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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

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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

If Ais an \(n \times n\) matrix and the equation \(A{\bf{x}} = {\bf{b}}\) has more than one solution for some b, then the transformation \({\bf{x}}| \to A{\bf{x}}\) is not one-to-one. What else can you say about this transformation? Justify your answer.

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.

A useful way to test new ideas in matrix algebra, or to make conjectures, is to make calculations with matrices selected at random. Checking a property for a few matrices does not prove that the property holds in general, but it makes the property more believable. Also, if the property is actually false, you may discover this when you make a few calculations.

37. Construct a random \({\bf{4}} \times {\bf{4}}\) matrix Aand test whether \(\left( {A + I} \right)\left( {A - I} \right) = {A^2} - I\). The best way to do this is to compute \(\left( {A + I} \right)\left( {A - I} \right) - \left( {{A^2} - I} \right)\) and verify that this difference is the zero matrix. Do this for three random matrices. Then test \(\left( {A + B} \right)\left( {A - B} \right) = {A^2} - {B^{\bf{2}}}\) the same way for three pairs of random \({\bf{4}} \times {\bf{4}}\) matrices. Report your conclusions.

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

1. \(\left[ {\begin{array}{*{20}{c}}I&{\bf{0}}\\E&I\end{array}} \right]\left[ {\begin{array}{*{20}{c}}A&B\\C&D\end{array}} \right]\)

Show that block upper triangular matrix \(A\) in Example 5is invertible if and only if both \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible. [Hint: If \({A_{{\bf{11}}}}\) and \({A_{{\bf{12}}}}\) are invertible, the formula for \({A^{ - {\bf{1}}}}\) given in Example 5 actually works as the inverse of \(A\).] This fact about \(A\) is an important part of several computer algorithims that estimates eigenvalues of matrices. Eigenvalues are discussed in chapter 5.

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