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Let \({{\bf{P}}_{\bf{3}}}\) have the inner product as in Exercise 9, with \({p_{\bf{0}}}\), \({p_{\bf{1}}}\) and qthe polynomials described there. Find the best approximation to \(p\left( t \right) = {t^{\bf{3}}}\) by polynomials in \({\bf{Span}}\left\{ {{p_{\bf{0}}},{p_{\bf{1}}},q} \right\}\).

Short Answer

Expert verified

The best approximation is \(\frac{{41}}{5}t\).

Step by step solution

01

Find the values of polynomials

The polynomials at \({t_0} = - 3\), \({t_1} = - 1\), \({t_2} = 1\), and \({t_3} = 3\).

\(\begin{aligned}p\left( {{t_0}} \right) &= p\left( { - 3} \right) = - 27\\{p_0}\left( {{t_0}} \right) &= {p_0}\left( { - 3} \right) = 1\\{p_1}\left( {{t_0}} \right) &= {p_1}\left( { - 3} \right) = - 3\\q\left( {{t_0}} \right) &= q\left( { - 3} \right) = 4\end{aligned}\)

And,

\(\begin{aligned}p\left( {{t_1}} \right) &= p\left( 1 \right) = - 1\\{p_0}\left( {{t_1}} \right) &= {p_0}\left( { - 1} \right) = 1\\{p_1}\left( {{t_1}} \right) &= {p_1}\left( { - 1} \right) = - 1\\q\left( {{t_1}} \right) &= q\left( { - 1} \right) = - 4\end{aligned}\)

And,

\(\begin{aligned}p\left( {{t_2}} \right) &= p\left( 1 \right) = 1\\{p_0}\left( {{t_2}} \right) &= {p_0}\left( 1 \right) = 1\\{p_1}\left( {{t_2}} \right) &= {p_1}\left( 1 \right) = 1\\q\left( {{t_2}} \right) &= q\left( 1 \right) = - 4\end{aligned}\)

And,

\(\begin{aligned}p\left( {{t_3}} \right) &= p\left( 3 \right) = 27\\{p_0}\left( {{t_3}} \right) &= 1\\{p_1}\left( {{t_3}} \right) = {p_1}\left( 3 \right) &= 3\\q\left( {{t_3}} \right) = q\left( 3 \right) &= 4\end{aligned}\)

02

Find the inner products

Find the inner product \(\left\langle {p,{p_0}} \right\rangle \).

\(\begin{aligned}\left\langle {p,{p_0}} \right\rangle &= p\left( { - 3} \right){p_0}\left( { - 3} \right) + p\left( { - 1} \right){p_0}\left( { - 1} \right) + p\left( 1 \right){p_0}\left( 1 \right) + p\left( 3 \right){p_0}\left( 3 \right)\\ &= \left( { - 27} \right)\left( 1 \right) + \left( { - 1} \right)\left( 1 \right) + \left( 1 \right)\left( 1 \right) + \left( {27} \right)\left( 1 \right)\\ &= 0\end{aligned}\)

Find the inner product \(\left\langle {p,q} \right\rangle \).

\(\begin{aligned}\left\langle {p,q} \right\rangle &= p\left( { - 3} \right)q\left( { - 3} \right) + p\left( { - 1} \right)q\left( { - 1} \right) + p\left( 1 \right)q\left( 1 \right) + p\left( 3 \right)q\left( 3 \right)\\ &= \left( { - 27} \right)\left( 4 \right) + \left( 1 \right)\left( { - 4} \right) + \left( 1 \right)\left( { - 4} \right) + \left( {27} \right)\left( 4 \right)\\ &= 0\end{aligned}\)

Find the inner product \(\left\langle {{p_0},{p_0}} \right\rangle \).

