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Let A be any invertible \(n \times n\) matrix. Show that for u, v in \({\mathbb{R}^n}\), the formula \(\left\langle {{\bf{u}},{\bf{v}}} \right\rangle = \left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{v}}} \right) = {\left( {A{\bf{u}}} \right)^T}\left( {A{\bf{v}}} \right)\) defines an inner product on \({\mathbb{R}^n}\).

Short Answer

Expert verified

The formula \(\left\langle {{\bf{u}},{\bf{v}}} \right\rangle = \left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{v}}} \right) = {\left( {A{\bf{u}}} \right)^T}\left( {A{\bf{v}}} \right)\) defines an inner product on \({\mathbb{R}^n}\).

Step by step solution

01

Check for the first Axiom

Consider the following expression:

\(\begin{aligned}\left\langle {{\bf{u}},{\bf{v}}} \right\rangle &= \left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{v}}} \right)\\ &= \left( {A{\bf{v}}} \right) \cdot \left( {A{\bf{u}}} \right)\\ &= \left\langle {{\bf{v}},{\bf{u}}} \right\rangle \end{aligned}\)

Thus, Axiom (1) is proved.

02

Check for the second Axiom

Consider the following expression:

\(\begin{aligned}\left\langle {{\bf{u}} + {\bf{v}},{\bf{w}}} \right\rangle &= \left[ {A\left( {{\bf{u}} + {\bf{v}}} \right)} \right] \cdot \left( {A{\bf{w}}} \right)\\ &= \left( {A{\bf{u}} + A{\bf{v}}} \right) \cdot \left( {A{\bf{w}}} \right)\\ &= \left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{w}}} \right) + \left( {A{\bf{v}}} \right) \cdot \left( {A{\bf{w}}} \right)\\ &= \left\langle {{\bf{u}},{\bf{w}}} \right\rangle + \left\langle {{\bf{v}},{\bf{w}}} \right\rangle \end{aligned}\)

Thus, Axiom (2) is proved.

03

Check for the third Axiom

Consider the following expression:

\(\begin{aligned}\left\langle {c{\bf{u}},{\bf{v}}} \right\rangle &= \left( {Ac{\bf{u}}} \right) \cdot \left( {A{\bf{v}}} \right)\\ &= c\left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{v}}} \right)\\ &= c\left\langle {{\bf{u}},{\bf{v}}} \right\rangle \end{aligned}\)

Thus, Axiom (3) is proved.

04

Check for the fourth Axiom

Consider the following expression:

\(\begin{aligned}\left\langle {{\bf{u}},{\bf{u}}} \right\rangle &= \left( {A{\bf{u}}} \right) \cdot \left( {A{\bf{u}}} \right)\\ &= {\left\| {A{\bf{u}}} \right\|^2}\end{aligned}\)

Here, \({\left\| {A{\bf{u}}} \right\|^2} \ge 0\), and this quantity is zero if the vector \(A{\bf{u}}\) is a zero vector. It also implies that \({\bf{u}} = 0\), since A is an invertible matrix.

Thus, Axiom (4) is proved.

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Most popular questions from this chapter

Let \(X\) be the design matrix in Example 2 corresponding to a least-square fit of parabola to data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\). Suppose \({x_1}\), \({x_2}\) and \({x_3}\) are distinct. Explain why there is only one parabola that best, in a least-square sense. (See Exercise 5.)

In exercises 1-6, determine which sets of vectors are orthogonal.

\(\left[ {\begin{align}{ 2}\\{ - 7}\\{-1}\end{align}} \right]\), \(\left[ {\begin{align}{ - 6}\\{ - 3}\\9\end{align}} \right]\), \(\left[ {\begin{align}{ 3}\\{ 1}\\{-1}\end{align}} \right]\)

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.

Find a \(QR\) factorization of the matrix in Exercise 11.

Let \({\mathbb{R}^{\bf{2}}}\) have the inner product of Example 1. Show that the Cauchy-Schwarz inequality holds for \({\bf{x}} = \left( {{\bf{3}}, - {\bf{2}}} \right)\) and \({\bf{y}} = \left( { - {\bf{2}},{\bf{1}}} \right)\). (Suggestion: Study \({\left| {\left\langle {{\bf{x}},{\bf{y}}} \right\rangle } \right|^{\bf{2}}}\).)

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