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Question: In parts (a)-(d), let U be the matrix formed by normalizing each column of matrix A in Exercise 35.

  1. Compute \({U^T}U\) and \(U{U^T}\). How do they differ?
  2. Generate a random vector y in\({\mathbb{R}^{\bf{8}}}\), and compute\({\bf{p}} = U{U^T}{\bf{y}}\)and\({\bf{z}} = {\bf{y}} - {\bf{p}}\). Explain why p is in col A. Verfiy that z is orthogonal to p.
  3. Verfiy that z is orthogonal to each column of U.
  4. Notice that \({\bf{y}} = {\bf{p}} + {\bf{z}}\), with p in Col A. Explain why z is in \({\left( {{\bf{Col}}A} \right)^ \bot }\). (The significance of this decomposition of y will be explained in the next section.)

Short Answer

Expert verified
  1. The matrices are \({U^T}U = \left( {\begin{array}{*{20}{c}}{1.0000}&0&0&0\\0&{1.0000}&0&0\\0&0&{1.0000}&0\\0&0&0&{1.0000}\end{array}} \right)\), and\(U{U^T} = \left( {\begin{array}{*{20}{c}}{0.8200}&0&{ - 0.2000}&{0.0800}\\0&{0.4200}&{0.2400}&0\\{ - 0.2000}&{0.2400}&{0.5800}&{0.2000}\\{0.0800}&0&{0.2000}&{0.8200}\\{0.0600}&{ - 0.2000}&0&{0.2400}\\{0.2000}&{0.0600}&{0.3200}&{ - 0.2000}\\{0.2400}&{0.2000}&0&{0.0600}\\0&{ - 0.3200}&{0.0600}&0\end{array}} \right)\). The matrix \({U^T}U\) is of order \(4 \times 4\) , and the matrix \(U{U^T}\) is of order \(8 \times 8\).
  1. It is verified that z and p are orthogonal to each other.
  2. It is verified that z is orthogonal to each column of U.
  3. z is in the \({\left( {{\rm{Col}}A} \right)^ \bot }\) because the product of vectors is zero.

Step by step solution

01

Find the answer for part (a)

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

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

Find the matrix \(U\).

\( > > U = \frac{A}{{10}};\)

The matrix Uis:

\(U = \left( {\begin{array}{*{20}{c}}{ - 0.6000}&{ - 0.3000}&{0.6000}&{0.1000}\\{ - 0.1000}&{0.2000}&{0.1000}&{ - 0.6000}\\{0.3000}&{0.6000}&{0.3000}&{ - 0.2000}\\{0.6000}&{ - 0.3000}&{0.6000}&{ - 0.1000}\\{0.2000}&{ - 0.1000}&{0.2000}&{0.3000}\\{ - 0.3000}&{0.6000}&{0.3000}&{0.2000}\\{ - 0.2000}&{ - 0.1000}&{0.2000}&{ - 0.3000}\\{0.1000}&{0.2000}&{0.1000}&{0.6000}\end{array}} \right)\)

Compute the product \({U^T}U\) and \(U{U^T}\).

\(\begin{array}{c} > > P\_1 = U'*U\\ > > P\_2 = U*U'\end{array}\)

The product \({U^T}U\) and \(U{U^T}\) is shown below:

\({U^T}U = \left( {\begin{array}{*{20}{c}}{1.0000}&0&0&0\\0&{1.0000}&0&0\\0&0&{1.0000}&0\\0&0&0&{1.0000}\end{array}} \right)\)

And,

\(U{U^T} = \left( {\begin{array}{*{20}{c}}{0.8200}&0&{ - 0.2000}&{0.0800}\\0&{0.4200}&{0.2400}&0\\{ - 0.2000}&{0.2400}&{0.5800}&{0.2000}\\{0.0800}&0&{0.2000}&{0.8200}\\{0.0600}&{ - 0.2000}&0&{0.2400}\\{0.2000}&{0.0600}&{0.3200}&{ - 0.2000}\\{0.2400}&{0.2000}&0&{0.0600}\\0&{ - 0.3200}&{0.0600}&0\end{array}} \right)\)

The matrix \({U^T}U\) is of order \(4 \times 4\) , and the matrix \(U{U^T}\) is of order \(8 \times 8\).

02

Find an answer for part (b)

Consider the following column vector \(y = {\left( {\begin{array}{*{20}{c}}1&1&1&1&1&1&1&1\end{array}} \right)^T}\).

Use the following command in the MATLAB to obtain the random vector:

\(\begin{array}{l} > > {\bf{y}} = {\rm{ones}}\left( {1,8} \right)\\ > > {\rm{p}} = {\rm{U}}*{\rm{U}}*{\rm{y}}\\ > > {\rm{z}} = {\rm{y}} - {\rm{p}}\end{array}\)

So, the vector z is shown below:

\(z = \left( {\begin{array}{*{20}{c}}{ - 0.2000}\\{0.6000}\\{ - 0.2000}\\{ - 0.2000}\\{0.6000}\\{ - 0.2000}\\{0.6000}\\{0.6000}\end{array}} \right)\)

As \({\bf{p}} = U{U^T}{\bf{y}}\), therefore, pis the column space of U.

Find the product of z and p.

\( > > {\rm{z}}*{\bf{p}}\)

The product \({\rm{z}}*{\rm{p}}\) gives the result as zero. Therefore z and p are orthogonal to each other.

03

Find an answer for part (c)

Multiply each column of U with zby using the following MATLAB commands:

\(\begin{array}{l} > > {\rm{z}}*{\rm{U}}\left( {;1} \right)\\ > > {\rm{z}}*{\rm{U}}\left( {;2} \right)\\ > > {\rm{z}}*{\rm{U}}\left( {;3} \right)\\ > > {\rm{z}}*{\rm{U}}\left( {;4} \right)\end{array}\)

The result of all the products is zero. Therefore z is orthogonal to each column of U.

04

Find an answer for part (d)

As it is proved in step 3, that zis orthogonal to each column of A. Therefore z must be orthogonal to every vector in the column space of A.

Thus, z is in the column space of A.

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Most popular questions from this chapter

Find the distance between \({\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{10}\\{ - 3}\end{aligned}} \right)\) and \({\mathop{\rm y}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\{ - 5}\end{aligned}} \right)\).

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.

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

9. \(\left[ {\begin{aligned}{{}{}}3&{ - 5}&1\\1&1&1\\{ - 1}&5&{ - 2}\\3&{ - 7}&8\end{aligned}} \right]\)

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

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) \({\left\| {X\hat \beta } \right\|^2}\)โ€”the sum of the squares of the โ€œregression term.โ€ Denote this number by \(SS\left( R \right)\).

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

(iii) \({\left\| {\bf{y}} \right\|^2}\)โ€”the โ€œtotalโ€ sum of the squares of the -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 \(y\)-values.

20. Show that \({\left\| {X\hat \beta } \right\|^2} = {\hat \beta ^T}{X^T}{\bf{y}}\). (Hint: Rewrite the left side and use the fact that \(\hat \beta \) satisfies the normal equations.) This formula for is used in statistics. From this and from Exercise 19, obtain the standard formula for \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

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