/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Q2.9-12Q Exercises 9–12 display a matri... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

Exercises 9–12 display a matrix Aand an echelon form of A. Find bases for Col Aand Nul A, and then state the dimensions of these subspaces.

\(A = \left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3\\5&{10}&{ - 9}&{ - 7}&8\\4&8&{ - 9}&{ - 2}&7\\{ - 2}&{ - 4}&5&0&{ - 6}\end{array}} \right] \sim \left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3\\0&0&1&{ - 2}&0\\0&0&0&0&{ - 5}\\0&0&0&0&0\end{array}} \right]\)a

Short Answer

Expert verified

The bases for Col A are \(\left\{ {\left[ {\begin{array}{*{20}{c}}1\\5\\4\\{ - 2}\end{array}} \right],\left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 9}\\{ - 9}\\5\end{array}} \right],\left[ {\begin{array}{*{20}{c}}3\\8\\7\\{ - 6}\end{array}} \right]} \right\}\). The bases for Nul A are \(\left\{ {\left[ {\begin{array}{*{20}{c}}{ - 2}\\1\\0\\0\\0\end{array}} \right],\left[ {\begin{array}{*{20}{c}}5\\0\\2\\1\\0\end{array}} \right]} \right\}\). The dimension of Col A is 3. The dimension of Nul A is 2.

Step by step solution

01

Bases for Nul A and Col A

The set of all linear combinations of the columns of matrix A is Col A, or it is called the column space of A. Pivot columns are the bases for Col A.

The set of all homogeneous equation solutions\(A{\bf{x}} = 0\)is Nul A, or it is called the null space of A.

02

Write the bases for Col A

To identify the pivot and the pivot position, observe the matrix’s leftmost column (nonzero column), which is the pivot column. At the top of this column, 1 is the pivot.

It is observed that the first, third, and fifth columns have pivot elements.

The corresponding columns of matrix A are shown below:

\(\left[ {\begin{array}{*{20}{c}}1\\5\\4\\{ - 2}\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 9}\\{ - 9}\\5\end{array}} \right]\), \(\left[ {\begin{array}{*{20}{c}}3\\8\\7\\{ - 6}\end{array}} \right]\)

The column space is given as shown below:

\({\rm{Col }}A = \left\{ {\left[ {\begin{array}{*{20}{c}}1\\5\\4\\{ - 2}\end{array}} \right],\left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 9}\\{ - 9}\\5\end{array}} \right],\left[ {\begin{array}{*{20}{c}}3\\8\\7\\{ - 6}\end{array}} \right]} \right\}\)

Thus, the bases for Col A are \(\left\{ {\left[ {\begin{array}{*{20}{c}}1\\5\\4\\{ - 2}\end{array}} \right],\left[ {\begin{array}{*{20}{c}}{ - 4}\\{ - 9}\\{ - 9}\\5\end{array}} \right],\left[ {\begin{array}{*{20}{c}}3\\8\\7\\{ - 6}\end{array}} \right]} \right\}\).

03

Write the bases for Nul A

It is given that there are 5 columns in the given matrix, which means there should be 5 entries in vector x.

Thus, the equation \(A{\bf{x}} = 0\) can be written as shown below:

\(\begin{array}{c}Ax = 0\\\left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3\\0&0&1&{ - 2}&0\\0&0&0&0&{ - 5}\\0&0&0&0&0\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\\{{x_4}}\\{{x_5}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}0\\0\\0\\0\end{array}} \right]\end{array}\)

The augmented matrix is shown below:

\(\left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3&0\\0&0&1&{ - 2}&0&0\\0&0&0&0&{ - 5}&0\\0&0&0&0&0&0\end{array}} \right]\)

Multiply row 3 by \( - \frac{1}{5}\).

\(\left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3&0\\0&0&1&{ - 2}&0&0\\0&0&0&0&{ - 5}&0\\0&0&0&0&0&0\end{array}} \right] \sim \left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&3&0\\0&0&1&{ - 2}&0&0\\0&0&0&0&1&0\\0&0&0&0&0&0\end{array}} \right]\)

Add \( - 3\) times row 3 to row 1.

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

Add 4 times row 2 to row 1.

\(\left[ {\begin{array}{*{20}{c}}1&2&{ - 4}&3&0&0\\0&0&1&{ - 2}&0&0\\0&0&0&0&1&0\\0&0&0&0&0&0\end{array}} \right] \sim \left[ {\begin{array}{*{20}{c}}1&2&0&{ - 5}&0&0\\0&0&1&{ - 2}&0&0\\0&0&0&0&1&0\\0&0&0&0&0&0\end{array}} \right]\)

So, the system of equations is as shown below:

\(\begin{array}{c}{x_1} + 2{x_2} - 5{x_4} = 0\\{x_3} - 2{x_4} = 0\\{x_5} = 0\end{array}\)

From the above equations, \({x_1}\), \({x_3}\), and \({x_5}\) correspond to the pivot positions. So, \({x_1}\), \({x_3}\), and \({x_5}\) are the basic variables, and \({x_2}\)and \({x_4}\) are the free variables.

Let \({x_2} = a\), \({x_4} = b\).

Substitute the values \({x_2} = a\) and \({x_4} = b\) in the equation \({x_1} + 2{x_2} - 5{x_4} = 0\) to obtain the general solution.

\(\begin{array}{c}{x_1} + 2\left( a \right) - 5\left( b \right) = 0\\{x_1} = - 2a + 5b\end{array}\)

Substitute the value \({x_4} = b\) in the equation \({x_3} - 2{x_4} = 0\) to obtain the general solution.

