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

[M] Let \(A = \left[ {\begin{array}{*{20}{c}}7&{ - 9}&{ - 4}&5&3&{ - 3}&{ - 7}\\{ - 4}&6&7&{ - 2}&{ - 6}&{ - 5}&5\\5&{ - 7}&{ - 6}&5&{ - 6}&2&8\\{ - 3}&5&8&{ - 1}&{ - 7}&{ - 4}&8\\6&{ - 8}&{ - 5}&4&4&9&3\end{array}} \right]\).

  1. Construct matrices \(C\) and \(N\) whose columns are bases for \({\mathop{\rm Col}\nolimits} A\) and \({\mathop{\rm Nul}\nolimits} A\), respectively, and construct a matrix \(R\) whose rows form a basis for Row\(A\).
  2. Construct a matrix \(M\) whose columns form a basis for \({\mathop{\rm Nul}\nolimits} {A^T}\), form the matrices \(S = \left[ {\begin{array}{*{20}{c}}{{R^T}}&N\end{array}} \right]\) and \(T = \left[ {\begin{array}{*{20}{c}}C&M\end{array}} \right]\), and explain why \(S\) and \(T\) should be square. Verify that both \(S\) and \(T\) are invertible.

Question: Determine if the matrix pairs in Exercises 19-22 are controllable.

22. (M) \(A = \left( {\begin{array}{*{20}{c}}0&1&0&0\\0&0&1&0\\0&0&0&1\\{ - 1}&{ - 13}&{ - 12.2}&{ - 1.5}\end{array}} \right),B = \left( {\begin{array}{*{20}{c}}1\\0\\0\\{ - 1}\end{array}} \right)\).

Let \(A\) be an \(m \times n\) matrix of rank \(r > 0\) and let \(U\) be an echelon form of \(A\). Explain why there exists an invertible matrix \(E\) such that \(A = EU\), and use this factorization to write \(A\) as the sum of \(r\) rank 1 matrices. [Hint: See Theorem 10 in Section 2.4.]

Let \({M_{2 \times 2}}\) be the vector space of all \(2 \times 2\) matrices, and define \(T:{M_{2 \times 2}} \to {M_{2 \times 2}}\) by \(T\left( A \right) = A + {A^T}\), where \(A = \left( {\begin{array}{*{20}{c}}a&b\\c&d\end{array}} \right)\).

  1. Show that \(T\)is a linear transformation.
  2. Let \(B\) be any element of \({M_{2 \times 2}}\) such that \({B^T} = B\). Find an \(A\) in \({M_{2 \times 2}}\) such that \(T\left( A \right) = B\).
  3. Show that the range of \(T\) is the set of \(B\) in \({M_{2 \times 2}}\) with the property that \({B^T} = B\).
  4. Describe the kernel of \(T\).

Consider the following two systems of equations:

\(\begin{array}{c}5{x_1} + {x_2} - 3{x_3} = 0\\ - 9{x_1} + 2{x_2} + 5{x_3} = 1\\4{x_1} + {x_2} - 6{x_3} = 9\end{array}\) \(\begin{array}{c}5{x_1} + {x_2} - 3{x_3} = 0\\ - 9{x_1} + 2{x_2} + 5{x_3} = 5\\4{x_1} + {x_2} - 6{x_3} = 45\end{array}\)

It can be shown that the first system of a solution. Use this fact and the theory from this section to explain why the second system must also have a solution. (Make no row operations.)

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