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Use the inner product axioms and other results of this section to verify the statements in Exercises 15–18.

\(\left\langle {{\rm{u,v}}} \right\rangle = \frac{1}{4}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} - \frac{1}{4}{\left\| {{\rm{u}} - {\rm{v}}} \right\|^2}\).

Short Answer

Expert verified

The statement \(\left\langle {{\rm{u,v}}} \right\rangle = \frac{1}{4}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} - \frac{1}{4}{\left\| {{\rm{u}} - {\rm{v}}} \right\|^2}\) is verified.

Step by step solution

01

Apply axiom 2

According to axiom 2, if \({\bf{u}}\) and \({\rm{v}}\) be pair of vectors in a vector space \(V\), then, the inner product on \(V\), relates a real number \(\left\langle {{\bf{u}},{\rm{v}}} \right\rangle \)and satisfies the axiom, \(\left\langle {{\bf{u}} + {\rm{v,}}\,{\rm{w}}} \right\rangle = \left\langle {{\rm{u,w}}} \right\rangle + \left\langle {{\rm{v,w}}} \right\rangle \) for all \({\bf{u}}\), \({\rm{v}}\), \({\rm{w}}\)and scalars \(c\).

Apply axiom 2 to the left side of the given equation, as follows:

\(\begin{aligned}{}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} &= \left\langle {{\rm{u}} + {\rm{v,}}\,{\rm{u}} + {\rm{v}}} \right\rangle \\ &= \left\langle {{\rm{u,}}\,{\rm{u}} + {\rm{v}}} \right\rangle + \left\langle {{\rm{v,}}\,{\rm{u}} + {\rm{v}}} \right\rangle \\ &= \left\langle {{\rm{u,}}\,{\rm{u}}} \right\rangle + \left\langle {{\rm{u,v}}} \right\rangle + \left\langle {{\rm{v,}}\,{\rm{u}}} \right\rangle + \left\langle {{\rm{v,}}\,{\rm{v}}} \right\rangle \end{aligned}\)

02

Apply axiom 1

According to axiom 1, if \({\bf{u}}\) and \({\rm{v}}\) be pair of vectors in a vector space \(V\), then, the inner product on \(V\), relates a real number \(\left\langle {{\bf{u}},{\rm{v}}} \right\rangle \)and satisfies the axiom, \(\left\langle {{\bf{u}},{\rm{v}}} \right\rangle = \left\langle {{\rm{v}},{\rm{u}}} \right\rangle \)for all \({\bf{u}}\), \({\rm{v}}\), \({\rm{w}}\)and scalars \(c\).

Apply axiom 1 to the left side of the given equation, as follows:

\(\begin{aligned}{}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} &= \left\langle {{\rm{u,}}\,{\rm{u}}} \right\rangle + \left\langle {{\rm{u,v}}} \right\rangle + \left\langle {{\rm{u,v}}} \right\rangle + \left\langle {{\rm{v,}}\,{\rm{v}}} \right\rangle \\ &= \left\langle {{\rm{u,}}\,{\rm{u}}} \right\rangle + {\rm{2}}\left\langle {{\rm{u,v}}} \right\rangle + \left\langle {{\rm{v,}}\,{\rm{v}}} \right\rangle \\ &= {\left\| {\rm{u}} \right\|^2} + 2\left\langle {{\rm{u,v}}} \right\rangle + {\left\| {\rm{v}} \right\|^2}\end{aligned}\)

Following the same steps, we can write that \({\left\| {{\rm{u}} - {\rm{v}}} \right\|^2} = {\left\| {\rm{u}} \right\|^2} - 2\left\langle {{\rm{u,v}}} \right\rangle + {\left\| {\rm{v}} \right\|^2}\).

03

Subtract the two results

Subtract the obtain equation for\({\left\| {{\rm{u}} + {\rm{v}}} \right\|^2}\)and\({\left\| {{\rm{u}} - {\rm{v}}} \right\|^2}\), as follows:

\(\begin{aligned}{}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} - {\left\| {{\rm{u}} - {\rm{v}}} \right\|^2} &= \left( {{{\left\| {\rm{u}} \right\|}^2} + 2\left\langle {{\rm{u,v}}} \right\rangle + {{\left\| {\rm{v}} \right\|}^2}} \right) - \left( {{{\left\| {\rm{u}} \right\|}^2} - 2\left\langle {{\rm{u,v}}} \right\rangle + {{\left\| {\rm{v}} \right\|}^2}} \right)\\ &= 4\left\langle {{\rm{u,v}}} \right\rangle \end{aligned}\)

Now divide the resulting equation by 4 and simplify as shown below:

\(\begin{aligned}{}\frac{{{{\left\| {{\rm{u}} + {\rm{v}}} \right\|}^2} - {{\left\| {{\rm{u}} - {\rm{v}}} \right\|}^2}}}{4} &= \frac{{4\left\langle {{\rm{u,v}}} \right\rangle }}{4}\\\left\langle {{\rm{u,v}}} \right\rangle &= \frac{1}{4}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} - \frac{1}{4}{\left\| {{\rm{u}} - {\rm{v}}} \right\|^2}\end{aligned}\)

Thus, the statement\(\left\langle {{\rm{u,v}}} \right\rangle = \frac{1}{4}{\left\| {{\rm{u}} + {\rm{v}}} \right\|^2} - \frac{1}{4}{\left\| {{\rm{u}} - {\rm{v}}} \right\|^2}\) is verified.

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Most popular questions from this chapter

In Exercises 9-12, find a unit vector in the direction of the given vector.

9. \(\left( {\begin{aligned}{*{20}{c}}{ - 30}\\{40}\end{aligned}} \right)\)

Let \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) be an orthonormal set. Verify the following equality by induction, beginning with \(p = 2\). If \({\bf{x}} = {c_1}{{\bf{v}}_1} + \ldots + {c_p}{{\bf{v}}_p}\), then

\({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + {\left| {{c_2}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\)

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

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.

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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