\(\begin{array}{c}{x_3} - 2\left( b \right) = 0\\{x_3} = 2b\end{array}\)

Obtain the vector in the parametric form by using \({x_1} = - 2a + 5b\), \({x_2} = a\), \({x_3} = 2b\), \({x_4} = b\), and \({x_5} = 0\).

\[\begin{array}{c}\left[ {\begin{array}{*{20}{c}}{{x_1}}\\{{x_2}}\\{{x_3}}\\{{x_4}}\\{{x_5}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{ - 2a + 5b}\\a\\{2b}\\b\\0\end{array}} \right]\\ = a\left[ {\begin{array}{*{20}{c}}{ - 2}\\1\\0\\0\\0\end{array}} \right] + b\left[ {\begin{array}{*{20}{c}}5\\0\\2\\1\\0\end{array}} \right]\\ = {x_3}\left[ {\begin{array}{*{20}{c}}{ - 2}\\1\\0\\0\\0\end{array}} \right] + {x_5}\left[ {\begin{array}{*{20}{c}}5\\0\\2\\1\\0\end{array}} \right]\end{array}\]

Nul A is shown below:

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

Thus, the bases for Nul A are \(\left\{ {\left[ {\begin{array}{*{20}{c}}{ - 2}\\1\\0\\0\\0\end{array}} \right],\left[ {\begin{array}{*{20}{c}}5\\0\\2\\1\\0\end{array}} \right]} \right\}\).

04

Dimensions of subspaces

It is observed that matrix A has 3 pivot columns; so the dimension of Col A is 3. Thus, Col A= 3.

Also, it is observed that the homogeneous equation \(A{\bf{x}} = 0\) has two free variables; so the dimension of Nul A is 2. Thus, Nul A= 2.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

The solution to the steady-state heat flow problem for
the plate in the figure is approximated by the solution to the
equation\(A{\bf{x}} = {\bf{b}}\);where\(b = \left( {5,15,0,10,0,10,20,30} \right)\)and

\(A = \left[ {\begin{array}{*{20}{c}}4&{ - 1}&{ - 1}&{}&{}&{}&{}&{}\\{ - 1}&4&0&{ - 1}&{}&{}&{}&{}\\{ - 1}&0&4&{ - 1}&{ - 1}&{}&{}&{}\\{}&{ - 1}&{ - 1}&4&0&{ - 1}&{}&{}\\{}&{}&{ - 1}&0&4&{ - 1}&{ - 1}&{}\\{}&{}&{}&{ - 1}&{ - 1}&4&0&{ - 1}\\{}&{}&{}&{}&{ - 1}&0&4&{ - 1}\\{}&{}&{}&{}&{}&{ - 1}&{ - 1}&4\end{array}} \right]\)

(Refer to Exercise 33 of Section 1.1.) The missing entries in Aare zeros. The nonzero entries of A lie within a band along the main diagonal. Such band matricesoccur in a variety of applications and often are extremely large (with thousands of rows and columns but relatively narrow bands).

  1. Use the method of Example 2 to construct an LU factorization of A, and note that both factors are band matrices (with two nonzero diagonals below or above the main diagonal). Compute \(LU - A\) to check your work.
  1. Use the LU factorization to solve\(A{\bf{x}} = {\bf{b}}\).
  1. Obtain \({A^{ - {\bf{1}}}}\) and note that\({A^{ - {\bf{1}}}}\) is a dense matrix with no
    band structure. When Ais large, LandUcan be stored in
    much less space than\({A^{ - {\bf{1}}}}\). This fact is another reason for
    preferring the LU factorization of Ato \({A^{ - {\bf{1}}}}\) itself.

In exercise 5 and 6, compute the product \(AB\) in two ways: (a) by the definition, where \(A{b_{\bf{1}}}\) and \(A{b_{\bf{2}}}\) are computed separately, and (b) by the row-column rule for computing \(AB\).

\(A = \left( {\begin{aligned}{*{20}{c}}{\bf{4}}&{ - {\bf{2}}}\\{ - {\bf{3}}}&{\bf{0}}\\{\bf{3}}&{\bf{5}}\end{aligned}} \right)\), \(B = \left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{3}}\\{\bf{2}}&{ - {\bf{1}}}\end{aligned}} \right)\)

Unless otherwise specified, assume that all matrices in these exercises are \(n \times n\). Determine which of the matrices in Exercises 1-10 are invertible. Use a few calculations as possible. Justify your answer.

10. [M] \[\left[ {\begin{array}{*{20}{c}}5&3&1&7&9\\6&4&2&8&{ - 8}\\7&5&3&{10}&9\\9&6&4&{ - 9}&{ - 5}\\8&5&2&{11}&4\end{array}} \right]\]

In Exercises 1–9, assume that the matrices are partitioned conformably for block multiplication. Compute the products shown in Exercises 1–4.

1. \(\left[ {\begin{array}{*{20}{c}}I&{\bf{0}}\\E&I\end{array}} \right]\left[ {\begin{array}{*{20}{c}}A&B\\C&D\end{array}} \right]\)

Consider the following geometric 2D transformations: D, a dilation (in which x-coordinates and y-coordinates are scaled by the same factor); R, a rotation; and T a translation. Does D commute with R? That is, is \(D\left( {R\left( {\bf{x}} \right)} \right) = R\left( {D\left( {\bf{x}} \right)} \right)\)for all \({\bf{x}}\) in \({\mathbb{R}^{\bf{2}}}\)? Does D commute with T? Does R commute with T?

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.