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Chapter 2: Q2.6-13Q (page 93)

[M] The consumption matrix C below is based on input-output data for the U.S. economy in 1958, with data for 81 sectors grouped into 7 larger sectors: (1) nonmetal household and personal products, (2) final metal products (such as motor vehicles), (3) basic metal products and mining, (4) basic nonmetal products and agriculture, (5) energy, (6) services, and (7) entertainment and miscellaneous products. Find the production levels needed to satisfy the final demand d. (Units are in millions of dollars.)

\(\left[ {\begin{array}{*{20}{c}}{.{\bf{1588}}}&{.{\bf{0064}}}&{.{\bf{0025}}}&{.{\bf{0304}}}&{.{\bf{0014}}}&{.{\bf{0083}}}&{.{\bf{1594}}}\\{.{\bf{0057}}}&{.{\bf{2645}}}&{.{\bf{0436}}}&{.{\bf{0099}}}&{.{\bf{0083}}}&{.{\bf{0201}}}&{.{\bf{3413}}}\\{.{\bf{0264}}}&{.{\bf{1506}}}&{.{\bf{3557}}}&{.{\bf{0139}}}&{.{\bf{0142}}}&{.{\bf{0070}}}&{.{\bf{0236}}}\\{.{\bf{3299}}}&{.{\bf{0565}}}&{.{\bf{0495}}}&{.{\bf{3636}}}&{.{\bf{0204}}}&{.{\bf{0483}}}&{.{\bf{0649}}}\\{.{\bf{0089}}}&{.{\bf{0081}}}&{.{\bf{0333}}}&{.{\bf{0295}}}&{.{\bf{3412}}}&{.{\bf{0237}}}&{.{\bf{0020}}}\\{.{\bf{1190}}}&{.{\bf{0901}}}&{.{\bf{0996}}}&{.{\bf{1260}}}&{.{\bf{1722}}}&{.{\bf{2368}}}&{.{\bf{3369}}}\\{.{\bf{0063}}}&{.{\bf{0126}}}&{.{\bf{0196}}}&{.{\bf{0098}}}&{.{\bf{0064}}}&{.{\bf{0132}}}&{.{\bf{0012}}}\end{array}} \right]\), \({\bf{d}} = \left[ {\begin{array}{*{20}{c}}{{\bf{74,000}}}\\{{\bf{56,000}}}\\{{\bf{10,500}}}\\{{\bf{25,000}}}\\{{\bf{17,500}}}\\{{\bf{196,000}}}\\{{\bf{5,000}}}\end{array}} \right]\)

Short Answer

Expert verified

\({\bf{x}} = \left( {10000,98000,51000,132000,49000,330000,14000} \right)\)

Step by step solution

01

Find the matrix \[I - C\]

Use the following MATLAB code to calculate \(I - C\).

\( > > I = \left[ \begin{array}{l}\begin{array}{*{20}{c}}1&0&0&0&0&0&{0;\,\,}\end{array}\begin{array}{*{20}{c}}0&1&0&0&0&0&{0;\,\,}\end{array}\begin{array}{*{20}{c}}0&0&1&0&0&0&{0;\,\,}\end{array}\\\begin{array}{*{20}{c}}0&0&0&1&0&0&{0;\,\,}\end{array}\begin{array}{*{20}{c}}0&0&0&0&1&0&{0;\,\,}\end{array}\begin{array}{*{20}{c}}0&0&0&0&0&1&{0;\,\,}\end{array}\\\begin{array}{*{20}{c}}0&0&0&0&0&0&{1;\,\,}\end{array}\end{array} \right]\)

\[ > > C = \left[ \begin{array}{l}\begin{array}{*{20}{c}}{.1588}&{.0064}&{.0025}&{.0304}&{.0014}&{.0083}&{.1594;\,\,}\end{array}\\\begin{array}{*{20}{c}}{.0057}&{0.2645}&{0.0436}&{.0099}&{.0083}&{.0201}&{0.3413;}\end{array}\\\begin{array}{*{20}{c}}{.0264}&{.1506}&{.3557}&{.0139}&{.0142}&{.0070}&{.0236;\,\,\,}\end{array}\\\begin{array}{*{20}{c}}{.3299}&{.0565}&{.0495}&{.3636}&{.0204}&{.0483}&{.0649;}\end{array}\\\begin{array}{*{20}{c}}{.0089}&{.0081}&{.0333}&{.0295}&{0.3412}&{.0237}&{.0020;\;}\end{array}\\\;\begin{array}{*{20}{c}}{.1190}&{0.0901}&{.09966}&{.1260}&{.1722}&{.2368}&{.3369;}\end{array}\\\begin{array}{*{20}{c}}{.0063}&{.0126}&{.0196}&{.0098}&{.0064}&{.0132}&{.0012}\end{array}\end{array} \right]\]

