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24. (QR Factorization) Suppose \[A = QR\], where Qand R are \[n \times n\], Ris invertible and upper triangular, and Q has the property that \[{Q^T}{\bf{Q}} = I\]. Show that for each b in \[{\mathbb{R}^n}\], the equation \[Ax = b\] has a unique solution. What computations with Q and R will produce the solution?

Short Answer

Expert verified

The matrices Q and R are both invertible, so A is invertible. Hence, \[Ax = b\] has a unique solution for each b in \[{\mathbb{R}^n}\]. Moreover, solutionxis obtained by the row reduction of \[\left[ {\begin{array}{*{20}{c}}R&{{Q^T}b}\end{array}} \right]\].

Step by step solution

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01

Describe the given data

Given, \[A = QR\] with R is invertible and upper triangular, and \[{Q^T}Q = I\].

02

Use the fact of an invertible matrix

Here, \[{Q^T}Q = I\]. This implies that Q is invertible and \[{Q^{ - 1}} = {Q^T}\].

Hence, Q and R are both invertible. Therefore, QR is invertible, and A is also invertible. This implies that \[Ax = b\] has aunique solution for each b in \[{\mathbb{R}^n}\].

03

Use row reduction

Substitute \[A = QR\] in \[Ax = b\] to get:

\[\begin{array}{c}QRx = b\\{Q^T}QRx = {Q^T}b\\IRx = {Q^T}b\\Rx = {Q^T}b\end{array}\]

This implies thatxis obtained by the row reduction of \[\left[ {\begin{array}{*{20}{c}}R&{{Q^T}b}\end{array}} \right]\]. This reduction is fast since R is upper triangular.

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Most popular questions from this chapter

Show that block upper triangular matrix \(A\) in Example 5is invertible if and only if both \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible. [Hint: If \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible, the formula for \({A^{ - {\bf{1}}}}\) given in Example 5 actually works as the inverse of \(A\).] This fact about \(A\) is an important part of several computer algorithims that estimates eigenvalues of matrices. Eigenvalues are discussed in chapter 5.

Suppose the third column of Bis the sum of the first two columns. What can you say about the third column of AB? Why?

Suppose \({A_{{\bf{11}}}}\) is invertible. Find \(X\) and \(Y\) such that

\[\left[ {\begin{array}{*{20}{c}}{{A_{{\bf{11}}}}}&{{A_{{\bf{12}}}}}\\{{A_{{\bf{21}}}}}&{{A_{{\bf{22}}}}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}I&{\bf{0}}\\X&I\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{{A_{{\bf{11}}}}}&{\bf{0}}\\{\bf{0}}&S\end{array}} \right]\left[ {\begin{array}{*{20}{c}}I&Y\\{\bf{0}}&I\end{array}} \right]\]

Where \(S = {A_{{\bf{22}}}} - {A_{21}}A_{{\bf{11}}}^{ - {\bf{1}}}{A_{{\bf{12}}}}\). The matrix \(S\) is called the Schur complement of \({A_{{\bf{11}}}}\). Likewise, if \({A_{{\bf{22}}}}\) is invertible, the matrix \({A_{{\bf{11}}}} - {A_{{\bf{12}}}}A_{{\bf{22}}}^{ - {\bf{1}}}{A_{{\bf{21}}}}\) is called the Schur complement of \({A_{{\bf{22}}}}\). Such expressions occur frequently in the theory of systems engineering, and elsewhere.

Suppose a linear transformation \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) has the property that \(T\left( {\mathop{\rm u}\nolimits} \right) = T\left( {\mathop{\rm v}\nolimits} \right)\) for some pair of distinct vectors u and v in \({\mathbb{R}^n}\). Can Tmap \({\mathbb{R}^n}\) onto \({\mathbb{R}^n}\)? Why or why not?

In exercise 5 and 6, compute the product \(AB\) in two ways: (a) by the definition, where \(A{b_{\bf{1}}}\) and \(A{b_{\bf{2}}}\) are computed separately, and (b) by the row-column rule for computing \(AB\).

\(A = \left( {\begin{aligned}{*{20}{c}}{ - {\bf{1}}}&{\bf{2}}\\{\bf{5}}&{\bf{4}}\\{\bf{2}}&{ - {\bf{3}}}\end{aligned}} \right)\), \(B = \left( {\begin{aligned}{*{20}{c}}{\bf{3}}&{ - {\bf{2}}}\\{ - {\bf{2}}}&{\bf{1}}\end{aligned}} \right)\)

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