Given u in \({\mathbb{R}^n}\) with \({{\mathop{\rm u}\nolimits} ^T}u = 1\), let \(P = u{{\mathop{\rm u}\nolimits} ^T}\) (an outer product) and \(Q = I - 2P\). Justify statements (a), (b), and (c).

  1. \({P^2} = P\)
  2. \({P^T} = P\)
  3. \({Q^2} = I\)

The transformation \({\mathop{\rm x}\nolimits} \mapsto P{\mathop{\rm x}\nolimits} \) is called a projection and \({\mathop{\rm x}\nolimits} \mapsto Q{\mathop{\rm x}\nolimits} \) is called a Householder reflection. Such reflections are used in computer programs to create multiple zeros in a vector (usually a column of a matrix).

Short Answer

Expert verified
  1. It is proved that \({P^2} = P\).
  2. It is proved that \({P^T} = P\).
  3. It is proved that \({Q^2} = I\).

Step by step solution

01

Show that \({P^2} = P\)

a)

It is given that u is in \({\mathbb{R}^n}\) with \({{\mathop{\rm u}\nolimits} ^T}u = 1\). Let \(P = u{{\mathop{\rm u}\nolimits} ^T}\) and \(Q = I - 2P\).

\(\begin{array}{c}{P^2} = \left( {{{{\mathop{\rm uu}\nolimits} }^T}} \right)\left( {{{{\mathop{\rm uu}\nolimits} }^T}} \right)\\ = {\mathop{\rm u}\nolimits} \left( {{u^T}{\mathop{\rm u}\nolimits} } \right){{\mathop{\rm u}\nolimits} ^T}\\ = {\mathop{\rm u}\nolimits} \left( 1 \right){{\mathop{\rm u}\nolimits} ^T}\\ = P\end{array}\)

Since u satisfies \({{\mathop{\rm u}\nolimits} ^T}{\mathop{\rm u}\nolimits} = 1\), it is proved that \({P^2} = P\).

02

Show that \({P^T} = P\)

\(\begin{array}{c}{P^T} = {\left( {{{{\mathop{\rm uu}\nolimits} }^T}} \right)^T}\\ = {\left( {{{\mathop{\rm u}\nolimits} ^T}} \right)^T}{{\mathop{\rm u}\nolimits} ^T}\\ = {\mathop{\rm u}\nolimits} {{\mathop{\rm u}\nolimits} ^T}\\ = P\end{array}\)

Thus, it is proved that \({P^T} = P\).

03

Show that \({Q^2} = I\)

\(\begin{array}{c}{Q^2} = \left( {I - 2P} \right)\left( {I - 2P} \right)\\ = I - I\left( {2P} \right) - 2PI + 2P\left( {2P} \right)\\ = I - 4P + 4{P^2}\\ = I - 4P + 4P\,\,\,\left( {{\mathop{\rm Since}\nolimits} \,\,{P^2} = P} \right)\\ = I\end{array}\)

Thus, it is proved that \({Q^2} = I\).

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Most popular questions from this chapter

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be an invertible linear transformation, and let Sand U be functions from \({\mathbb{R}^n}\) into \({\mathbb{R}^n}\) such that \(S\left( {T\left( {\mathop{\rm x}\nolimits} \right)} \right) = {\mathop{\rm x}\nolimits} \) and \(\)\(U\left( {T\left( {\mathop{\rm x}\nolimits} \right)} \right) = {\mathop{\rm x}\nolimits} \) for all x in \({\mathbb{R}^n}\). Show that \(U\left( v \right) = S\left( v \right)\) for all v in \({\mathbb{R}^n}\). This will show that Thas a unique inverse, as asserted in theorem 9. [Hint: Given any v in \({\mathbb{R}^n}\), we can write \({\mathop{\rm v}\nolimits} = T\left( {\mathop{\rm x}\nolimits} \right)\) for some x. Why? Compute \(S\left( {\mathop{\rm v}\nolimits} \right)\) and \(U\left( {\mathop{\rm v}\nolimits} \right)\)].

Use the inverse found in Exercise 1 to solve the system

\(\begin{aligned}{l}{\bf{8}}{{\bf{x}}_{\bf{1}}} + {\bf{6}}{{\bf{x}}_{\bf{2}}} = {\bf{2}}\\{\bf{5}}{{\bf{x}}_{\bf{1}}} + {\bf{4}}{{\bf{x}}_{\bf{2}}} = - {\bf{1}}\end{aligned}\)

Use the inverse found in Exercise 3 to solve the system

\(\begin{aligned}{l}{\bf{8}}{{\bf{x}}_{\bf{1}}} + {\bf{5}}{{\bf{x}}_{\bf{2}}} = - {\bf{9}}\\ - {\bf{7}}{{\bf{x}}_{\bf{1}}} - {\bf{5}}{{\bf{x}}_{\bf{2}}} = {\bf{11}}\end{aligned}\)

In the rest of this exercise set and in those to follow, you should assume that each matrix expression is defined. That is, the sizes of the matrices (and vectors) involved match appropriately.

Let \(A = \left( {\begin{aligned}{*{20}{c}}{\bf{4}}&{ - {\bf{1}}}\\{\bf{5}}&{ - {\bf{2}}}\end{aligned}} \right)\). Compute \({\bf{3}}{I_{\bf{2}}} - A\) and \(\left( {{\bf{3}}{I_{\bf{2}}}} \right)A\).

If Ais an \(n \times n\) matrix and the transformation \({\bf{x}}| \to A{\bf{x}}\) is one-to-one, what else can you say about this transformation? Justify your answer.

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