Let \(D = \left\{ {{{\mathop{\rm d}\nolimits} _1},{{\mathop{\rm d}\nolimits} _2},{{\mathop{\rm d}\nolimits} _3}} \right\}\) and \(F = \left\{ {{{\mathop{\rm f}\nolimits} _1},{{\mathop{\rm f}\nolimits} _2},{{\mathop{\rm f}\nolimits} _3}} \right\}\) be bases for a vector space \(V\), and suppose \({{\mathop{\rm f}\nolimits} _1} = 2{{\mathop{\rm d}\nolimits} _1} - {{\mathop{\rm d}\nolimits} _2} + {{\mathop{\rm d}\nolimits} _3}\), \({{\mathop{\rm f}\nolimits} _2} = 3{{\mathop{\rm d}\nolimits} _2} + {{\mathop{\rm d}\nolimits} _3}\) and \({{\mathop{\rm f}\nolimits} _3} = - 3{{\mathop{\rm d}\nolimits} _1} + 2{{\mathop{\rm d}\nolimits} _3}\).

a. Find the change-of-coordinates matrix from \(F\) to \(D\).

b. Find \({\left[ {\mathop{\rm x}\nolimits} \right]_D}\) for \({\mathop{\rm x}\nolimits} = {{\mathop{\rm f}\nolimits} _1} - 2{{\mathop{\rm f}\nolimits} _2} + 2{{\mathop{\rm f}\nolimits} _3}\).

Short Answer

Expert verified
  1. The change-of-coordinates matrix from \(F\) to \(D\) is \(\mathop P\limits_{D \leftarrow F} = \left[ {\begin{array}{*{20}{c}}2&0&{ - 3}\\{ - 1}&3&0\\1&1&2\end{array}} \right]\).
  2. The \(D - \)coordinate vector is \({\left[ {\mathop{\rm x}\nolimits} \right]_D} = \left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 7}\\3\end{array}} \right]\).

Step by step solution

01

State the change-of-coordinate matrix

Let \(B = \left\{ {{{\mathop{\rm b}\nolimits} _1},...,{{\mathop{\rm b}\nolimits} _n}} \right\}\) and \(C = \left\{ {{{\mathop{\rm c}\nolimits} _1},...,{{\mathop{\rm c}\nolimits} _n}} \right\}\) be bases of a vector space \(V\). Then according to Theorem 15,there is a unique \(n \times n\) matrix \(\mathop P\limits_{C \leftarrow B} \) such that \({\left[ {\mathop{\rm x}\nolimits} \right]_C} = \mathop P\limits_{C \leftarrow B} {\left[ {\mathop{\rm x}\nolimits} \right]_B}\).

The columns of \(\mathop P\limits_{C \leftarrow B} \) are the \(C - \)coordinate vectors of the vectors in the basis \(B\). That is, \(\mathop P\limits_{C \leftarrow B} = \left[ {\begin{array}{*{20}{c}}{{{\left[ {{{\mathop{\rm b}\nolimits} _1}} \right]}_C}}&{{{\left[ {{{\mathop{\rm b}\nolimits} _2}} \right]}_C}}& \cdots &{{{\left[ {{{\mathop{\rm b}\nolimits} _n}} \right]}_C}}\end{array}} \right]\).

02

Determine the change-of-coordinate matrix from \(F\) to \(D\)

a)

It is given that \({{\mathop{\rm f}\nolimits} _1} = 2{{\mathop{\rm d}\nolimits} _1} - {{\mathop{\rm d}\nolimits} _2} + {{\mathop{\rm d}\nolimits} _3},{{\mathop{\rm f}\nolimits} _2} = 3{{\mathop{\rm d}\nolimits} _2} + {{\mathop{\rm d}\nolimits} _3}\ and \({{\mathop{\rm f}\nolimits} _3} = - 3{{\mathop{\rm d}\nolimits} _1} + 2{{\mathop{\rm d}\nolimits} _3}\). Then, \({\left[ {{{\mathop{\rm f}\nolimits} _1}} \right]_D} = \left[ {\begin{array}{*{20}{c}}2\\{ - 1}\\1\end{array}} \right],{\left[ {{{\mathop{\rm f}\nolimits} _2}} \right]_D} = \left[ {\begin{array}{*{20}{c}}0\\3\\1\end{array}} \right],{\left[ {{{\mathop{\rm f}\nolimits} _3}} \right]_D} = \left[ {\begin{array}{*{20}{c}}{ - 3}\\0\\2\end{array}} \right]\).

