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Use partitioned matrices to prove by induction that for \(n = 2,3,...\), the \(n \times n\) matrices \(A\) shown below is invertible and \(B\) is its inverse.

\[A = \left[ {\begin{array}{*{20}{c}}1&0&0& \cdots &0\\1&1&0&{}&0\\1&1&1&{}&0\\ \vdots &{}&{}& \ddots &{}\\1&1&1& \ldots &1\end{array}} \right]\]

\[B = \left[ {\begin{array}{*{20}{c}}1&0&0& \cdots &0\\{ - 1}&1&0&{}&0\\0&{ - 1}&1&{}&0\\ \vdots &{}& \ddots & \ddots &{}\\0&{}& \ldots &{ - 1}&1\end{array}} \right]\]

For the induction step, assume A and Bare \(\left( {k + 1} \right) \times \left( {k + 1} \right)\) matrices, and partition Aand B in a form similar to that displayed in Exercises 23.

Short Answer

Expert verified

It is proved that by induction, the \(n \times n\) matrices are invertible and \(B\) is their inverse.

Step by step solution

01

Identify partitions A and B in exercise 23

The partition matrices are \({A_1} = \left[ {\begin{array}{*{20}{c}}a&{{0^T}}\\0&A\end{array}} \right],{B_1} = \left[ {\begin{array}{*{20}{c}}b&{{0^T}}\\0&A\end{array}} \right]\).

Here, v and w are in \({\mathbb{R}^k}\); \(A\) and B are \(k \times k\) lower triangular matrices, and \(a\), \(b\) are scalars.

02

Show by induction that \(n \times n\) matrices A are invertible and B is their inverse

Consider the matrices \[{A_n} = \left[ {\begin{array}{*{20}{c}}1&0&0& \cdots &0\\1&1&0&{}&0\\1&1&1&{}&0\\ \vdots &{}&{}& \ddots &{}\\1&1&1& \ldots &1\end{array}} \right]\] and \[{B_n} = \left[ {\begin{array}{*{20}{c}}1&0&0& \cdots &0\\{ - 1}&1&0&{}&0\\0&{ - 1}&1&{}&0\\ \vdots &{}& \ddots & \ddots &{}\\0&{}& \ldots &{ - 1}&1\end{array}} \right]\].

As a result of direct calculation, \({A_2}{B_2} = {I_2}\). Suppose that for \[{\mathop{\rm n}\nolimits} = k\], the matrix \({A_k}{B_k}\) is \({I_k}\) , and it is written as

\({A_{k + 1}} = \left[ {\begin{array}{*{20}{c}}1&{{0^T}}\\{\mathop{\rm v}\nolimits} &{{A_k}}\end{array}} \right]\)and \({B_{k + 1}} = \left[ {\begin{array}{*{20}{c}}1&{{0^T}}\\{\mathop{\rm v}\nolimits} &{{B_k}}\end{array}} \right]\).

Here, \({\mathop{\rm v}\nolimits} \) and \({\mathop{\rm w}\nolimits} \) are in \({\mathbb{R}^k}\) , \({v^T} = \left[ {\begin{array}{*{20}{c}}1&1&{...}&1\end{array}} \right]\) and \({w^T} = \left[ {\begin{array}{*{20}{c}}{ - 1}&0&{...}&0\end{array}} \right]\). Hence,

\[\begin{array}{c}{A_{k + 1}}{B_{k + 1}} = \left[ {\begin{array}{*{20}{c}}1&{{0^T}}\\{\mathop{\rm v}\nolimits} &{{A_k}}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}1&{{0^T}}\\{\mathop{\rm w}\nolimits} &{{A_k}}\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}{1 + {0^T}{\mathop{\rm w}\nolimits} }&{{0^T} + {0^T}{B_K}}\\{{\mathop{\rm v}\nolimits} + {A_K}{\mathop{\rm w}\nolimits} }&{{\mathop{\rm v}\nolimits} {0^T} + {A_K}{B_K}}\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}1&{{0^T}}\\0&{{I_k}}\end{array}} \right]\\ = {I_{k + 1}}.\end{array}\]

The \(\left( {2,1} \right)\)-entry is 0 since v equals the first column of \({A_k}\). \({A_k}{\mathop{\rm w}\nolimits} \) is \( - 1\) times the first column of \({A_k}\). For all \(n \ge 2\), \({A_n}{B_n} = {I_n}\) according to the principle of induction. The invertible matrix theorem demonstrates that these matrices are invertible because \({A_n}\) and \({B_n}\) are square matrices. Thus, \({B_n} = A_n^{ - 1}\).

Hence, it is proved that by induction, the \(n \times n\) matrices are invertible and \(B\)is their inverse.

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Most popular questions from this chapter

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.

Solve the equation \(AB = BC\) for A, assuming that A, B, and C are square and Bis invertible.

Suppose Ais \(n \times n\) and the equation \(A{\bf{x}} = {\bf{0}}\) has only the trivial solution. Explain why Ahas npivot columns and Ais row equivalent to \({I_n}\). By Theorem 7, this shows that Amust be invertible. (This exercise and Exercise 24 will be cited in Section 2.3.)

Suppose the third column of Bis the sum of the first two columns. What can you say about the third column of AB? Why?

Generalize the idea of Exercise 21(a) [not 21(b)] by constructing a \(5 \times 5\) matrix \(M = \left[ {\begin{array}{*{20}{c}}A&0\\C&D\end{array}} \right]\) such that \({M^2} = I\). Make C a nonzero \(2 \times 3\) matrix. Show that your construction works.

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