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Let \(A = \left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{1}}&{\bf{1}}&{\bf{0}}\\{\bf{0}}&{\bf{1}}&{\bf{1}}&{\bf{1}}\end{aligned}} \right)\). Construct a \({\bf{4}} \times {\bf{2}}\) matrix \(D\) using only \({\bf{1}}\) and \({\bf{0}}\) as enteries, such that \(AD = {I_{\bf{2}}}\). It is possible that \(CA = {I_{\bf{4}}}\) for some \({\bf{4}} \times {\bf{2}}\) matrix \(C\)? Why or why not?

Short Answer

Expert verified

(D = \left( {\begin{aligned}{*{20}{c}}1&0\\0&0\\0&0\\0&1\end{aligned}} \right)\) and matrix \(C\) is not defined as the column vectors of \(A\) are linearly independent.

Step by step solution

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01

Construct matrix \(D\) using the elements \({\bf{1}}\) and \({\bf{0}}\)

The first column of the matrix \(D\) is \(\left( {\begin{aligned}{*{20}{c}}1\\0\\0\\0\end{aligned}} \right)\).

When matrix \(A\) is multiplied by the column of \(D\), the first row of \(AD\) becomes \(\left( {\begin{aligned}{*{20}{c}}1&0\end{aligned}} \right)\).

02

Construct matrix \(D\) using the elements \({\bf{1}}\) and \({\bf{0}}\)

The second column of matrix \(D\) is \(\left( {\begin{aligned}{*{20}{c}}0\\0\\0\\1\end{aligned}} \right)\).

03

Find matrix \(C\)

If \(CA = {I_4}\), then \(CA{\bf{x}}\) is equal to \({\bf{x}}\) in \({\mathbb{R}^4}\). As the columns in \(A\) are linearly independent and \(A{\bf{x}} = 0\) for a non-zero vector \({\bf{x}}\), \(CA{\bf{x}}\) is not equal to \({\bf{x}}\).

So, \(D = \left( {\begin{aligned}{*{20}{c}}1&0\\0&0\\0&0\\0&1\end{aligned}} \right)\) and matrix \(C\) are not defined as the column vectors of \(A\) are linearly independent.

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Most popular questions from this chapter

Suppose \(CA = {I_n}\)(the \(n \times n\) identity matrix). Show that the equation \(Ax = 0\) has only the trivial solution. Explain why Acannot have more columns than rows.

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.

Suppose \(\left( {B - C} \right)D = 0\), where Band Care \(m \times n\) matrices and \(D\) is invertible. Show that B = C.

Let T be a linear transformation that maps \({\mathbb{R}^n}\) onto \({\mathbb{R}^n}\). Is \({T^{ - 1}}\) also one-to-one?

Exercises 15 and 16 concern arbitrary matrices A, B, and Cfor which the indicated sums and products are defined. Mark each statement True or False. Justify each answer.

15. a. If A and B are \({\bf{2}} \times {\bf{2}}\) with columns \({{\bf{a}}_1},{{\bf{a}}_2}\) and \({{\bf{b}}_1},{{\bf{b}}_2}\) respectively, then \(AB = \left( {\begin{aligned}{*{20}{c}}{{{\bf{a}}_1}{{\bf{b}}_1}}&{{{\bf{a}}_2}{{\bf{b}}_2}}\end{aligned}} \right)\).

b. Each column of ABis a linear combination of the columns of Busing weights from the corresponding column of A.

c. \(AB + AC = A\left( {B + C} \right)\)

d. \({A^T} + {B^T} = {\left( {A + B} \right)^T}\)

e. The transpose of a product of matrices equals the product of their transposes in the same order.

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