\(\begin{aligned} \mathop P\limits_{D \leftarrow F} &= \left[ {\begin{array}{*{20}{c}}{{{\left[ {{{\mathop{\rm f}\nolimits} _1}} \right]}_D}}&{{{\left[ {{{\mathop{\rm f}\nolimits} _2}} \right]}_D}}&{{{\left[ {{{\mathop{\rm f}\nolimits} _3}} \right]}_D}}\end{array}} \right]\\ &= \left[ {\begin{array}{*{20}{c}}2&0&{ - 3}\\{ - 1}&3&0\\1&1&2\end{array}} \right]\end{aligned}\)

Thus, the change-of-coordinates matrix from \(F\) to \(D\) is \(\mathop P\limits_{D \leftarrow F} = \left[ {\begin{array}{*{20}{c}}2&0&{ - 3}\\{ - 1}&3&0\\1&1&2\end{array}} \right]\).

03

Determine \({\left[ {\mathop{\rm x}\nolimits}   \right]_D}\) for \({\mathop{\rm x}\nolimits}  = {{\mathop{\rm f}\nolimits} _1} - 2{{\mathop{\rm f}\nolimits} _2} + 2{{\mathop{\rm f}\nolimits} _3}\)

b)

It is given that \({\mathop{\rm x}\nolimits} = {{\mathop{\rm f}\nolimits} _1} - 2{{\mathop{\rm f}\nolimits} _2} + 2{{\mathop{\rm f}\nolimits} _3}\), then \({\left[ {\mathop{\rm x}\nolimits} \right]_F} = \left[ {\begin{array}{*{20}{c}}1\\{ - 2}\\2\end{array}} \right]\).

Use part (a) to compute the D-coordinate vector.

\(\begin{aligned} {\left[ {\mathop{\rm x}\nolimits} \right]_D} &= \mathop P\limits_{D \leftarrow F} {\left[ {\mathop{\rm x}\nolimits} \right]_F}\\ &= \left[ {\begin{array}{*{20}{c}}2&0&{ - 3}\\{ - 1}&3&0\\1&1&2\end{array}} \right]\left[ {\begin{array}{*{20}{c}}1\\{ - 2}\\2\end{array}} \right]\\ &= \left[ {\begin{array}{*{20}{c}}{2 + 0 - 6}\\{ - 1 - 6 + 0}\\{1 - 2 + 4}\end{array}} \right]\\ &= \left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 7}\\3\end{array}} \right]\end{aligned}\)

Therefore, the \(D - \)coordinate vector is \({\left[ {\mathop{\rm x}\nolimits} \right]_D} = \left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 7}\\3\end{array}} \right]\).

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Most popular questions from this chapter

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

17. A submatrix of a matrix A is any matrix that results from deleting some (or no) rows and/or columns of A. It can be shown that A has rank \(r\) if and only if A contains an invertible \(r \times r\) submatrix and no longer square submatrix is invertible. Demonstrate part of this statement by explaining (a) why an \(m \times n\) matrix A of rank \(r\) has an \(m \times r\) submatrix \({A_1}\) of rank \(r\), and (b) why \({A_1}\) has an invertible \(r \times r\) submatrix \({A_2}\).

