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Repeat the strategy of Exercise 33 to guess the inverse of \(A = \left( {\begin{aligned}{*{20}{c}}1&0&0& \cdots &0\\1&2&0&{}&0\\1&2&3&{}&0\\ \vdots &{}&{}& \ddots & \vdots \\1&2&3& \cdots &n\end{aligned}} \right)\). Prove that your guess is correct.

Short Answer

Expert verified

\(\left( {\begin{aligned}{*{20}{c}}1&0&0& \cdots &0\\{ - \frac{1}{2}}&{\frac{1}{2}}&0&{}&{}\\0&{ - \frac{1}{3}}&{\frac{1}{3}}&{}&{}\\ \vdots &{}& \ddots & \ddots & \vdots \\0&0& \cdots &{ - \frac{1}{n}}&{\frac{1}{n}}\end{aligned}} \right)\)

Step by step solution

01

Guess matrix \(B\)

Suppose the inverse matrix of \(A\) is \(B\). Matrix \(B\) of order \(n \times n\) can be expressed as

\(B = \left( {\begin{aligned}{*{20}{c}}1&0&0& \cdots &0\\{ - \frac{1}{2}}&{\frac{1}{2}}&0&{}&{}\\0&{ - \frac{1}{3}}&{\frac{1}{3}}&{}&{}\\ \vdots &{}& \ddots & \ddots & \vdots \\0&0& \cdots &{ - \frac{1}{n}}&{\frac{1}{n}}\end{aligned}} \right)\).

For \(j = 1,...,n\), let \({{\bf{a}}_j}\), \({{\bf{b}}_j}\) and \({{\bf{e}}_j}\) denote the \(j\)th columns of \(A\), \(B\) and \(I\), respectively.

For \(j = 1,...,n - 1\),

\({{\bf{a}}_j} = j\left( {{{\bf{e}}_j} + ....... + {{\bf{e}}_n}} \right)\),

\({{\bf{b}}_j} = \frac{1}{j}{{\bf{e}}_j} - \frac{1}{{j + 1}}{{\bf{e}}_{j + 1}}\), and

\({{\bf{b}}_n} = \frac{1}{n}{{\bf{e}}_n}\).

02

Find the value of \(A{{\bf{b}}_j}\)

\(\begin{aligned}{c}A{b_j} = A\left( {\frac{1}{j}{{\bf{e}}_j} - \frac{1}{{j + 1}}{{\bf{e}}_{j + 1}}} \right)\\ = \frac{1}{j}{{\bf{a}}_j} - \frac{1}{{j + 1}}{{\bf{a}}_{j + 1}}\\ = \left( {{{\bf{e}}_j} + ..... + {{\bf{e}}_n}} \right) - \left( {{{\bf{e}}_{j + 1}} + .... + {{\bf{e}}_n}} \right)\\ = {{\bf{e}}_j}\end{aligned}\)

So, \(AB = I\).

03

Find the value of \(B{{\bf{a}}_j}\)

As \(\frac{1}{n}{a_n} = {e_n}\),

\(\begin{aligned}{c}B{{\bf{a}}_j} = j\left( {B{{\bf{e}}_j} + ..... + B{{\bf{e}}_n}} \right)\\ = j\left( {{{\bf{b}}_j} + ..... + {{\bf{b}}_n}} \right)\\ = j\left( {\frac{1}{j}{{\bf{e}}_j}} \right)\\ = {{\bf{e}}_j}.\end{aligned}\)

So, \(BA = I\).

The inverse of matrix \(A\) is \(\left( {\begin{aligned}{*{20}{c}}1&0&0& \cdots &0\\{ - \frac{1}{2}}&{\frac{1}{2}}&0&{}&{}\\0&{ - \frac{1}{3}}&{\frac{1}{3}}&{}&{}\\ \vdots &{}& \ddots & \ddots & \vdots \\0&0& \cdots &{ - \frac{1}{n}}&{\frac{1}{n}}\end{aligned}} \right)\) for which \(AB = BA = I\).

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Most popular questions from this chapter

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

4. \[\left[ {\begin{array}{*{20}{c}}I&0\\{ - X}&I\end{array}} \right]\left[ {\begin{array}{*{20}{c}}A&B\\C&D\end{array}} \right]\]

If A, B, and X are \(n \times n\) invertible matrices, does the equation \({C^{ - 1}}\left( {A + X} \right){B^{ - 1}} = {I_n}\) have a solution, X? If so, find it.

Show that if the columns of Bare linearly dependent, then so are the columns of AB.

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.

16. a. If A and B are \({\bf{3}} \times {\bf{3}}\) and \(B = \left( {\begin{aligned}{*{20}{c}}{{{\bf{b}}_1}}&{{{\bf{b}}_2}}&{{{\bf{b}}_3}}\end{aligned}} \right)\), then \(AB = \left( {A{{\bf{b}}_1} + A{{\bf{b}}_2} + A{{\bf{b}}_3}} \right)\).

b. The second row of ABis the second row of Amultiplied on the right by B.

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

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

e. The transpose of a sum of matrices equals the sum of their transposes.

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be an invertible linear transformation, and let Sand U be functions from \({\mathbb{R}^n}\) into \({\mathbb{R}^n}\) such that \(S\left( {T\left( {\mathop{\rm x}\nolimits} \right)} \right) = {\mathop{\rm x}\nolimits} \) and \(\)\(U\left( {T\left( {\mathop{\rm x}\nolimits} \right)} \right) = {\mathop{\rm x}\nolimits} \) for all x in \({\mathbb{R}^n}\). Show that \(U\left( v \right) = S\left( v \right)\) for all v in \({\mathbb{R}^n}\). This will show that Thas a unique inverse, as asserted in theorem 9. [Hint: Given any v in \({\mathbb{R}^n}\), we can write \({\mathop{\rm v}\nolimits} = T\left( {\mathop{\rm x}\nolimits} \right)\) for some x. Why? Compute \(S\left( {\mathop{\rm v}\nolimits} \right)\) and \(U\left( {\mathop{\rm v}\nolimits} \right)\)].

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