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Let \({p_{\bf{0}}}\), \({p_{\bf{1}}}\), and \({p_{\bf{2}}}\)be the orthogonal polynomials described in Example 5, where the inner product on \({{\bf{P}}_{\bf{4}}}\) is given by evaluation at \( - {\bf{2}}\), \( - {\bf{1}}\), 0, 1, and 2. Find the orthogonal projection of \({t^{\bf{3}}}\) onto \({\bf{Span}}\left\{ {{p_{\bf{0}}},{p_{\bf{1}}},{p_{\bf{2}}}} \right\}\).

Short Answer

Expert verified

The orthogonal projection is \(\frac{{17}}{5}t\).

Step by step solution

01

Write the polynomials in vector form

The polynomials can be written as:

\({p_0} = \left( {\begin{aligned}{*{20}}1\\1\\1\\1\\1\end{aligned}} \right)\), \({p_1} = \left( {\begin{aligned}{*{20}}{ - 2}\\{ - 1}\\0\\1\\2\end{aligned}} \right)\), \({p_2} = \left( {\begin{aligned}{*{20}}4\\1\\0\\1\\4\end{aligned}} \right)\)

02

Find the inner products

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

\(\begin{aligned}\left\langle {p,{p_1}} \right\rangle & = \left\langle {{t^3},t} \right\rangle \\ &= p\left( { - 2} \right){p_1}\left( { - 2} \right) + p\left( { - 1} \right){p_1}\left( { - 1} \right) + p\left( 0 \right){p_1}\left( 0 \right) + p\left( 1 \right){p_1}\left( 1 \right) + p\left( 2 \right){p_1}\left( 2 \right)\\ &= {\left( { - 2} \right)^3}\left( { - 2} \right) + {\left( { - 1} \right)^3}\left( { - 1} \right) + 0 + {1^3}\left( 1 \right) + {2^3}\left( 2 \right)\\ &= 34\end{aligned}\)

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

\(\begin{aligned}\left\langle {p,{p_0}} \right\rangle &= \left\langle {{t^3},1} \right\rangle \\ &= p\left( { - 2} \right){p_0}\left( { - 2} \right) + p\left( { - 1} \right){p_0}\left( { - 1} \right) + p\left( 0 \right){p_0}\left( 0 \right) + p\left( 1 \right){p_0}\left( 1 \right) + p\left( 2 \right){p_0}\left( 2 \right)\\ &= {\left( { - 2} \right)^3}\left( 1 \right) + {\left( { - 1} \right)^3}\left( 1 \right) + {\left( 0 \right)^3}\left( 1 \right) + {1^3}\left( 1 \right) + {2^3}\left( 1 \right)\\ &= 0\end{aligned}\)

Find the inner product \(\left\langle {p,{p_2}} \right\rangle \).

\(\begin{aligned}\left\langle {p,{p_2}} \right\rangle &= \left\langle {{t^3},{t^2}} \right\rangle \\ &= p\left( { - 2} \right){p_2}\left( { - 2} \right) + p\left( { - 1} \right){p_2}\left( { - 1} \right) + p\left( 0 \right){p_2}\left( 0 \right) + p\left( 1 \right){p_2}\left( 1 \right) + p\left( 2 \right){p_2}\left( 2 \right)\\ &= {\left( { - 2} \right)^3}{\left( { - 2} \right)^2} + {\left( { - 1} \right)^3}{\left( { - 1} \right)^2} + {\left( 0 \right)^3}{\left( 0 \right)^2} + {\left( 1 \right)^3}{\left( 1 \right)^2} + {\left( 2 \right)^3}{\left( 2 \right)^2}\\ &= 0\end{aligned}\)

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

\(\begin{aligned}\left\langle {{p_0},{p_0}} \right\rangle &= \left\langle {1,1} \right\rangle \\ &= {p_0}\left( { - 2} \right){p_0}\left( { - 2} \right) + {p_0}\left( { - 1} \right){p_0}\left( { - 1} \right) + {p_0}\left( 0 \right){p_0}\left( 0 \right) + {p_0}\left( 1 \right){p_0}\left( 1 \right) + {p_0}\left( 2 \right){p_0}\left( 2 \right)\\ &= \left( 1 \right)\left( 1 \right) + \left( 1 \right)\left( 1 \right) + \left( 1 \right)\left( 1 \right) + \left( 1 \right)\left( 1 \right)\\ &= 5\end{aligned}\)