\(\begin{aligned}\left\langle {{p_0},{p_0}} \right\rangle &= {p_0}\left( { - 3} \right){p_0}\left( { - 3} \right) + {p_0}\left( { - 1} \right){p_0}\left( { - 1} \right) + {p_0}\left( 1 \right){p_0}\left( 1 \right) + {p_0}\left( 3 \right){p_1}\left( 3 \right)\\ &= 1 + 1 + 1 + 1\\ &= 4\end{aligned}\)

Find the inner product \(\left\langle {{p_1},{p_1}} \right\rangle \).

\(\begin{aligned}\left\langle {{p_1},{p_1}} \right\rangle &= {p_1}\left( { - 3} \right){p_1}\left( { - 3} \right) + {p_1}\left( { - 1} \right){p_1}\left( { - 1} \right) + {p_1}\left( 1 \right){p_1}\left( 1 \right) + {p_1}\left( 3 \right){p_1}\left( 3 \right)\\ &= \left( { - 3} \right)\left( { - 3} \right) + \left( { - 1} \right)\left( { - 1} \right) + \left( 1 \right)\left( 1 \right) + \left( 3 \right)\left( 3 \right)\\ &= 20\end{aligned}\)

Find the inner product \(\left\langle {q,q} \right\rangle \).

\(\begin{aligned}\left\langle {q,q} \right\rangle &= q\left( { - 3} \right)q\left( { - 3} \right) + q\left( { - 1} \right)q\left( { - 1} \right) + q\left( 1 \right)q\left( 1 \right) + q\left( 3 \right)q\left( 3 \right)\\ &= \left( 4 \right)\left( 4 \right) + \left( { - 4} \right)\left( { - 4} \right) + \left( { - 4} \right)\left( { - 4} \right) + \left( 4 \right)\left( 4 \right)\\ &= 64\end{aligned}\)

03

Find the best approximation

The best approximation can be done as:

\(\begin{aligned}\widehat {{p_2}} &= \frac{{\left\langle {p,{p_0}} \right\rangle }}{{\left\langle {{p_0},{p_0}} \right\rangle }}{p_0} + \frac{{\left\langle {p,{p_1}} \right\rangle }}{{\left\langle {{p_1},{p_1}} \right\rangle }}{p_1} + \frac{{\left\langle {p,{q_1}} \right\rangle }}{{\left\langle {{p_1},{p_1}} \right\rangle }}{p_1}\\ &= \frac{{20}}{4}\left( 1 \right) + \frac{{164}}{{20}}\left( t \right) + \frac{0}{{64}}\left( {{t^2} - 5} \right)\\ &= \frac{{41}}{5}t\end{aligned}\)

Thus, the best approximation is \(\frac{{41}}{5}t\).

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Most popular questions from this chapter

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

[M] Let \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) be the fourth-order and fifth order Fourier approximations in \(C\left[ {{\bf{0}},{\bf{2}}\pi } \right]\) to the square wave function in Exercise 10. Produce separate graphs of \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) on the interval \(\left[ {{\bf{0}},{\bf{2}}\pi } \right]\), and produce graph of \({f_{\bf{5}}}\) on \(\left[ { - {\bf{2}}\pi ,{\bf{2}}\pi } \right]\).

In Exercises 9-12, find (a) the orthogonal projection of b onto \({\bf{Col}}A\) and (b) a least-squares solution of \(A{\bf{x}} = {\bf{b}}\).

10. \(A = \left[ {\begin{aligned}{{}{}}{\bf{1}}&{\bf{2}}\\{ - {\bf{1}}}&{\bf{4}}\\{\bf{1}}&{\bf{2}}\end{aligned}} \right]\), \({\bf{b}} = \left[ {\begin{aligned}{{}{}}{\bf{3}}\\{ - {\bf{1}}}\\{\bf{5}}\end{aligned}} \right]\)

Let \(U\) be an \(n \times n\) orthogonal matrix. Show that if \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_n}} \right\}\) is an orthonormal basis for \({\mathbb{R}^n}\), then so is \(\left\{ {U{{\bf{v}}_1}, \ldots ,U{{\bf{v}}_n}} \right\}\).

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.

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