Suppose \(A = QR\) is a \(QR\) factorization of an \(m \times n\) matrix

A (with linearly independent columns). Partition \(A\) as \(\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right]\), where \({A_1}\) has \(p\) columns. Show how to obtain a \(QR\) factorization of \({A_1}\), and explain why your factorization has the appropriate properties.

Short Answer

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It is shown that how to obtain a \(QR\) factorization of \({A_1}\).

Step by step solution

01

\(QR\) factorization of a Matrix

A matrix with order \(m \times n\) can be written as the multiplication of an upper triangular matrix \(R\) and a matrix \(Q\) which is formed by applying the Gram–Schmidt orthogonalization processto the \({\rm{col}}\left( A \right)\).

The matrix \(R\) can be found by the formula \({Q^T}A = R\).

02

Obtain \(QR\) factorization of  \(A\)

Given that \(A = QR\).

Since \(\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right]\) is the partition of\(A\), we can also obtain \(Q\) as portioned such as \(Q = \left[ {\begin{aligned}{{}{}}{{Q_1}}&{{Q_2}}\end{aligned}} \right]\), where both \({A_1}\) and \({Q_1}\) have \(p\) columns.

Now we can partition \(R\) as

\(R = \left[ {\begin{aligned}{{}{}}B&C\\0&D\end{aligned}} \right]\)

Here the matrix \(B\) is \(p \times p\).

Then, find \(A = QR\).

\(\begin{aligned}{}\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right] = \left[ {\begin{aligned}{{}{}}{{Q_1}}&{{Q_2}}\end{aligned}} \right]\left[ {\begin{aligned}{{}{}}B&C\\0&D\end{aligned}} \right]\\\left[ {\begin{aligned}{{}{}}{{A_1}}&{{A_2}}\end{aligned}} \right] = \left[ {\begin{aligned}{{}{}}{{Q_1}B}&{{Q_1}}\end{aligned}C + {Q_2}D} \right]\end{aligned}\)

On comparing both sides, we get\({A_1} = {Q_1}B\), this the \(QR\) factorization of matrix\({A_1}\).

Since,\({Q_1}\) is obtained from \(Q\), thus the columns of \({Q_1}\) are orthonormal.

Also \(B\) is an upper triangular since it is in the 1st entry of the partition. Also, the diagonal entries of the matrix \(B\) are positive since these entries are from the diagonal entries of \(R\).

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Most popular questions from this chapter

Let \(\overline x = \frac{1}{n}\left( {{x_1} + \cdots + {x_n}} \right)\), and \(\overline y = \frac{1}{n}\left( {{y_1} + \cdots + {y_n}} \right)\). Show that the least-squares line for the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) must pass through \(\left( {\overline x ,\overline y } \right)\). That is, show that \(\overline x \) and \(\overline y \) satisfies the linear equation \(\overline y = {\hat \beta _0} + {\hat \beta _1}\overline x \). (Hint: Derive this equation from the vector equation \({\bf{y}} = X{\bf{\hat \beta }} + \in \). Denote the first column of \(X\) by 1. Use the fact that the residual vector \( \in \) is orthogonal to the column space of \(X\) and hence is orthogonal to 1.)

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.

Find a \(QR\) factorization of the matrix in Exercise 12.

In Exercises 9-12, find a unit vector in the direction of the given vector.

9. \(\left( {\begin{aligned}{*{20}{c}}{ - 30}\\{40}\end{aligned}} \right)\)

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

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