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Let \({\rm{u}} = \left( \begin{array}{l}a\\b\end{array} \right)\)and\({\rm{v}} = \left( \begin{array}{l}1\\1\end{array} \right)\). Use the Cauchy鈥揝chwarz inequality to show that \({\left( {\frac{{a + b}}{2}} \right)^2} \le \frac{{{a^2} + {b^2}}}{2}\).

Short Answer

Expert verified

The inequality \({\left( {\frac{{a + b}}{2}} \right)^2} \le \frac{{{a^2} + {b^2}}}{2}\) is proved to be true.

Step by step solution

01

Use the given information

It is given that \({\rm{u}} = \left( \begin{array}{l}a\\b\end{array} \right)\) and \({\rm{v}} = \left( \begin{array}{l}1\\1\end{array} \right)\). So, according to inner product axioms, \({\left\| {\rm{u}} \right\|^2} = {a^2} + {b^2}\) and \({\left\| {\rm{v}} \right\|^2} = 2\)and\(\left\langle {{\rm{u,v}}} \right\rangle = a + b\).

02

Modify the Cauchy Schwarz inequality

According to Cauchy Schwarz inequality, an inner product on a vector space \(V\) is a function that, to each pair of vectors \({\bf{u}}\) and \({\rm{v}}\) in \(V\), associates a real number \(\left\langle {{\bf{u}},{\rm{v}}} \right\rangle \)and satisfies the axiom, \(\left| {\left\langle {{\bf{u}},{\rm{v}}} \right\rangle } \right| \le \left\| {\rm{u}} \right\|\left\| {\rm{v}} \right\|\) for all \({\bf{u}}\) and \({\rm{v}}\)in \(V\).

Divide both sides of Cauchy Schwarz inequality by 2 and thence squaring both sides yieldthe following result:

\(\begin{array}{c}{\left\langle {{\rm{u,v}}} \right\rangle ^2} \le {\left\| {\rm{u}} \right\|^2}{\left\| {\rm{v}} \right\|^2}\\\frac{{{{\left\langle {{\rm{u,v}}} \right\rangle }^2}}}{4} \le \frac{{{{\left\| {\rm{u}} \right\|}^2}{{\left\| {\rm{v}} \right\|}^2}}}{4}\\{\left( {\frac{{\left\langle {{\rm{u,v}}} \right\rangle }}{2}} \right)^2} \le \frac{{{{\left\| {\rm{u}} \right\|}^2}{{\left\| {\rm{v}} \right\|}^2}}}{4}\end{array}\)

03

Use Cauchy Schwarz inequality

Plug the above obtained values into theinequality \({\left( {\frac{{\left\langle {{\rm{u,v}}} \right\rangle }}{2}} \right)^2} \le \frac{{{{\left\| {\rm{u}} \right\|}^2}{{\left\| {\rm{v}} \right\|}^2}}}{4}\), as follows:

\(\begin{array}{c}{\left( {\frac{{\left\langle {{\rm{u,v}}} \right\rangle }}{2}} \right)^2} \le \frac{{{{\left\| {\rm{u}} \right\|}^2}{{\left\| {\rm{v}} \right\|}^2}}}{4}\\{\left( {\frac{{a + b}}{2}} \right)^2} \le \frac{{\left( {{a^2} + {b^2}} \right)\left( 2 \right)}}{4}\\{\left( {\frac{{a + b}}{2}} \right)^2} \le \frac{{{a^2} + {b^2}}}{2}\end{array}\)

Thus, the inequality\({\left( {\frac{{a + b}}{2}} \right)^2} \le \frac{{{a^2} + {b^2}}}{2}\)is proved to be true.

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Most popular questions from this chapter

Compute the quantities in Exercises 1-8 using the vectors

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

5. \(\left( {\frac{{{\mathop{\rm u}\nolimits} \cdot {\mathop{\rm v}\nolimits} }}{{{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm v}\nolimits} }}} \right){\mathop{\rm v}\nolimits} \)

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.

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) \({\left\| {X\hat \beta } \right\|^2}\)鈥攖he sum of the squares of the 鈥渞egression term.鈥 Denote this number by \(SS\left( R \right)\).

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)鈥攖he sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)鈥攖he 鈥渢otal鈥 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 \(SS\left( E \right)\):

\(SS\left( E \right) = {y^T}y - \hat \beta {X^T}y\)

A simple curve that often makes a good model for the variable costs of a company, a function of the sales level \(x\), has the form \(y = {\beta _1}x + {\beta _2}{x^2} + {\beta _3}{x^3}\). There is no constant term because fixed costs are not included.

a. Give the design matrix and the parameter vector for the linear model that leads to a least-squares fit of the equation above, with data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\).

b. Find the least-squares curve of the form above to fit the data \(\left( {4,1.58} \right),\left( {6,2.08} \right),\left( {8,2.5} \right),\left( {10,2.8} \right),\left( {12,3.1} \right),\left( {14,3.4} \right),\left( {16,3.8} \right)\) and \(\left( {18,4.32} \right)\), with values in thousands. If possible, produce a graph that shows the data points and the graph of the cubic approximation.

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) \({\left\| {X\hat \beta } \right\|^2}\)鈥攖he sum of the squares of the 鈥渞egression term.鈥 Denote this number by .

(ii) \({\left\| {{\bf{y}} - X\hat \beta } \right\|^2}\)鈥攖he sum of the squares for error term. Denote this number by \(SS\left( E \right)\).

(iii) \({\left\| {\bf{y}} \right\|^2}\)鈥攖he 鈥渢otal鈥 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.

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