the orthogonal projection of b onto \({\bf{Col}}A\) and (b) a least-squares solution of \(A{\bf{x}} = {\bf{b}}\).

9. \(A = \left[ {\begin{aligned}{{}{}}{\bf{1}}&{\bf{5}}\\{\bf{3}}&{\bf{1}}\\{ - {\bf{2}}}&{\bf{4}}\end{aligned}} \right]\), \({\bf{b}} = \left[ {\begin{aligned}{{}{}}{\bf{4}}\\{ - {\bf{2}}}\\{ - {\bf{3}}}\end{aligned}} \right]\)

In Exercises 13 and 14, find the best approximation to\[{\bf{z}}\]by vectors of the form\[{c_1}{{\bf{v}}_1} + {c_2}{{\bf{v}}_2}\].

13.\[z = \left[ {\begin{aligned}3\\{ - 7}\\2\\3\end{aligned}} \right]\],\[{{\bf{v}}_1} = \left[ {\begin{aligned}2\\{ - 1}\\{ - 3}\\1\end{aligned}} \right]\],\[{{\bf{v}}_2} = \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\]

Exercises 19 and 20 involve a design matrix \(X\) with two or more columns and a least-squares solution \(\hat \beta \) of \({\bf{y}} = X\beta \). Consider the following numbers.

(i) \({\left\| {X\hat \beta } \right\|^2}\)鈥攖he sum of the squares of the 鈥渞egression term.鈥 Denote this number by .

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)鈥攖he sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)鈥攖he 鈥渢otal鈥 sum of the squares of the \(y\)-values. Denote this number by \(SS\left( T \right)\).

Every statistics text that discusses regression and the linear model \(y = X\beta + \in \) introduces these numbers, though terminology and notation vary somewhat. To simplify matters, assume that the mean of the -values is zero. In this case, \(SS\left( T \right)\) is proportional to what is called the variance of the set of -values.

19. Justify the equation \(SS\left( T \right) = SS\left( R \right) + SS\left( E \right)\). (Hint: Use a theorem, and explain why the hypotheses of the theorem are satisfied.) This equation is extremely important in statistics, both in regression theory and in the analysis of variance.

In exercises 1-6, determine which sets of vectors are orthogonal.

  1. \(\left[ {\begin{array}{*{20}{c}}{ - 1}\\4\\{ - 3}\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}5\\2\\1\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\{ - 4}\\{ - 7}\end{array}} \right]\)

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

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

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