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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–Schwarz 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

[M] Let \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) be the fourth-order and fifth order Fourier approximations in \(C\left[ {{\bf{0}},{\bf{2}}\pi } \right]\) to the square wave function in Exercise 10. Produce separate graphs of \({f_{\bf{4}}}\) and \({f_{\bf{5}}}\) on the interval \(\left[ {{\bf{0}},{\bf{2}}\pi } \right]\), and produce graph of \({f_{\bf{5}}}\) on \(\left[ { - {\bf{2}}\pi ,{\bf{2}}\pi } \right]\).

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

\(\left[ {\begin{align} 2\\{-5}\\{-3}\end{align}} \right]\), \(\left[ {\begin{align}0\\0\\0\end{align}} \right]\), \(\left[ {\begin{align} 4\\{ - 2}\\6\end{align}} \right]\)

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.)

Let \({{\bf{u}}_1},......,{{\bf{u}}_p}\) be an orthogonal basis for a subspace \(W\) of \({\mathbb{R}^n}\), and let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be defined by \(T\left( x \right) = {\rm{pro}}{{\rm{j}}_W}x\). Show that \(T\) is a linear transformation.

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

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