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

15.\(\left\langle {{\rm{u,}}\,c{\rm{v}}} \right\rangle = c\left\langle {{\rm{u,}}\,{\rm{v}}} \right\rangle \) for all scalars \(c\).

Short Answer

Expert verified

The statement \(\left\langle {{\rm{u}},c{\rm{v}}} \right\rangle = c\left\langle {{\rm{u}},{\rm{v}}} \right\rangle \) is verified.

Step by step solution

01

Apply axiom 1

According to axiom 1, if\({\bf{u}}\)and\({\rm{v}}\)bepair 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:

\(\left\langle {{\rm{u}},c{\rm{v}}} \right\rangle = \left\langle {c{\rm{v}},{\rm{u}}} \right\rangle \)

02

Apply axiom 3

According to axiom 3, if\({\bf{u}}\)and\({\rm{v}}\)bepair 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,\(\left\langle {c{\rm{u}},{\rm{v}}} \right\rangle = c\left\langle {{\rm{u}},{\rm{v}}} \right\rangle \)for all \({\bf{u}}\),\({\rm{v}}\),\({\rm{w}}\)and scalars\(c\).

Apply axiom 3 to the resulting equation, as follows:

\(\begin{aligned}\left\langle {{\rm{u}},c{\rm{v}}} \right\rangle = \left\langle {c{\rm{v}},{\rm{u}}} \right\rangle \\ = c\left\langle {{\rm{v}},{\rm{u}}} \right\rangle \end{aligned}\)

Again, apply axiom 1 to the resulting equation:

\(\begin{aligned}\left\langle {{\rm{u}},c{\rm{v}}} \right\rangle = c\left\langle {{\rm{v}},{\rm{u}}} \right\rangle \\ = c\left\langle {{\rm{u}},{\rm{v}}} \right\rangle \end{aligned}\)

Thus, the statement \(\left\langle {{\rm{u}},c{\rm{v}}} \right\rangle = c\left\langle {{\rm{u}},{\rm{v}}} \right\rangle \) is verified.

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Most popular questions from this chapter

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}4\\6\end{aligned}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{aligned}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}6\\{ - 2}\\3\end{aligned}} \right)\)

6. \(\left( {\frac{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm w}\nolimits} }}{{{\mathop{\rm x}\nolimits} \cdot {\mathop{\rm x}\nolimits} }}} \right){\mathop{\rm x}\nolimits} \)

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

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

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 11 and 12, find the closest point to \[{\bf{y}}\] in the subspace \[W\] spanned by \[{{\bf{v}}_1}\], and \[{{\bf{v}}_2}\].

12. \[y = \left[ {\begin{aligned}3\\{ - 1}\\1\\{13}\end{aligned}} \right]\], \[{{\bf{v}}_1} = \left[ {\begin{aligned}1\\{ - 2}\\{ - 1}\\2\end{aligned}} \right]\], \[{{\bf{v}}_2} = \left[ {\begin{aligned}{ - 4}\\1\\0\\3\end{aligned}} \right]\]

In Exercises 1-4, find a least-sqaures solution of \(A{\bf{x}} = {\bf{b}}\) by (a) constructing a normal equations for \({\bf{\hat x}}\) and (b) solving for \({\bf{\hat x}}\).

4. \(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{\bf{3}}\\{\bf{1}}&{ - {\bf{1}}}\\{\bf{1}}&{\bf{1}}\end{aligned}} \right)\), \({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{5}}\\{\bf{1}}\\{\bf{0}}\end{aligned}} \right)\)

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