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In Exercises 21 and 22, mark each statement True and False. Justify each answer.

a. A subset H of \({\mathbb{R}^n}\) is a subspace if the zero vector is in H.

b. Given vectors \({{\bf{v}}_{\bf{1}}}\),…..,\({{\bf{v}}_p}\) in \({\mathbb{R}^n}\), the set of all linear combinations of these vectors is a subspace of \({\mathbb{R}^n}\).

c. The null space of an \(m \times n\) matrix is a subspace of \({\mathbb{R}^n}\).

d. The column space of matrix A is set of solutions of \(A{\bf{x}} = {\bf{b}}\).

e. If B is an echelon form of a matrix A, then the pivot columns of B form a basis for col A.

Short Answer

Expert verified

a. The given statement is false

b. The given statement is true.

c. The given statement is true.

d. The given statement is false.

e. The given statement is false.

Step by step solution

01

Check for statement (a)

The zero vector is a condition for the subspace but it is not the only one. Therefore, the statement is false.

02

Check for statement (b)

\(x = \sum\limits_{i = 1}^p {{c_i}{v_i}} \)

\(y = \sum\limits_{i = 1}^p {{d_i}{v_i}} \)

Check the vector addition:

\(\begin{array}{c}x + y = \sum\limits_{i = 1}^p {{c_i}{v_i}} + \sum\limits_{i = 1}^p {{d_i}{v_i}} \\c = \sum\limits_{i = 1}^p {\left( {{c_i} + {d_i}} \right){v_i} \in H} \end{array}\)

Multiply the vector by x:

\(cx = \sum\limits_{i = 1}^p {{{\left( {cc} \right)}_i}{v_i} \in H} \)

As both properties are satisfied, the given statement is true.

03

Check for statement (c)

A non-empty subset H of \({\mathbb{R}^n}\) if an only if \(x + y\), \(cx \in H\)for all \(x,y \in H\), \(c \in \mathbb{R}\).

Check for zero vector.

As \(A\left( 0 \right) = 0\), the zero-vector condition is satisfied.

Similarly, \(A\left( {x + y} \right) = Ax + Ay\). Therefore, vector additionis satisfied.

For \(A\left( {cx} \right) = cAx\). Therefore, scalar multiplicationis satisfied.

Thus, the statement is true.

04

Check for statement (d)

Suppose an identity matrix of orders n and \(b\) is

\(b = \left( {\begin{array}{*{20}{c}}1\\1\\ \vdots \\1\end{array}} \right)\).

The column vectors of A are

\({e_1} = \left( {\begin{array}{*{20}{c}}1\\0\\0\\ \vdots \\0\end{array}} \right)\), \({e_2} = \left( {\begin{array}{*{20}{c}}0\\1\\0\\0\\ \vdots \\0\end{array}} \right)\), \[{e_n} = \left( {\begin{array}{*{20}{c}}0\\0\\ \vdots \\0\\1\end{array}} \right)\].

Since span \(\left\{ {{e_1},{e_2},\,....,{e_n}} \right\} = {\mathbb{R}^n}\), every vector in \({\mathbb{R}^n}\) is an element of the column space of A. The solution set of \(A{\bf{x}} = {\bf{b}}\) is \(\left\{ b \right\}\) but \({\mathbb{R}^n}\) is not \(\left\{ b \right\}\). So, the given statement is false.

05

Check for statement (e)

The pivot column of matrix A forms a basis and so the columns of B. As the columns of A and B are the same, the pivot columns of B from a basis of Col B. But the column of echelon form are often not in the column space A.

Thus, the statement is False.

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Most popular questions from this chapter

Prove Theorem 2(d). (Hint: The \(\left( {i,j} \right)\)- entry in \(\left( {rA} \right)B\) is \(\left( {r{a_{i1}}} \right){b_{1j}} + ... + \left( {r{a_{in}}} \right){b_{nj}}\).)

Suppose Ais an \(n \times n\) matrix with the property that the equation \[A{\mathop{\rm x}\nolimits} = 0\] has at least one solution for each b in \({\mathbb{R}^n}\). Without using Theorem 5 or 8, explain why each equation Ax = b has in fact exactly one solution.

Let \({{\bf{r}}_1} \ldots ,{{\bf{r}}_p}\) be vectors in \({\mathbb{R}^{\bf{n}}}\), and let Qbe an\(m \times n\)matrix. Write the matrix\(\left( {\begin{aligned}{*{20}{c}}{Q{{\bf{r}}_1}}& \cdots &{Q{{\bf{r}}_p}}\end{aligned}} \right)\)as a productof two matrices (neither of which is an identity matrix).

Suppose \({A_{{\bf{11}}}}\) is invertible. Find \(X\) and \(Y\) such that

\[\left[ {\begin{array}{*{20}{c}}{{A_{{\bf{11}}}}}&{{A_{{\bf{12}}}}}\\{{A_{{\bf{21}}}}}&{{A_{{\bf{22}}}}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}I&{\bf{0}}\\X&I\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{{A_{{\bf{11}}}}}&{\bf{0}}\\{\bf{0}}&S\end{array}} \right]\left[ {\begin{array}{*{20}{c}}I&Y\\{\bf{0}}&I\end{array}} \right]\]

Where \(S = {A_{{\bf{22}}}} - {A_{21}}A_{{\bf{11}}}^{ - {\bf{1}}}{A_{{\bf{12}}}}\). The matrix \(S\) is called the Schur complement of \({A_{{\bf{11}}}}\). Likewise, if \({A_{{\bf{22}}}}\) is invertible, the matrix \({A_{{\bf{11}}}} - {A_{{\bf{12}}}}A_{{\bf{22}}}^{ - {\bf{1}}}{A_{{\bf{21}}}}\) is called the Schur complement of \({A_{{\bf{22}}}}\). Such expressions occur frequently in the theory of systems engineering, and elsewhere.

In Exercise 9 mark each statement True or False. Justify each answer.

9. a. In order for a matrix B to be the inverse of A, both equations \(AB = I\) and \(BA = I\) must be true.

b. If A and B are \(n \times n\) and invertible, then \({A^{ - {\bf{1}}}}{B^{ - {\bf{1}}}}\) is the inverse of \(AB\).

c. If \(A = \left( {\begin{aligned}{*{20}{c}}a&b\\c&d\end{aligned}} \right)\) and \(ab - cd \ne {\bf{0}}\), then A is invertible.

d. If A is an invertible \(n \times n\) matrix, then the equation \(Ax = b\) is consistent for each b in \({\mathbb{R}^{\bf{n}}}\).

e. Each elementary matrix is invertible.

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