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In Exercises 1-4, find a least-sqaures solution of \(A{\bf{x}} = {\bf{b}}\) by (a) constructing a normal equations for \({\bf{\hat x}}\) and (b) solving for \({\bf{\hat x}}\).

1. \(A = \left[ {\begin{aligned}{{}{}}{ - {\bf{1}}}&{\bf{2}}\\{\bf{2}}&{ - {\bf{3}}}\\{ - {\bf{1}}}&{\bf{3}}\end{aligned}} \right]\), \({\bf{b}} = \left[ {\begin{aligned}{{}{}}{\bf{4}}\\{\bf{1}}\\{\bf{2}}\end{aligned}} \right]\)

Short Answer

Expert verified

(a) \(\left[ {\begin{aligned}{{}{}}{ - 4}\\{11}\end{aligned}} \right]\)

(b)\(\left[ {\begin{aligned}{{}{}}3\\2\end{aligned}} \right]\)

Step by step solution

01

(a) Step 1: Find the products \({A^T}A\) and \({A^T}{\bf{b}}\)

Find the product \({A^T}A\).

\(\begin{aligned}{}{A^T}A &= \left[ {\begin{aligned}{{}{}}{ - 1}&2&{ - 1}\\2&{ - 3}&3\end{aligned}} \right]\left[ {\begin{aligned}{{}{}}{ - 1}&2\\2&{ - 3}\\{ - 1}&3\end{aligned}} \right]\\ &= \left[ {\begin{aligned}{{}{}}6&{ - 11}\\{ - 11}&{22}\end{aligned}} \right]\end{aligned}\)

Find the product \({A^T}{\bf{b}}\).

\(\begin{aligned}{}{A^T}{\bf{b}} &= \left[ {\begin{aligned}{{}{}}{ - 1}&2&{ - 1}\\2&{ - 3}&3\end{aligned}} \right]\left[ {\begin{aligned}{{}{}}4\\1\\2\end{aligned}} \right]\\ &= \left[ {\begin{aligned}{{}{}}{ - 4}\\{11}\end{aligned}} \right]\end{aligned}\)

02

Find the solution by constructing the normal equations

The normal equations can be written as:

\(\begin{aligned}{}\left( {{A^T}A} \right){\bf{x}} = {A^T}{\bf{b}}\\\left[ {\begin{aligned}{{}{}}6&{ - 11}\\{ - 11}&{22}\end{aligned}} \right]\left[ {\begin{aligned}{{}{}}{{x_1}}\\{{x_2}}\end{aligned}} \right] = \left[ {\begin{aligned}{{}{}}{ - 4}\\{11}\end{aligned}} \right]\end{aligned}\)

03

(b) Step 3: Find the component \({\bf{\hat x}}\)

The component \({\bf{\hat x}}\) can be calculated as:

\(\begin{aligned}{}{\bf{\hat x}} &= {\left( {{A^T}A} \right)^{ - 1}}\left( {{A^T}{\bf{b}}} \right)\\ &= {\left[ {\begin{aligned}{{}{}}6&{ - 11}\\{ - 11}&{22}\end{aligned}} \right]^{ - 1}}\left[ {\begin{aligned}{{}{}}{ - 4}\\{11}\end{aligned}} \right]\\ &= \frac{1}{{11}}\left[ {\begin{aligned}{{}{}}{22}&{11}\\{11}&6\end{aligned}} \right]\left[ {\begin{aligned}{{}{}}{ - 4}\\{11}\end{aligned}} \right]\\ &= \frac{1}{{11}}\left[ {\begin{aligned}{{}{}}{33}\\{22}\end{aligned}} \right]\\ &= \left[ {\begin{aligned}{{}{}}3\\2\end{aligned}} \right]\end{aligned}\)

The \({\bf{\hat x}}\) component is \(\left[ {\begin{aligned}{{}{}}3\\2\end{aligned}} \right]\).

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Most popular questions from this chapter

In Exercises 9-12, find (a) the orthogonal projection of b onto \({\bf{Col}}A\) and (b) a least-squares solution of \(A{\bf{x}} = {\bf{b}}\).

10. \(A = \left[ {\begin{aligned}{{}{}}{\bf{1}}&{\bf{2}}\\{ - {\bf{1}}}&{\bf{4}}\\{\bf{1}}&{\bf{2}}\end{aligned}} \right]\), \({\bf{b}} = \left[ {\begin{aligned}{{}{}}{\bf{3}}\\{ - {\bf{1}}}\\{\bf{5}}\end{aligned}} \right]\)

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 9-12 find (a) the orthogonal projection of b onto \({\bf{Col}}A\) and (b) a least-squares solution of \(A{\bf{x}} = {\bf{b}}\).

12. \(A = \left[ {\begin{array}{{}{}}{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{1}}&{\bf{0}}&{ - {\bf{1}}}\\{\bf{0}}&{\bf{1}}&{\bf{1}}\\{ - {\bf{1}}}&{\bf{1}}&{ - {\bf{1}}}\end{array}} \right]\), \({\bf{b}} = \left( {\begin{array}{{}{}}{\bf{2}}\\{\bf{5}}\\{\bf{6}}\\{\bf{6}}\end{array}} \right)\)

In Exercises 1-6, the given set is a basis for a subspace W. Use the Gram-Schmidt process to produce an orthogonal basis for W.

  1. \(\left( {\begin{aligned}{{}{}}3\\0\\{ - 1}\end{aligned}} \right),\left( {\begin{aligned}{{}{}}8\\5\\{ - 6}\end{aligned}} \right)\)

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.)

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