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Qis any matrix such that

\({\left( {\bf{v}} \right)_C} = Q{\left( {\bf{v}} \right)_B}\)for each v in V (9)

Set \({\bf{v}} = {{\bf{b}}_{\bf{1}}}\) in (9). Then (9) shows that \({\left( {{{\bf{b}}_{\bf{1}}}} \right)_C}\) is the first column of Q because (a) _____. Similarly, for \(k = {\bf{2}}\),…..n the kth column of Q is (b) _____ because (c) _____. This shows the matrix \(\mathop P\limits_{C \leftarrow B} \) defined by (5) in Theorem 15 is the only matrix that satisfies condition (4).

Short Answer

Expert verified

(a) \({\left( {{{\bf{b}}_1}} \right)_C} = Q{{\bf{e}}_1}\)

(b) \({\left( {{{\bf{b}}_k}} \right)_C}\)

(c) \({\left( {{{\bf{b}}_k}} \right)_C} = Q{{\bf{e}}_k}\)

Step by step solution

01

Check for blank (a)

The columns of Q are C coordinate vectors of the vectors in basis B,that is,

\(Q = \left( {{{\left( {{{\bf{b}}_1}} \right)}_C}{{\left( {{{\bf{b}}_2}} \right)}_C}.....{{\left( {{{\bf{b}}_k}} \right)}_C}} \right)\).

If you set \({\bf{v}} = {{\bf{b}}_1}\) in (1), then \({\left( {{{\bf{b}}_1}} \right)_C}\) represents the first column of Q because

\(\begin{aligned} {\left( {{{\bf{b}}_1}} \right)_C} &= Q{\left( {{{\bf{b}}_1}} \right)_B}\\ &= \left( {{{\left( {{{\bf{b}}_1}} \right)}_C}\,{{\left( {{{\bf{b}}_2}} \right)}_C}\,\,....\,\,{{\left( {{{\bf{b}}_k}} \right)}_C}} \right)\left( {\begin{array}{*{20}{c}}1\\0\\ \vdots \\0\end{array}} \right)\\ &= Q{{\bf{e}}_1}.\end{aligned}\)

02

Check for blank (b)

For \(k = 2\),…n, the kthcolumn of Q is

\(\begin{aligned} {\left( {{{\bf{b}}_k}} \right)_C} &= Q{\left( {{{\bf{b}}_1}} \right)_B}\\ &= \left( {{{\left( {{{\bf{b}}_1}} \right)}_C}\,\,{{\left( {{{\bf{b}}_2}} \right)}_C}\,\,...\,\,{{\left( {{{\bf{b}}_k}} \right)}_C}} \right)\left( {\begin{array}{*{20}{c}}0\\0\\ \vdots \\1\end{array}} \right)\\ &= Q{e_k}.\end{aligned}\)

So, the kth column of Q is \({\left( {{{\bf{b}}_k}} \right)_C}\).

03

Check for blank (c)

\(\mathop P\limits_{C + B} = \left( {{{\left( {{{\bf{b}}_1}} \right)}_C}\,\,{{\left( {{{\bf{b}}_2}} \right)}_C}\,\,....\,\,{{\left( {{{\bf{b}}_n}} \right)}_C}} \right)\)is the only matrix that satisfies the condition

\({\left( {\bf{x}} \right)_c} = \mathop P\limits_{C = S} {\left( {\bf{x}} \right)_S}\).

It means,

\(\begin{aligned}{c}{\left( {{{\bf{b}}_k}} \right)_C} &= Q{\left( {{{\bf{b}}_1}} \right)_B}\\ &= \left( {{{\left( {{{\bf{b}}_1}} \right)}_C}\,{{\left( {{{\bf{b}}_2}} \right)}_C}\,...\,\,{{\left( {{{\bf{b}}_k}} \right)}_C}} \right)\left( {\begin{array}{*{20}{c}}0\\0\\ \vdots \\1\end{array}} \right)\\ &= Q{{\bf{e}}_k}.\end{aligned}\)

So, \({\left( {{{\bf{b}}_k}} \right)_C} = Q{{\bf{e}}_k}\).

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Most popular questions from this chapter

Consider the polynomials , and \({p_{\bf{3}}}\left( t \right) = {\bf{2}}\) \({p_{\bf{1}}}\left( t \right) = {\bf{1}} + t,{p_{\bf{2}}}\left( t \right) = {\bf{1}} - t\)(for all t). By inspection, write a linear dependence relation among \({p_{\bf{1}}},{p_{\bf{2}}},\) and \({p_{\bf{3}}}\). Then find a basis for Span\(\left\{ {{p_{\bf{1}}},{p_{\bf{2}}},{p_{\bf{3}}}} \right\}\).

If the null space of an \({\bf{8}} \times {\bf{5}}\) matrix A is 2-dimensional, what is the dimension of the row space of A?

Exercises 23-26 concern a vector space V, a basis \(B = \left\{ {{{\bf{b}}_{\bf{1}}},....,{{\bf{b}}_n}\,} \right\}\) and the coordinate mapping \({\bf{x}} \mapsto {\left( {\bf{x}} \right)_B}\).

Show that a subset \(\left\{ {{{\bf{u}}_1},...,{{\bf{u}}_p}} \right\}\) in V is linearly independent if and only if the set of coordinate vectors \(\left\{ {{{\left( {{{\bf{u}}_{\bf{1}}}} \right)}_B},.....,{{\left( {{{\bf{u}}_p}} \right)}_B}} \right\}\) is linearly independent in \({\mathbb{R}^n}\)(Hint: Since the coordinate mapping is one-to-one, the following equations have the same solutions, \({c_{\bf{1}}}\),….,\({c_p}\))

\({c_{\bf{1}}}{{\bf{u}}_{\bf{1}}} + ..... + {c_p}{{\bf{u}}_p} = {\bf{0}}\) The zero vector V

\({\left( {{c_{\bf{1}}}{{\bf{u}}_{\bf{1}}} + ..... + {c_p}{{\bf{u}}_p}} \right)_B} = {\left( {\bf{0}} \right)_B}\) The zero vector in \({\mathbb{R}^n}\)a

The null space of a \({\bf{5}} \times {\bf{6}}\) matrix A is 4-dimensional, what is the dimension of the column space of A.

Question: Exercises 12-17 develop properties of rank that are sometimes needed in applications. Assume the matrix \(A\) is \(m \times n\).

13. Show that if \(P\) is an invertible \(m \times m\) matrix, then rank\(PA\)=rank\(A\).(Hint: Apply Exercise12 to \(PA\) and \({P^{ - 1}}\left( {PA} \right)\).)

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