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 linear transformation \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) has the property that \(T\left( {\mathop{\rm u}\nolimits} \right) = T\left( {\mathop{\rm v}\nolimits} \right)\) for some pair of distinct vectors u and v in \({\mathbb{R}^n}\). Can Tmap \({\mathbb{R}^n}\) onto \({\mathbb{R}^n}\)? Why or why not?

[M] Suppose memory or size restrictions prevent your matrix program from working with matrices having more than 32 rows and 32 columns, and suppose some project involves \(50 \times 50\) matrices A and B. Describe the commands or operations of your program that accomplish the following tasks.

a. Compute \(A + B\)

b. Compute \(AB\)

c. Solve \(Ax = b\) for some vector b in \({\mathbb{R}^{50}}\), assuming that \(A\) can be partitioned into a \(2 \times 2\) block matrix \(\left[ {{A_{ij}}} \right]\), with \({A_{11}}\) an invertible \(20 \times 20\) matrix, \({A_{22}}\) an invertible \(30 \times 30\) matrix, and \({A_{12}}\) a zero matrix. [Hint: Describe appropriate smaller systems to solve, without using any matrix inverse.]

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.

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.

Suppose Tand U are linear transformations from \({\mathbb{R}^n}\) to \({\mathbb{R}^n}\) such that \(T\left( {U{\mathop{\rm x}\nolimits} } \right) = {\mathop{\rm x}\nolimits} \) for all x in \({\mathbb{R}^n}\) . Is it true that \(U\left( {T{\mathop{\rm x}\nolimits} } \right) = {\mathop{\rm x}\nolimits} \) for all x in \({\mathbb{R}^n}\)? Why or why not?

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