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Repeat Exercise 5 with

\(C = \left[ {\begin{array}{*{20}{c}}{.1}&{.6}\\{.5}&{.2}\end{array}} \right],\,{\mathop{\rm d}\nolimits} = \left[ {\begin{array}{*{20}{c}}{18}\\{11}\end{array}} \right]\)

Short Answer

Expert verified

The production level necessary to satisfy the final demand is \(x = \left[ {\begin{array}{*{20}{c}}{50}\\{45}\end{array}} \right]\).

Step by step solution

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01

Use an inverse matrix to determine the production level

Theorem 11states that C is the consumption matrix for an economy. Let d be the final demand. If C and d have non-negative entries and each column sum of C is less than 1, then \({\left( {I - C} \right)^{ - 1}}\) exists. Also, the production vector \(x = {\left( {I - C} \right)^{ - 1}}{\mathop{\rm d}\nolimits} \) has non-negative entries and is the unique solution of \(x = Cx + {\mathop{\rm d}\nolimits} \).

\[\begin{array}{c}{\mathop{\rm x}\nolimits} = {\left( {I - C} \right)^{ - 1}}{\mathop{\rm d}\nolimits} \\ = {\left( {\left[ {\begin{array}{*{20}{c}}1&0\\0&1\end{array}} \right] - \left[ {\begin{array}{*{20}{c}}{.1}&{.6}\\{.5}&{.2}\end{array}} \right]} \right)^{ - 1}}\left[ {\begin{array}{*{20}{c}}{18}\\{11}\end{array}} \right]\\ = {\left[ {\begin{array}{*{20}{c}}{.9}&{ - .6}\\{ - .5}&{.8}\end{array}} \right]^{ - 1}}\left[ {\begin{array}{*{20}{c}}{18}\\{11}\end{array}} \right]\\ = \frac{1}{{0.42}}\left[ {\begin{array}{*{20}{c}}{.8}&{.6}\\{.5}&{.9}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{18}\\{11}\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}{1.9}&{1.42}\\{1.19}&{2.14}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{18}\\{11}\end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}}{50}\\{45}\end{array}} \right]\end{array}\]

Thus, the production level necessary to satisfy the final demand is \(x = \left[ {\begin{array}{*{20}{c}}{50}\\{45}\end{array}} \right]\).

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Most popular questions from this chapter

In Exercise 10 mark each statement True or False. Justify each answer.

10. a. A product of invertible \(n \times n\) matrices is invertible, and the inverse of the product of their inverses in the same order.

b. If A is invertible, then the inverse of \({A^{ - {\bf{1}}}}\) is A itself.

c. If \(A = \left( {\begin{aligned}{*{20}{c}}a&b\\c&d\end{aligned}} \right)\) and \(ad = bc\), then A is not invertible.

d. If A can be row reduced to the identity matrix, then A must be invertible.

e. If A is invertible, then elementary row operations that reduce A to the identity \({I_n}\) also reduce \({A^{ - {\bf{1}}}}\) to \({I_n}\).

Suppose the first two columns, \({{\bf{b}}_1}\) and \({{\bf{b}}_2}\), of Bare equal. What can you say about the columns of AB(if ABis defined)? Why?

In Exercises 27 and 28, view vectors in \({\mathbb{R}^n}\)as\(n \times 1\)matrices. For \({\mathop{\rm u}\nolimits} \) and \({\mathop{\rm v}\nolimits} \) in \({\mathbb{R}^n}\), the matrix product \({{\mathop{\rm u}\nolimits} ^T}v\) is a \(1 \times 1\) matrix, called the scalar product, or inner product, of u and v. It is usually written as a single real number without brackets. The matrix product \({{\mathop{\rm uv}\nolimits} ^T}\) is a \(n \times n\) matrix, called the outer product of u and v. The products \({{\mathop{\rm u}\nolimits} ^T}{\mathop{\rm v}\nolimits} \) and \({{\mathop{\rm uv}\nolimits} ^T}\) will appear later in the text.

28. If u and v are in \({\mathbb{R}^n}\), how are \({{\mathop{\rm u}\nolimits} ^T}{\mathop{\rm v}\nolimits} \) and \({{\mathop{\rm v}\nolimits} ^T}{\mathop{\rm u}\nolimits} \) related? How are \({{\mathop{\rm uv}\nolimits} ^T}\) and \({\mathop{\rm v}\nolimits} {{\mathop{\rm u}\nolimits} ^T}\) related?

In exercise 5 and 6, compute the product \(AB\) in two ways: (a) by the definition, where \(A{b_{\bf{1}}}\) and \(A{b_{\bf{2}}}\) are computed separately, and (b) by the row-column rule for computing \(AB\).

\(A = \left( {\begin{aligned}{*{20}{c}}{ - {\bf{1}}}&{\bf{2}}\\{\bf{5}}&{\bf{4}}\\{\bf{2}}&{ - {\bf{3}}}\end{aligned}} \right)\), \(B = \left( {\begin{aligned}{*{20}{c}}{\bf{3}}&{ - {\bf{2}}}\\{ - {\bf{2}}}&{\bf{1}}\end{aligned}} \right)\)

Suppose \(AD = {I_m}\) (the \(m \times m\) identity matrix). Show that for any b in \({\mathbb{R}^m}\), the equation \(A{\mathop{\rm x}\nolimits} = {\mathop{\rm b}\nolimits} \) has a solution. (Hint: Think about the equation \(AD{\mathop{\rm b}\nolimits} = {\mathop{\rm b}\nolimits} \).) Explain why Acannot have more rows than columns.

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