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

\(\begin{aligned}\left\langle {{p_1},{p_1}} \right\rangle &= \left\langle {t,t} \right\rangle \\ &= {p_1}\left( { - 2} \right){p_1}\left( { - 2} \right) + {p_1}\left( { - 1} \right){p_1}\left( { - 1} \right) + {p_1}\left( 0 \right){p_1}\left( 0 \right) + {p_1}\left( 1 \right){p_1}\left( 1 \right) + {p_1}\left( 2 \right){p_1}\left( 2 \right)\\ &= \left( { - 2} \right)\left( { - 2} \right) + \left( { - 1} \right)\left( { - 1} \right) + \left( 0 \right)\left( 0 \right) + \left( 1 \right)\left( 1 \right) + \left( 2 \right)\left( 2 \right)\\ &= 10\end{aligned}\)

Find the inner product \(\left\langle {{p_2},{p_2}} \right\rangle \).

\(\begin{aligned}\left\langle {{p_2},{p_2}} \right\rangle &= \left\langle {{t^2},{t^2}} \right\rangle \\ &= {p_2}\left( { - 2} \right){p_2}\left( { - 2} \right) + {p_2}\left( { - 1} \right){p_2}\left( { - 1} \right) + {p_2}\left( 0 \right){p_2}\left( 0 \right) + {p_2}\left( 1 \right){p_2}\left( 1 \right) + {p_2}\left( 2 \right){p_2}\left( 2 \right)\\ &= {\left( { - 2} \right)^2}{\left( { - 2} \right)^2} + {\left( { - 1} \right)^2}{\left( { - 1} \right)^2} + {\left( 0 \right)^2}{\left( 0 \right)^2} + {\left( 1 \right)^2}{\left( 1 \right)^2} + {\left( 2 \right)^2}{\left( 2 \right)^2}\\ &= 34\end{aligned}\)

03

Find the orthogonal projection

The orthogonal projection can be calculated as follows:

\(\begin{aligned}\hat p &= \frac{{\left\langle {p,{p_0}} \right\rangle }}{{\left\langle {{p_0},{p_0}} \right\rangle }}{p_0} + \frac{{\left\langle {p,{p_1}} \right\rangle }}{{\left\langle {{p_1},{p_1}} \right\rangle }}{p_1} + \frac{{\left\langle {p,{p_2}} \right\rangle }}{{\left\langle {{p_2},{p_2}} \right\rangle }}{p_2}\\ &= \frac{0}{5}\left( 1 \right) + \frac{{34}}{{10}}t + \frac{0}{{34}}{t^2}\\ &= \frac{{34}}{{10}}t\\ &= \frac{{17}}{5}t\end{aligned}\)

Thus, the orthogonal projection is \(\frac{{17}}{5}t\).

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Most popular questions from this chapter

In Exercises 17 and 18, all vectors and subspaces are in \({\mathbb{R}^n}\). Mark each statement True or False. Justify each answer.

a. If \(W = {\rm{span}}\left\{ {{x_1},{x_2},{x_3}} \right\}\) with \({x_1},{x_2},{x_3}\) linearly independent,

and if \(\left\{ {{v_1},{v_2},{v_3}} \right\}\) is an orthogonal set in \(W\) , then \(\left\{ {{v_1},{v_2},{v_3}} \right\}\) is a basis for \(W\) .

b. If \(x\) is not in a subspace \(W\) , then \(x - {\rm{pro}}{{\rm{j}}_W}x\) is not zero.

c. In a \(QR\) factorization, say \(A = QR\) (when \(A\) has linearly

independent columns), the columns of \(Q\) form an

orthonormal basis for the column space of \(A\).

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

2. \(\left( {\begin{aligned}{{}{}}0\\4\\2\end{aligned}} \right),\left( {\begin{aligned}{{}{}}5\\6\\{ - 7}\end{aligned}} \right)\)

Compute the least-squares error associated with the least square solution found in Exercise 3.

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 .

(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 \(y\)-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.

Suppose \(A = QR\) is a \(QR\) factorization of an \(m \times n\) matrix

A (with linearly independent columns). Partition \(A\) as \(\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right]\), where \({A_1}\) has \(p\) columns. Show how to obtain a \(QR\) factorization of \({A_1}\), and explain why your factorization has the appropriate properties.

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