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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

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

Short Answer

Expert verified

It is verified that the equality \({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\) by induction.

Step by step solution

01

The Pythagoras Theorem  

The vectors \({\bf{u}}\) and \({\bf{v}}\) are said to beorthogonal such that if \({\left\| {{\bf{u}} + {\bf{v}}} \right\|^2} = {\left\| {\bf{u}} \right\|^2} + {\left\| {\bf{v}} \right\|^2}\).

02

Verify the equality

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

Consider an orthogonal set of nonzero vectors as \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2}} \right\}\) and \({c_1},{c_2}\) as a nonzero scalar. Prove that \(\left\{ {{c_1}{{\bf{v}}_1},{c_2}{{\bf{v}}_2}} \right\}\) is also an orthogonal set. This demonstrates that when the vectors in an orthogonal set are normalized, then the set would still be orthogonal.


If the set \(\left\{ {{{\bf{v}}_1},{{\bf{v}}_2}} \right\}\) is orthonormal and \({\bf{x}} = {c_1}{{\bf{v}}_1} + {c_{12}}{{\bf{v}}_2}\), then the vectors \({c_1}{{\bf{v}}_1}\) and \({c_2}{{\bf{v}}_2}\) are orthogonal.

According to the Pythagoras Theorem and properties of the norm as shown below:

\(\begin{array}{c}{\left\| {\bf{x}} \right\|^2} = {\left\| {{c_1}{{\bf{v}}_1} + {c_2}{{\bf{v}}_2}} \right\|^2}\\ = {\left\| {{c_1}{{\bf{v}}_1}} \right\|^2} + {\left\| {{c_2}{{\bf{v}}_2}} \right\|^2}\\ = {\left( {{c_1}\left\| {{{\bf{v}}_1}} \right\|} \right)^2} + {\left( {{c_2}\left\| {{{\bf{v}}_2}} \right\|} \right)^2}\\ = {\left| {{c_1}} \right|^2} + {\left| {{c_2}} \right|^2}\end{array}\)

Therefore, the stated equality is true for \(p = 2\). Assume that the equality is true for \(p = k\), with \(k \ge 2\).

Consider that \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_{k + 1}}} \right\}\) as an orthonormal set and let \({\bf{x}} = {c_1}{{\bf{v}}_1} + \ldots + {c_k}{{\bf{v}}_k} + {c_{k + 1}}{{\bf{v}}_{k + 1}} = {{\bf{u}}_k} + {c_{k + 1}}{{\bf{v}}_{k + 1}}\), with \({{\bf{u}}_k} = {c_1}{{\bf{v}}_1} + \ldots + {c_k}{{\bf{v}}_k}\).

It is observed that \({{\bf{u}}_k}\) and \({c_{k + 1}}{{\bf{v}}_{k + 1}}\) is orthogonal since \({{\bf{v}}_j} \cdot {{\bf{v}}_{k + 1}} = 0\) for \(j = 1, \ldots ,k\). According to the Pythagoras Theorem and the assumption that the claimed equality is true for \(k\) and since \({\left\| {{c_{k + 1}}{{\bf{v}}_{k + 1}}} \right\|^2} = {\left| {{c_{k + 1}}} \right|^2}{\left\| {{{\bf{v}}_{k + 1}}} \right\|^2} = \left| {{c_{k + 1}}} \right|\)

\(\begin{array}{c}{\left\| {\bf{x}} \right\|^2} = {\left\| {{{\bf{u}}_k} + {c_{k + 1}}{{\bf{v}}_{k + 1}}} \right\|^2}\\ = {\left\| {{{\bf{u}}_k}} \right\|^2} + {\left\| {{c_{k + 1}}{{\bf{v}}_{k + 1}}} \right\|^2}\\ = {\left| {{c_1}} \right|^2} + \ldots + {\left| {{c_{k + 1}}} \right|^2}\end{array}\)

Therefore, the equality holds for \(p = k\) indicates that it's true for \(p = k + 1\). According to the principle of induction, equality holds for all integers \(p \ge 2\).

Hence, it is verified that the equality \({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\) by induction.

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Most popular questions from this chapter

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.

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.

6. \(\left( {\begin{aligned}{{}}3\\{ - 1}\\2\\{ - 1}\end{aligned}} \right),\left( {\begin{aligned}{{}}{ - 5}\\9\\{ - 9}\\3\end{aligned}} \right)\)

Show that if \(U\) is an orthogonal matrix, then any real eigenvalue of \(U\) must be \( \pm 1\).

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\[W\].

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

Compute the quantities in Exercises 1-8 using the vectors

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

4. \(\frac{1}{{{\mathop{\rm u}\nolimits} \cdot {\mathop{\rm u}\nolimits} }}{\mathop{\rm u}\nolimits} \)

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