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Let \(A\) be an \(m \times n\) matrix such that \({A^T}A\) is invertible. Show that the columns of \(A\) are linearly independent.

Short Answer

Expert verified

It is proved that columns of \(A\) are linearly independent.

Step by step solution

01

Rank of matrix

Here, it is given that \(A\) is an \(m \times n\) matrix. So, \({A^T}A\) be an \(n \times n\) matrix since \({A^T}A\) is invertible. Therefore, the rank of \({A^T}A\) is \(n\).

Now, the dimension of \({\rm{Nul}}\,{A^T}A\)will be \(\dim \left( {Nul\,{A^T}A} \right) = n - n = 0\).

It is known that if \(A\) is a \(m \times n\) matrix, then a vector \(x\) in \({\mathbb{R}^n}\) satisfies \(Ax = 0\) if and only if \({A^T}Ax = 0\). Hence,

\(\begin{aligned}{}{\rm{Nul}}\,A &= {\rm{Nul}}\,{A^T}A\\\dim \left( {{\rm{Nul}}\,A} \right) &= \dim \left( {{\rm{Nul}}\,{A^T}A} \right)\\\dim \left( {{\rm{Nul}}\,A} \right) &= 0\end{aligned}\)

02

Dimension of column space

The dimension of column space of \(A\) is given by:

\(\begin{aligned}{}\dim \left( {{\rm{Col}}\,A} \right) & = {\rm{rank}}\,A\\ &= n - 0\\ &= n\end{aligned}\)

Therefore, the above calculation shows that the columns of \(A\) are linearly independent.

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Most popular questions from this chapter

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

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

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

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

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\[W\].

8.\[y = \left[ {\begin{aligned}{ - 1}\\4\\3\end{aligned}} \right]\],\[{{\bf{u}}_1} = \left[ {\begin{aligned}1\\1\\{\bf{1}}\end{aligned}} \right]\],\[{{\bf{u}}_2} = \left[ {\begin{aligned}{ - 1}\\3\\{ - 2}\end{aligned}} \right]\]

Exercises 13 and 14, the columns of \(Q\) were obtained by applying the Gram Schmidt process to the columns of \(A\). Find anupper triangular matrix \(R\) such that \(A = QR\). Check your work.

14.\(A = \left( {\begin{aligned}{{}{r}}{ - 2}&3\\5&7\\2&{ - 2}\\4&6\end{aligned}} \right)\), \(Q = \left( {\begin{aligned}{{}{r}}{\frac{{ - 2}}{7}}&{\frac{5}{7}}\\{\frac{5}{7}}&{\frac{2}{7}}\\{\frac{2}{7}}&{\frac{{ - 4}}{7}}\\{\frac{4}{7}}&{\frac{2}{7}}\end{aligned}} \right)\)

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.

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