Let \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) be an orthonormal set in \({\mathbb{R}^n}\). Verify the following inequality, called Bessel’s inequality, which is true for each x in \({\mathbb{R}^n}\):

\({\left\| {\bf{x}} \right\|^2} \ge {\left| {{\bf{x}} \cdot {{\bf{v}}_1}} \right|^2} + {\left| {{\bf{x}} \cdot {{\bf{v}}_2}} \right|^2} + \ldots + {\left| {{\bf{x}} \cdot {{\bf{v}}_p}} \right|^2}\)

Short Answer

Expert verified

It is verified that Bessel’s equality \({\left\| {\widehat {\bf{x}}} \right\|^2} = {\left| {{\bf{x}} \cdot {{\bf{v}}_1}} \right|^2} + \ldots + {\left| {{\bf{x}} \cdot {{\bf{v}}_p}} \right|^2}\) is true for all \({\bf{x}}\) in \({\mathbb{R}^n}\).

Step by step solution

01

Statement of Theorem 10

When the orthonormal basisfor a subspace \(W\) of \({\mathbb{R}^n}\) is \(\left\{ {{{\bf{u}}_1}, \ldots ,{{\bf{u}}_p}} \right\}\),then;

\({{\mathop{\rm proj}\nolimits} _W}{\bf{y}} = \left( {{\bf{y}} \cdot {{\bf{u}}_1}} \right){{\bf{u}}_1} + \left( {{\bf{y}} \cdot {{\bf{u}}_2}} \right){{\bf{u}}_2} + \cdots + \left( {{\bf{y}} \cdot {{\bf{u}}_p}} \right){{\bf{u}}_p}\) … (4)

If \(U = \left[ {\begin{array}{*{20}{c}}{{{\bf{u}}_1}}&{{{\bf{u}}_2}}& \cdots &{{{\bf{u}}_p}}\end{array}} \right]\), then \({{\mathop{\rm proj}\nolimits} _W}{\bf{y}} = U{U^T}{\bf{y}}\) for every \({\bf{y}}\) in \({\mathbb{R}^n}\).

02

Verify Bessel’s inequality

Consider \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) as an orthonormal set in \({\mathbb{R}^n}\). If \({\bf{x}} = {c_1}{{\bf{v}}_1} + \cdots + {c_p}{{\bf{v}}_p}\), then \({\left\| {\bf{x}} \right\|^2} = {\left| {{c_1}} \right|^2} + \ldots + {\left| {{c_p}} \right|^2}\).

It is given that \({\bf{x}}\) and \(\left\{ {{{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}} \right\}\) is an orthonormal set in \({\mathbb{R}^n}\). Consider \(\widehat {\bf{x}}\) as the orthogonal projection of \({\bf{x}}\) onto the subspace spanned by the vectors \({{\bf{v}}_1}, \ldots ,{{\bf{v}}_p}\).

According to Theorem 10, \(\widehat {\bf{x}} = \left( {{\bf{x}} \cdot {{\bf{v}}_1}} \right){{\bf{v}}_1} + \ldots + \left( {{\bf{x}} \cdot {{\bf{v}}_p}} \right){{\bf{v}}_p}\). According to Exercise 2, \({\left\| {\widehat {\bf{x}}} \right\|^2} = {\left| {{\bf{x}} \cdot {{\bf{v}}_1}} \right|^2} + \ldots + {\left| {{\bf{x}} \cdot {{\bf{v}}_p}} \right|^2}\). Bessel’s inequality arises from the fact that \({\left\| {\widehat {\bf{x}}} \right\|^2} \le {\left\| {\bf{x}} \right\|^2}\)that is stated before the proof of the Cauchy-Schwarz inequality.

Thus, it is verified that Bessel’s equality \({\left\| {\widehat {\bf{x}}} \right\|^2} = {\left| {{\bf{x}} \cdot {{\bf{v}}_1}} \right|^2} + \ldots + {\left| {{\bf{x}} \cdot {{\bf{v}}_p}} \right|^2}\) is true for all \({\bf{x}}\) in \({\mathbb{R}^n}\).

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Most popular questions from this chapter

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.

  1. \(\left( {1,0} \right),\left( {2,1} \right),\left( {4,2} \right),\left( {5,3} \right)\)

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

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

Use the Gram–Schmidt process as in Example 2 to produce an orthogonal basis for the column space of

\(A = \left( {\begin{aligned}{{}{r}}{ - 10}&{13}&7&{ - 11}\\2&1&{ - 5}&3\\{ - 6}&3&{13}&{ - 3}\\{16}&{ - 16}&{ - 2}&5\\2&1&{ - 5}&{ - 7}\end{aligned}} \right)\)

Given data for a least-squares problem, \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\), the following abbreviations are helpful:

\(\begin{aligned}{l}\sum x = \sum\nolimits_{i = 1}^n {{x_i}} ,{\rm{ }}\sum {{x^2}} = \sum\nolimits_{i = 1}^n {x_i^2} ,\\\sum y = \sum\nolimits_{i = 1}^n {{y_i}} ,{\rm{ }}\sum {xy} = \sum\nolimits_{i = 1}^n {{x_i}{y_i}} \end{aligned}\)

The normal equations for a least-squares line \(y = {\hat \beta _0} + {\hat \beta _1}x\) may be written in the form

\(\begin{aligned}{c}{{\hat \beta }_0} + {{\hat \beta }_1}\sum x = \sum y \\{{\hat \beta }_0}\sum x + {{\hat \beta }_1}\sum {{x^2}} = \sum {xy} {\rm{ (7)}}\end{aligned}\)

Derive the normal equations (7) from the matrix form given in this section.

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

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