The concept of rank plays an important role in the design of engineering control systems, such as the space shuttle system mentioned in this chapter’s introductory example. A state-space model of a control system includes a difference equation of the form

\({{\mathop{\rm x}\nolimits} _{k + 1}} = A{{\mathop{\rm x}\nolimits} _k} + B{{\mathop{\rm u}\nolimits} _k}\)for \(k = 0,1,....\) (1)

Where \(A\) is \(n \times n\), \(B\) is \(n \times m\), \(\left\{ {{{\mathop{\rm x}\nolimits} _k}} \right\}\) is a sequence of “state vectors” in \({\mathbb{R}^n}\) that describe the state of the system at discrete times, and \(\left\{ {{{\mathop{\rm u}\nolimits} _k}} \right\}\) is a control, or input, sequence. The pair \(\left( {A,B} \right)\) is said to be controllable if

\({\mathop{\rm rank}\nolimits} \left( {\begin{array}{*{20}{c}}B&{AB}&{{A^2}B}& \cdots &{{A^{n - 1}}B}\end{array}} \right) = n\) (2)

The matrix that appears in (2) is called the controllability matrix for the system. If \(\left( {A,B} \right)\) is controllable, then the system can be controlled, or driven from the state 0 to any specified state \({\mathop{\rm v}\nolimits} \) (in \({\mathbb{R}^n}\)) in at most \(n\) steps, simply by choosing an appropriate control sequence in \({\mathbb{R}^m}\). This fact is illustrated in Exercise 18 for \(n = 4\) and \(m = 2\). For a further discussion of controllability, see this text’s website (Case study for Chapter 4).

Let S be a maximal linearly independent subset of a vector space V. In other words, S has the property that if a vector not in S is adjoined to S, the new set will no longer be linearly independent. Prove that S must be a basis of V. [Hint: What if S were linearly independent but not a basis of V?]

(M) Determine whether w is in the column space of \(A\), the null space of \(A\), or both, where

\({\mathop{\rm w}\nolimits} = \left( {\begin{array}{*{20}{c}}1\\2\\1\\0\end{array}} \right),A = \left( {\begin{array}{*{20}{c}}{ - 8}&5&{ - 2}&0\\{ - 5}&2&1&{ - 2}\\{10}&{ - 8}&6&{ - 3}\\3&{ - 2}&1&0\end{array}} \right)\)

(M) Let \(H = {\mathop{\rm Span}\nolimits} \left\{ {{{\mathop{\rm v}\nolimits} _1},{{\mathop{\rm v}\nolimits} _2}} \right\}\) and \(K = {\mathop{\rm Span}\nolimits} \left\{ {{{\mathop{\rm v}\nolimits} _3},{{\mathop{\rm v}\nolimits} _4}} \right\}\), where

\({{\mathop{\rm v}\nolimits} _1} = \left( {\begin{array}{*{20}{c}}5\\3\\8\end{array}} \right),{{\mathop{\rm v}\nolimits} _2} = \left( {\begin{array}{*{20}{c}}1\\3\\4\end{array}} \right),{{\mathop{\rm v}\nolimits} _3} = \left( {\begin{array}{*{20}{c}}2\\{ - 1}\\5\end{array}} \right),{{\mathop{\rm v}\nolimits} _4} = \left( {\begin{array}{*{20}{c}}0\\{ - 12}\\{ - 28}\end{array}} \right)\)

Then \(H\) and \(K\) are subspaces of \({\mathbb{R}^3}\). In fact, \(H\) and \(K\) are planes in \({\mathbb{R}^3}\) through the origin, and they intersect in a line through 0. Find a nonzero vector w that generates that line. (Hint: w can be written as \({c_1}{{\mathop{\rm v}\nolimits} _1} + {c_2}{{\mathop{\rm v}\nolimits} _2}\) and also as \({c_3}{{\mathop{\rm v}\nolimits} _3} + {c_4}{{\mathop{\rm v}\nolimits} _4}\). To build w, solve the equation \({c_1}{{\mathop{\rm v}\nolimits} _1} + {c_2}{{\mathop{\rm v}\nolimits} _2} = {c_3}{{\mathop{\rm v}\nolimits} _3} + {c_4}{{\mathop{\rm v}\nolimits} _4}\) for the unknown \({c_j}'{\mathop{\rm s}\nolimits} \).)

If A is a \({\bf{6}} \times {\bf{8}}\) matrix, what is the smallest possible dimension of Null A?

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