Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

(M) Let V be the space \(C\left( {0,2\pi } \right)\)with the inner product of Example 7. Use the Gram–Schmidt process to create an orthogonal basis for the subspace spanned by \(\left\{ {1,\cos t,{{\cos }^2}t,{{\cos }^3}t} \right\}\). Use a matrix program or computational program to compute the appropriate definite integrals.

Short Answer

Expert verified

The orthogonal basis are \({f_0}\left( t \right) = 1\), \({f_1}\left( t \right) = \cos t\), \({f_2}\left( t \right) = \frac{1}{2}\cos 2t\) and \({f_3}\left( t \right) = \frac{1}{4}\cos 3t\) .

Step by step solution

01

Find the basis

It is given that \(V\) be the space \(C\left( {0,2\pi } \right)\) and an orthogonal basis for the subspace is spanned by \(\left\{ {1,\cos t,{{\cos }^2}t,{{\cos }^3}t} \right\}\).

Let the matrix be defined as \(A = \left( {\begin{array}{*{20}{c}}1\\{\cos t}\\{{{\cos }^2}t}\\{{{\cos }^3}t}\end{array}} \right)\).Use the following steps to find the associated values for the obtained data in MATLAB.

Formulate the matrix A using the commands as:

>>A=(1,cos(t),(cos(t))^2,(cos(t))^3);
>>(m,n) = size(A);

And to find\(QR\)by using the following commands:

>>fori = 1:n
>>v = A(:,i);
>>for j=1:i-1
>>R(j,i) = Q(:,j)'*A(:,i);
>>v = v - R(i,j)*Q(:,j);
>>end
>>R(i,i) = norm(v);
>>Q(:,i) = v/R(i,i);
>>end

By using the matrix program, the new orthogonal polynomials, are obtained as \({f_0}\left( t \right) = 1\), \({f_1}\left( t \right) = \cos t\), \({f_2}\left( t \right) = \frac{1}{2} - {\cos ^2}t\) and \({f_3}\left( t \right) = \frac{3}{4}\cos t - {\cos ^3}t\) .

02

Simplify the polynomials

By using the trigonometric identities, the simplified orthogonal basis is \({f_0}\left( t \right) = 1\), \({f_1}\left( t \right) = \cos t\), \({f_2}\left( t \right) = \frac{1}{2}\cos 2t\) and \({f_3}\left( t \right) = \frac{1}{4}\cos 3t\).

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

In Exercises 5-14, the space is \(C\left[ {0,2\pi } \right]\) with inner product (6).

7. Show that \({\left\| {\cos kt} \right\|^2} = \pi \) and \({\left\| {\sin kt} \right\|^2} = \pi \) for \(k > 0\).

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

11. \(\left( {\begin{aligned}{{}{}}1&2&5\\{ - 1}&1&{ - 4}\\{ - 1}&4&{ - 3}\\1&{ - 4}&7\\1&2&1\end{aligned}} \right)\)

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

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 \(SS\left( R \right)\).

(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 -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 \(SS\left( E \right)\):

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

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

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