Question 26: Let U be the matrix in Exercise 25. Find the distance from \({\mathop{\rm b}\nolimits} = \left( {1,1,1,1, - 1, - 1, - 1, - 1} \right)\) to Col U.

Short Answer

Expert verified

The distance from yto \({\mathop{\rm Col}\nolimits} U\) is \(2.1166\).

Step by step solution

01

The Best Approximation Theorem

Consider \(W\) as a subspace of \({\mathbb{R}^n}\), and assume that y as any vector in \({\mathbb{R}^n}\) and \(\widehat {\bf{y}}\) as the orthogonal projection of y onto \(W\). Then, theclosest pointin \(W\) to \({\bf{y}}\) is \(\widehat {\bf{y}}\), such that \(\left\| {{\bf{y}} - \widehat {\bf{y}}} \right\| < \left\| {{\bf{y}} - {\bf{v}}} \right\|\) for every \({\bf{v}}\) in \(W\) distinct from \(\widehat {\bf{y}}\).

02

Determine the distance from b to Col U

The distance from b to Col U is given by \(\left\| {{\bf{b}} - \widehat {\bf{b}}} \right\|\), with \(\widehat {\bf{b}} = U{U^T}{\bf{b}}\) as:

Consider that \(U = \left[ {\begin{array}{*{20}{c}}{ - 6}&{ - 3}&6&1\\{ - 1}&2&1&{ - 6}\\3&6&3&{ - 2}\\6&{ - 3}&6&{ - 1}\\2&{ - 1}&2&3\\{ - 3}&6&3&2\\{ - 2}&{ - 1}&2&{ - 3}\\1&2&1&6\end{array}} \right]\) as in Exercise 25.

Use the MATLAB code to compute \(\widehat {\bf{b}} = U{U^T}{\bf{b}}\) as shown below:

\(\begin{array}{c} > > U = \left[ \begin{array}{l} - 6\,\,\, - 3\,\,\,6\,\,\,1;\, - 1\,\,\,2\,\,\,\,1\,\,\, - 6;\,3\,\,\,6\,\,\,3\,\,\, - 2;\,\,6\,\,\, - 3\,\,\,6\,\,\, - 1;\,\\2\,\,\, - 1\,\,\,2\,\,\,\,3;\,\, - 3\,\,\,\,6\,\,\,3\,\,\,2;\,\, - 2\,\,\, - 1\,\,\,2\,\,\, - 3;1\,\,\,2\,\,\,1\,\,\,6\end{array} \right]\\ > > {\bf{b}} = \left[ {1\,;\,1;\,\,\,1\,;\,\,1\,;\,\, - 1;\,\,\, - 1;\,\, - 1;\,\, - 1} \right]\\ > > \widehat {\bf{b}} = U * U' * {\bf{b}}\end{array}\)

\(\widehat {\bf{b}} = \left[ {\begin{array}{*{20}{c}}{.2}\\{.92}\\{.44}\\1\\{ - .2}\\{ - .44}\\{.6}\\{ - .92}\end{array}} \right]\)

Use the MATLAB code to compute \({\bf{b}} - \widehat {\bf{b}}\) as shown below:

\(\begin{array}{l} > > {\bf{b}} = \left[ {1\,;\,1;\,\,\,1\,;\,\,1\,;\,\, - 1;\,\,\, - 1;\,\, - 1;\,\, - 1} \right]\\ > > \widehat {\bf{b}} = \left[ {.2;\,\,\,.92;\,\,.44;\,\,\,1;\,\, - .2;\,\, - .44;\,\,.6;\, - .92} \right]\\ > > {\bf{b}} - \widehat {\bf{b}}\end{array}\)

\({\bf{b}} - \widehat {\bf{b}} = \left[ {\begin{array}{*{20}{c}}{.8}\\{.08}\\{.56}\\0\\{ - .8}\\{ - .56}\\{ - 1.6}\\{ - .08}\end{array}} \right]\)

Compute \(\left\| {{\bf{b}} - \widehat {\bf{b}}} \right\|\) as shown below:

\(\begin{array}{c}\left\| {{\bf{b}} - \widehat {\bf{b}}} \right\| = \sqrt {{{\left( {.8} \right)}^2} + {{\left( {.08} \right)}^2} + {{\left( {.56} \right)}^2} + \left( 0 \right) + {{\left( { - .8} \right)}^2} + {{\left( { - .56} \right)}^2} + {{\left( { - 1.6} \right)}^2} + {{\left( { - .08} \right)}^2}} \\ = \frac{{\sqrt {112} }}{5}\end{array}\)

It implies that \(2.1166\) to four decimal places.

Thus, the distance from yto \({\mathop{\rm Col}\nolimits} U\) is \(2.1166\).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

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) \({\left\| {X\hat \beta } \right\|^2}\)—the sum of the squares of the “regression term.” Denote this number by .

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

(iii) \({\left\| {\bf{y}} \right\|^2}\)—the “total” 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 9-12, find a unit vector in the direction of the given vector.

10. \(\left( {\begin{aligned}{*{20}{c}}{ - 6}\\4\\{ - 3}\end{aligned}} \right)\)

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

\(\left[ {\begin{array}{*{20}{c}}5\\{ - 4}\\0\\3\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}{ - 4}\\1\\{ - 3}\\8\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\3\\5\\{ - 1}\end{array}} \right]\)

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

Show that if an \(n \times n\) matrix satisfies \(\left( {U{\bf{x}}} \right) \cdot \left( {U{\bf{y}}} \right) = {\bf{x}} \cdot {\bf{y}}\) for all x and y in \({\mathbb{R}^n}\), then \(U\) is an orthogonal matrix.

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free