Question: In Exercises 3-6, verify that\(\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\)is an orthogonal set, and then find the orthogonal projection of y onto\({\bf{Span}}\left\{ {{{\bf{u}}_{\bf{1}}},{{\bf{u}}_{\bf{2}}}} \right\}\).

4.\(y = \left[ {\begin{aligned}{\bf{6}}\\{\bf{3}}\\{ - {\bf{2}}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{1}}} = \left[ {\begin{aligned}{\bf{3}}\\{\bf{4}}\\{\bf{0}}\end{aligned}} \right]\),\({{\bf{u}}_{\bf{2}}} = \left[ {\begin{aligned}{ - {\bf{4}}}\\{\bf{3}}\\{\bf{0}}\end{aligned}} \right]\)

Short Answer

Expert verified

The orthogonal projection is \(\left[ {\begin{aligned}6\\3\\0\end{aligned}} \right]\).

Step by step solution

01

Perform the orthogonality test for \({{\bf{u}}_1}\) and \({{\bf{u}}_{\bf{2}}}\)

The orthogonality test for vectors\({{\bf{u}}_1}\)and\({{\bf{u}}_2}\)can be performed as follows:

\[\begin{aligned}{c}{{\bf{u}}_1} \cdot {{\bf{u}}_2} &= {\bf{u}}_1^T{{\bf{u}}_2}\\ &= \left[ {\begin{aligned}3&4&0\end{aligned}} \right]\left[ {\begin{aligned}{ - 4}\\3\\0\end{aligned}} \right]\\ &= - 12 + 12\\ &= 0\end{aligned}\]

Thus, the vectors \({{\bf{u}}_1}\) and \({{\bf{u}}_2}\) are orthogonal.

02

Find the orthogonal component

The orthogonal component in the\({\rm{span}}\left\{ {{{\bf{u}}_1},{{\bf{u}}_2}} \right\}\)is:

\(\begin{aligned}{c}{\bf{\hat y}} &= \frac{{{\bf{y}} \cdot {{\bf{u}}_1}}}{{{{\bf{u}}_1} \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \frac{{{\bf{y}} \cdot {{\bf{u}}_{\bf{2}}}}}{{{{\bf{u}}_{\bf{2}}} \cdot {{\bf{u}}_{\bf{2}}}}}{{\bf{u}}_2}\\ &= \frac{{{{\bf{y}}^T} \cdot {{\bf{u}}_1}}}{{{\bf{u}}_{\bf{1}}^T \cdot {{\bf{u}}_1}}}{{\bf{u}}_1} + \frac{{{{\bf{y}}^T} \cdot {{\bf{u}}_{\bf{2}}}}}{{{\bf{u}}_2^T \cdot {{\bf{u}}_{\bf{2}}}}}{{\bf{u}}_2}\\ &= \frac{{\left[ {\begin{aligned}6&3&{ - 2}\end{aligned}} \right]\left[ {\begin{aligned}3\\4\\0\end{aligned}} \right]}}{{\left[ {\begin{aligned}3&4&0\end{aligned}} \right]\left[ {\begin{aligned}3\\4\\0\end{aligned}} \right]}}{{\bf{u}}_1} + \frac{{\left[ {\begin{aligned}6&3&{ - 2}\end{aligned}} \right]\left[ {\begin{aligned}{ - 4}\\3\\0\end{aligned}} \right]}}{{\left[ {\begin{aligned}{ - 4}&3&0\end{aligned}} \right]\left[ {\begin{aligned}{ - 4}\\3\\0\end{aligned}} \right]}}{{\bf{u}}_2}\\ &= \frac{{30}}{{25}}\left[ {\begin{aligned}3\\4\\0\end{aligned}} \right] - \frac{3}{5}\left[ {\begin{aligned}{ - 4}\\3\\0\end{aligned}} \right]\\ &= \left[ {\begin{aligned}6\\3\\0\end{aligned}} \right]\end{aligned}\)

Thus, the orthogonal projection is \(\left[ {\begin{aligned}6\\3\\0\end{aligned}} \right]\).

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Most popular questions from this chapter

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.

In Exercises 5 and 6, describe all least squares solutions of the equation \(A{\bf{x}} = {\bf{b}}\).

5. \(A = \left( {\begin{aligned}{{}{}}{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{0}}&{\bf{1}}\\{\bf{1}}&{\bf{0}}&{\bf{1}}\end{aligned}} \right)\), \({\bf{b}} = \left( {\begin{aligned}{{}{}}{\bf{1}}\\{\bf{3}}\\{\bf{8}}\\{\bf{2}}\end{aligned}} \right)\)

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\[W\].

10.\[y = \left[ {\begin{aligned}3\\4\\5\\6\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\0\\{ - 1}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}1\\0\\1\\1\end{aligned}} \right]\],\[{{\bf{u}}_3} = \left[ {\begin{aligned}0\\{ - 1}\\1\\{ - 1}\end{aligned}} \right]\]

Compute the least-squares error associated with the least square solution found in Exercise 3.

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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