Question: Show that columns of matrix A are orthogonal by making an appropriate matrix calculation. State the calculation you use.

\(A = \left( {\begin{array}{*{20}{c}}{ - {\bf{6}}}&{ - {\bf{3}}}&{\bf{6}}&{\bf{1}}\\{ - {\bf{1}}}&{\bf{2}}&{\bf{1}}&{ - {\bf{6}}}\\{\bf{3}}&{\bf{6}}&{\bf{3}}&{ - {\bf{2}}}\\{\bf{6}}&{ - {\bf{3}}}&{\bf{6}}&{ - {\bf{1}}}\\{\bf{2}}&{ - {\bf{1}}}&{\bf{2}}&{\bf{3}}\\{ - {\bf{3}}}&{\bf{6}}&{\bf{3}}&{\bf{2}}\\{ - {\bf{2}}}&{ - {\bf{1}}}&{\bf{2}}&{ - {\bf{3}}}\\{\bf{1}}&{\bf{2}}&{\bf{1}}&{\bf{6}}\end{array}} \right)\)

Short Answer

Expert verified

It is proved that columns of matrix A are orthogonal.

Step by step solution

01

Find the transpose of matrix A

Use the following MATLAB command to obtain the transpose of matrix A.

\(\begin{array}{c} > > {\rm{A}} = \left( \begin{array}{l}\begin{array}{*{20}{c}}{ - 6}&{ - 3}&6&1\end{array};\,\begin{array}{*{20}{c}}{ - 1}&2&1&{ - 6}\end{array};\,\begin{array}{*{20}{c}}3&6&3&{ - 2;\,\begin{array}{*{20}{c}}6&{ - 3}&6&{ - 1}\end{array}}\end{array};\\\begin{array}{*{20}{c}}2&{ - 1}&2&{3;\,\begin{array}{*{20}{c}}{ - 3}&6&3&{2;\,\,\begin{array}{*{20}{c}}{ - 2}&{ - 1}&2&{ - 3;\,\,\begin{array}{*{20}{c}}1&2&1&6\end{array}}\end{array}}\end{array}}\end{array}\end{array} \right);\\ > > {\rm{A'}} = {\rm{A}};\end{array}\)

The transpose of matrix A is shown below:

\({A^T} = \left( {\begin{array}{*{20}{c}}{ - 6}&{ - 1}&3&6&2&{ - 3}&{ - 2}&1\\{ - 3}&2&6&{ - 3}&{ - 1}&6&{ - 1}&2\\6&1&3&6&2&3&2&1\\1&{ - 6}&{ - 2}&{ - 1}&3&2&{ - 3}&6\end{array}} \right)\)

02

Find the product \({A^T}*A\)

The product \({A^T}*A\) can be calculated by using the following MATLAB code:

\({\rm{P}} = {\rm{A'}}*{\rm{A}}\)

The product \({A^T}*A\) is:

\(\begin{array}{c}{A^T}A = \left( {\begin{array}{*{20}{c}}{100}&0&0&0\\0&{100}&0&0\\0&0&{100}&0\\0&0&0&{100}\end{array}} \right)\\ = 100\left( {\begin{array}{*{20}{c}}1&0&0&0\\0&1&0&0\\0&0&1&0\\0&0&0&1\end{array}} \right)\\ = 100{I_4}\end{array}\)

As the matrix \({A^T}A\) has only diagonal entries, so the columns of matrix A are orthogonal.

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Most popular questions from this chapter

Compute the least-squares error associated with the least square solution found in Exercise 4.

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

  1. \(\left( {\begin{aligned}{{}{}}3\\0\\{ - 1}\end{aligned}} \right),\left( {\begin{aligned}{{}{}}8\\5\\{ - 6}\end{aligned}} \right)\)

Let \(X\) be the design matrix in Example 2 corresponding to a least-square fit of parabola to data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\). Suppose \({x_1}\), \({x_2}\) and \({x_3}\) are distinct. Explain why there is only one parabola that best, in a least-square sense. (See Exercise 5.)

In Exercises 7–10, let\[W\]be the subspace spanned by the\[{\bf{u}}\]’s, and write y as the sum of a vector in\[W\]and a vector orthogonal to\[W\].

7.\[y = \left[ {\begin{aligned}1\\3\\5\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\3\\{ - 2}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}5\\1\\4\end{aligned}} \right]\]

Given data for a least-squares problem, \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\), the following abbreviations are helpful:

\(\begin{aligned}{l}\sum x = \sum\nolimits_{i = 1}^n {{x_i}} ,{\rm{ }}\sum {{x^2}} = \sum\nolimits_{i = 1}^n {x_i^2} ,\\\sum y = \sum\nolimits_{i = 1}^n {{y_i}} ,{\rm{ }}\sum {xy} = \sum\nolimits_{i = 1}^n {{x_i}{y_i}} \end{aligned}\)

The normal equations for a least-squares line \(y = {\hat \beta _0} + {\hat \beta _1}x\)may be written in the form

\(\begin{aligned}{{\hat \beta }_0} + {{\hat \beta }_1}\sum x = \sum y \\{{\hat \beta }_0}\sum x + {{\hat \beta }_1}\sum {{x^2}} = \sum {xy} {\rm{ (7)}}\end{aligned}\)

16. Use a matrix inverse to solve the system of equations in (7) and thereby obtain formulas for \({\hat \beta _0}\) , and that appear in many statistics texts.

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