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Suppose a vector \({\bf{y}}\) is orthogonal to vectors \({\bf{u}}\) and \({\bf{v}}\). Show that \({\rm{y}}\) is orthogonal to the vector \({\bf{u}} + {\bf{v}}\).

Short Answer

Expert verified

It is verified that \({\bf{y}}\)is orthogonal to vector \({\bf{u}} + {\bf{v}}\).

Step by step solution

01

Definition of Orthogonal sets.

The two vectors \({\bf{u}}{\rm{ and }}{\bf{v}}\) are Orthogonal if:

\(\begin{aligned}{l}{\left\| {{\bf{u}} + {\bf{v}}} \right\|^2} = {\left\| {\bf{u}} \right\|^2} + {\left\| {\bf{v}} \right\|^2}\\{\rm{and}}\\{\bf{u}} \cdot {\bf{v}} = 0\end{aligned}\).

02

 Computing the required values.

Since, \({\bf{y}}\) is orthogonal to vectors \({\bf{u}}{\rm{ and }}{\bf{v}}\).

Therefore,

\(\begin{aligned}{l}{\bf{y}} \cdot {\bf{u}} = 0\\{\bf{y}} \cdot {\bf{v}} = 0\end{aligned}\)

Using the inner productrule, we have:

\(\begin{aligned}{c}{\bf{y}} \cdot \left( {{\bf{u}} + {\bf{v}}} \right) &= {\bf{y}} \cdot {\bf{u}} + {\bf{y}} \cdot {\bf{v}}\\ &= 0 + 0\\ &= 0\end{aligned}\).

Hence proved: \({\bf{y}}\)is orthogonal to vector \({\bf{u}} + {\bf{v}}\).

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Most popular questions from this chapter

a. Rewrite the data in Example 1 with new \(x\)-coordinates in mean deviation form. Let \(X\) be the associated design matrix. Why are the columns of \(X\) orthogonal?

b. Write the normal equations for the data in part (a), and solve them to find the least-squares line, \(y = {\beta _0} + {\beta _1}x*\), where \(x* = x - 5.5\).

In Exercises 3–6, verify that\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\]is an orthogonal set, and then find the orthogonal projection of\[y\]onto Span\[\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\].

6.\[{\rm{y}} = \left[ {\begin{aligned}6\\4\\1\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}{ - 4}\\{ - 1}\\1\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}0\\1\\1\end{aligned}} \right]\]

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.

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

Let \(X\) be the design matrix used to find the least square line of fit data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\). Use a theorem in Section 6.5 to show that the normal equations have a unique solution if and only if the data include at least two data points with different \(x\)-coordinates.

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