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Suppose \(A = QR\), where \(R\) is an invertible matrix. Showthat \(A\) and \(Q\) have the same column space.

Short Answer

Expert verified

It is verified that \(A\) and \(Q\) have same column space.

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

Show that \(Col\left( A \right) = Col\left( Q \right)\)

Let \({\bf{y}} \in {\rm{Col}}A\). Then we have\(A{\bf{x}} = {\bf{y}}\) for any vector \({\bf{x}}\).

Then

\(\begin{aligned}{}A{\bf{x}} &= QR{\bf{x}}\\{\bf{y}} &= Q\left( {R{\bf{x}}} \right)\\{\bf{y}} &= Q{\bf{v}}\end{aligned}\)

Where \({\bf{v}} = R{\bf{x}}\).

Hence it is shown that \({\bf{y}}\) is also in \({\rm{Col}}Q\).

So \({\rm{Col}}A \subseteq {\rm{Col}}Q\).

Conversely let \({\bf{y}} \in {\rm{Col}}Q\), then for some vector \({\bf{x}}\) we have

\(Q{\bf{x}} = {\bf{y}}\).

Now it is given that \(R\) is invertible, hence

\(\begin{aligned}{}A &= QR\\A{R^{ - 1}} &= Q\\A{R^{ - 1}}{\bf{x}} &= Q{\bf{x}}\\A\left( {{R^{ - 1}}{\bf{x}}} \right) &= {\bf{y}}\\Aw &= {\bf{y}}\end{aligned}\)

Where \(w = {R^{ - 1}}{\bf{x}}\).

Hence \(y\) is also in the column space of \(A\).

So \({\rm{Col}}A \supseteq {\rm{Col}}Q\).

Thus \({\rm{Col}}A = {\rm{Col}}Q\). Hence it is showed.

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Most popular questions from this chapter

Suppose the x-coordinates of the data \(\left( {{x_1},{y_1}} \right), \ldots ,\left( {{x_n},{y_n}} \right)\) are in mean deviation form, so that \(\sum {{x_i}} = 0\). Show that if \(X\) is the design matrix for the least-squares line in this case, then \({X^T}X\) is a diagonal matrix.

Compute the quantities in Exercises 1-8 using the vectors

\({\mathop{\rm u}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 1}\\2\end{aligned}} \right),{\rm{ }}{\mathop{\rm v}\nolimits} = \left( {\begin{aligned}{*{20}{c}}4\\6\end{aligned}} \right),{\rm{ }}{\mathop{\rm w}\nolimits} = \left( {\begin{aligned}{*{20}{c}}3\\{ - 1}\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm x}\nolimits} = \left( {\begin{aligned}{*{20}{c}}6\\{ - 2}\\3\end{aligned}} \right)\)

  1. \({\mathop{\rm u}\nolimits} \cdot {\mathop{\rm u}\nolimits} ,{\rm{ }}{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm u}\nolimits} ,\,\,{\mathop{\rm and}\nolimits} \,\,\frac{{{\mathop{\rm v}\nolimits} \cdot {\mathop{\rm u}\nolimits} }}{{{\mathop{\rm u}\nolimits} \cdot {\mathop{\rm u}\nolimits} }}\)

Find an orthogonal basis for the column space of each matrix in Exercises 9-12.

11. \(\left( {\begin{aligned}{{}{}}1&2&5\\{ - 1}&1&{ - 4}\\{ - 1}&4&{ - 3}\\1&{ - 4}&7\\1&2&1\end{aligned}} \right)\)

In exercises 1-6, determine which sets of vectors are orthogonal.

\(\left[ {\begin{align}{ 2}\\{ - 7}\\{-1}\end{align}} \right]\), \(\left[ {\begin{align}{ - 6}\\{ - 3}\\9\end{align}} \right]\), \(\left[ {\begin{align}{ 3}\\{ 1}\\{-1}\end{align}} \right]\)

Determine which pairs of vectors in Exercises 15-18 are orthogonal.

15. \({\mathop{\rm a}\nolimits} = \left( {\begin{aligned}{*{20}{c}}8\\{ - 5}\end{aligned}} \right),{\rm{ }}{\mathop{\rm b}\nolimits} = \left( {\begin{aligned}{*{20}{c}}{ - 2}\\{ - 3}\end{aligned}} \right)\)

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