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(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 \({f_3}\left( t \right) = \frac{1}{4}\cos 3t\).

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Most popular questions from this chapter

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

10. \(\left( {\begin{aligned}{{}{}}{ - 1} & 6 & 6 \\ 3 & { - 8}&3\\1&{ - 2}&6\\1&{ - 4}&{ - 3}\end{aligned}} \right)\)

In Exercises 1-4, find the equation \(y = {\beta _0} + {\beta _1}x\) of the least-square line that best fits the given data points.

4. \(\left( {2,3} \right),\left( {3,2} \right),\left( {5,1} \right),\left( {6,0} \right)\)

For a matrix program, the Gram–Schmidt process worksbetter with orthonormal vectors. Starting with \({x_1},......,{x_p}\) asin Theorem 11, let \(A = \left\{ {{x_1},......,{x_p}} \right\}\) . Suppose \(Q\) is an\(n \times k\)matrix whose columns form an orthonormal basis for

the subspace \({W_k}\) spanned by the first \(k\) columns of A. Thenfor \(x\) in \({\mathbb{R}^n}\), \(Q{Q^T}x\) is the orthogonal projection of x onto \({W_k}\) (Theorem 10 in Section 6.3). If \({x_{k + 1}}\) is the next column of \(A\),then equation (2) in the proof of Theorem 11 becomes

\({v_{k + 1}} = {x_{k + 1}} - Q\left( {{Q^T}T {x_{k + 1}}} \right)\)

(The parentheses above reduce the number of arithmeticoperations.) Let \({u_{k + 1}} = \frac{{{v_{k + 1}}}}{{\left\| {{v_{k + 1}}} \right\|}}\). The new \(Q\) for thenext step is \(\left( {\begin{aligned}{{}{}}Q&{{u_{k + 1}}}\end{aligned}} \right)\). Use this procedure to compute the\(QR\)factorization of the matrix in Exercise 24. Write thekeystrokes or commands you use.

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

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