In Exercises 1-4, assume that the matrix A is row equivalent to B. Without calculations, list rank A and dim Nul A. Then find bases for Col A, Row A, and Nul A.

\[A = \left[ {\begin{array}{*{20}{c}}{\bf{2}}&{ - {\bf{3}}}&{\bf{6}}&{\bf{2}}&{\bf{5}}\\{ - {\bf{2}}}&{\bf{3}}&{ - {\bf{3}}}&{ - {\bf{3}}}&{ - {\bf{4}}}\\{\bf{4}}&{ - {\bf{6}}}&{\bf{9}}&{\bf{5}}&{\bf{9}}\\{ - {\bf{2}}}&{\bf{3}}&{\bf{3}}&{ - {\bf{4}}}&{\bf{1}}\end{array}} \right]\]

\[B = \left[ {\begin{array}{*{20}{c}}{\bf{2}}&{ - {\bf{3}}}&{\bf{6}}&{\bf{2}}&{\bf{5}}\\{\bf{0}}&{\bf{0}}&{\bf{3}}&{ - {\bf{1}}}&{\bf{1}}\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{\bf{1}}&{\bf{3}}\\{\bf{0}}&{\bf{0}}&{\bf{0}}&{\bf{0}}&{\bf{0}}\end{array}} \right]\]

Short Answer

Expert verified

Rank A=3, dim Nul A=2, \({\rm{Basis}}\left( {{\rm{Col}}\,A} \right) = \left\{ {\left[ {\begin{array}{*{20}{c}}2\\{ - 2}\\4\\{ - 2}\end{array}} \right],\;\;\left[ {\begin{array}{*{20}{c}}6\\{ - 3}\\9\\3\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}2\\{ - 3}\\5\\{ - 4}\end{array}} \right]} \right\}\), \({\rm{basis}}\left( {{\rm{Row}}\,A} \right) = \left\{ {\left[ {\begin{array}{*{20}{c}}2&{ - 3}&6&2&5\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}0&0&3&{ - 1}&{ - 1}\end{array}} \right],\,\left[ {\begin{array}{*{20}{c}}0&0&0&1&3\end{array}} \right]} \right\}\), \[\left\{ {\left[ {\begin{array}{*{20}{c}}{\frac{3}{2}}\\1\\0\\0\\0\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}{\frac{9}{2}}\\0\\{ - \frac{4}{3}}\\{ - 3}\\1\end{array}} \right]} \right\}\]

Step by step solution

01

Find the rank of A

In the row equivalent matrix B, there are three non-zero rows. Therefore, the rank of A is 3.

02

Find dim Nul A

Using the rank theorem, you get:

\(\begin{array}{c}{\rm{rank}}\,A + \dim \,{\rm{Nul}}A = n\\3 + \dim \;{\rm{Nul}}\,A = 5\\\dim \;{\rm{Nul}}\,A = 5 - 3\\ = 2\end{array}\)

03

Find the basis of Col A

From matrix B:

\(B = \left[ {\begin{array}{*{20}{c}}2&{ - 3}&6&2&5\\0&0&3&{ - 1}&1\\0&0&0&1&3\\0&0&0&0&0\end{array}} \right]\)

The basis of Col A can be written as:

\({\rm{Basis}}\left( {{\rm{Col}}\,A} \right) = \left\{ {\left[ {\begin{array}{*{20}{c}}2\\{ - 2}\\4\\{ - 2}\end{array}} \right],\;\;\left[ {\begin{array}{*{20}{c}}6\\{ - 3}\\9\\3\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}2\\{ - 3}\\5\\{ - 4}\end{array}} \right]} \right\}\)

04

Find the basis of row A

The basis of the row space of A can be written as:

\({\rm{basis}}\left( {{\rm{Row}}\,A} \right) = \left\{ {\left[ {\begin{array}{*{20}{c}}2&{ - 3}&6&2&5\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}0&0&3&{ - 1}&{ - 1}\end{array}} \right],\,\left[ {\begin{array}{*{20}{c}}0&0&0&1&3\end{array}} \right]} \right\}\)

05

Find the basis of row A

Write the augmented matrix for the system \(B{\bf{x}} = 0\).

\(M = \left[ {\begin{array}{*{20}{c}}2&{ - 3}&6&2&5&0\\0&0&3&{ - 1}&1&0\\0&0&0&1&3&0\\0&0&0&0&0&0\end{array}} \right]\)

Write the row reduced echelon form of matrix M.

\(M = \left[ {\begin{array}{*{20}{c}}1&{\frac{{ - 3}}{2}}&0&0&{ - \frac{9}{2}}&0\\0&0&1&0&{\frac{4}{3}}&0\\0&0&0&1&3&0\\0&0&0&0&0&0\end{array}} \right]\)

Write the system of equations.

\(\begin{array}{c}{x_1} = \frac{3}{2}{x_2} + \frac{9}{2}{x_5}\\{x_3} = - \frac{4}{3}{x_5}\\{x_4} = - 3{x_5}\end{array}\)

Consider \({x_2}\) and \({x_5}\) as free variables.

\[\begin{array}{c}\left[ {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\\{{x_4}}\\{{x_5}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\frac{3}{2}{x_2} + \frac{9}{2}{x_5}}\\{{x_2}}\\{ - \frac{4}{3}{x_5}}\\{ - 3{x_5}}\\{{x_5}}\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}{\frac{3}{2}}\\1\\0\\0\\0\end{array}} \right]{x_2} + \left[ {\begin{array}{*{20}{c}}{\frac{9}{2}}\\0\\{ - \frac{4}{3}}\\{ - 3}\\1\end{array}} \right]{x_5}\end{array}\]

So, the null space of A is \[\left\{ {\left[ {\begin{array}{*{20}{c}}{\frac{3}{2}}\\1\\0\\0\\0\end{array}} \right],\,\,\left[ {\begin{array}{*{20}{c}}{\frac{9}{2}}\\0\\{ - \frac{4}{3}}\\{ - 3}\\1\end{array}} \right]} \right\}\].

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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 \({M_{2 \times 2}}\) 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 \(T\).

If A is a \({\bf{4}} \times {\bf{3}}\) matrix, what is the largest possible dimension of the row space of A? If Ais a \({\bf{3}} \times {\bf{4}}\) matrix, what is the largest possible dimension of the row space of A? Explain.

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

15. Let\(A\)be an\(m \times n\)matrix, and let\(B\)be a\(n \times p\)matrix such that\(AB = 0\). Show that\({\mathop{\rm rank}\nolimits} A + {\mathop{\rm rank}\nolimits} B \le n\). (Hint: One of the four subspaces\({\mathop{\rm Nul}\nolimits} A\),\({\mathop{\rm Col}\nolimits} A,\,{\mathop{\rm Nul}\nolimits} B\), and\({\mathop{\rm Col}\nolimits} B\)is contained in one of the other three subspaces.)

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 the coordinate mapping is one to one. (Hint: Suppose \({\left( {\bf{u}} \right)_B} = {\left( {\bf{w}} \right)_B}\) for some u and w in V, and show that \({\bf{u}} = {\bf{w}}\)).

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