\( > > I - C\)

\[I - C = \left[ {\begin{array}{*{20}{c}}{.8412}&{ - 0.0064}&{ - 0.0025}&{ - 0.0304}&{ - 0.0014}&{ - 0.0083}&{ - 0.1594}\\{ - .0057}&{0.7355}&{ - 0.0436}&{ - 0.0099}&{ - 0.0083}&{ - 0.0201}&{ - 0.3413}\\{ - .0264}&{ - 0.1506}&{0.6443}&{ - 0.0139}&{ - 0.0142}&{ - 0.0070}&{ - 0.0236}\\{ - .3299}&{ - 0.0565}&{ - 0.0495}&{0.6364}&{ - 0.0204}&{ - 0.0483}&{ - 0.0649}\\{ - .0089}&{ - 0.0081}&{ - 0.0333}&{ - 0.0295}&{0.6588}&{ - 0.0237}&{ - 0.0020}\\{ - 0.1190}&{ - 0.0901}&{ - 0.0996}&{ - 0.1260}&{ - 0.1722}&{0.7632}&{ - 0.3369}\\{ - 0.0063}&{ - 0.0126}&{ - 0.0196}&{ - 0.0098}&{ - 0.0064}&{ - 0.0132}&{0.9988}\end{array}} \right]\]

02

Write the augmented matrix \(\left[ {\begin{array}{*{20}{c}}{I - C}&{\bf{d}}\end{array}} \right]\)

\[\left[ {\begin{array}{*{20}{c}}{I - C}&{\bf{d}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\begin{array}{*{20}{c}}{.8412}&{ - 0.0064}&{ - 0.0025}&{ - 0.0304}&{ - 0.0014}&{ - 0.0083}&{ - 0.1594}\\{ - .0057}&{0.7355}&{ - 0.0436}&{ - 0.0099}&{ - 0.0083}&{ - 0.0201}&{ - 0.3413}\\{ - .0264}&{ - 0.1506}&{0.6443}&{ - 0.0139}&{ - 0.0142}&{ - 0.0070}&{ - 0.0236}\\{ - .3299}&{ - 0.0565}&{ - 0.0495}&{0.6364}&{ - 0.0204}&{ - 0.0483}&{ - 0.0649}\\{ - .0089}&{ - 0.0081}&{ - 0.0333}&{ - 0.0295}&{0.6588}&{ - 0.0237}&{ - 0.0020}\\{ - 0.1190}&{ - 0.0901}&{ - 0.0996}&{ - 0.1260}&{ - 0.1722}&{0.7632}&{ - 0.3369}\\{ - 0.0063}&{ - 0.0126}&{ - 0.0196}&{ - 0.0098}&{ - 0.0064}&{ - 0.0132}&{0.9988}\end{array}}&{\begin{array}{*{20}{c}}{74000}\\{56000}\\{10500}\\{25000}\\{17500}\\{196000}\\{5000}\end{array}}\end{array}\,} \right]\]

03

Convert the matrix into row-reduced echelon form

Consider the following:

\(A = \left[ {\begin{array}{*{20}{c}}{\begin{array}{*{20}{c}}{.8412}&{ - 0.0064}&{ - 0.0025}&{ - 0.0304}&{ - 0.0014}&{ - 0.0083}&{ - 0.1594}\\{ - .0057}&{0.7355}&{ - 0.0436}&{ - 0.0099}&{ - 0.0083}&{ - 0.0201}&{ - 0.3413}\\{ - .0264}&{ - 0.1506}&{0.6443}&{ - 0.0139}&{ - 0.0142}&{ - 0.0070}&{ - 0.0236}\\{ - .3299}&{ - 0.0565}&{ - 0.0495}&{0.6364}&{ - 0.0204}&{ - 0.0483}&{ - 0.0649}\\{ - .0089}&{ - 0.0081}&{ - 0.0333}&{ - 0.0295}&{0.6588}&{ - 0.0237}&{ - 0.0020}\\{ - 0.1190}&{ - 0.0901}&{ - 0.0996}&{ - 0.1260}&{ - 0.1722}&{0.7632}&{ - 0.3369}\\{ - 0.0063}&{ - 0.0126}&{ - 0.0196}&{ - 0.0098}&{ - 0.0064}&{ - 0.0132}&{0.9988}\end{array}}&{\begin{array}{*{20}{c}}{74000}\\{56000}\\{10500}\\{25000}\\{17500}\\{196000}\\{5000}\end{array}}\end{array}\,} \right]\)

Use the code in MATLAB to obtain the row-reducedechelon form, as shown below:

\[\begin{array}{l} > > {\rm{ A }} = {\rm{ }} > > C = \left[ \begin{array}{l}\begin{array}{*{20}{c}}{.8412}&{ - .0064}&{ - .0025}&{ - .0304}&{ - .0014}&{ - .0083}&{ - .1594\,\,\,74000;\,\,}\end{array}\\\begin{array}{*{20}{c}}{ - .0057}&{0.7355}&{ - 0.0436}&{ - .0099}&{ - .0083}&{ - .0201}&{ - 0.3413\,\,56000;}\end{array}\\ - \begin{array}{*{20}{c}}{.0264}&{ - .1506}&{.6443}&{ - .0139}&{ - .0142}&{ - .0070}&{ - .0236\,\,\,\,10500;\,\,\,}\end{array}\\\begin{array}{*{20}{c}}{ - .3299}&{ - .0565}&{ - .0495}&{.6364}&{ - .0204}&{ - .0483}&{ - .0649\,\,\,\,\,25000;}\end{array}\\\begin{array}{*{20}{c}}{ - .0089}&{ - .0081}&{ - .0333}&{ - .0295}&{0.6588}&{ - .0237}&{ - .0020\,\,\,17500;\;}\end{array}\\\;\begin{array}{*{20}{c}}{ - .1190}&{ - 0.0901}&{ - .09966}&{ - .1260}&{ - .1722}&{.7632}&{ - .3369\,\,\,196000;}\end{array}\\\begin{array}{*{20}{c}}{ - .0063}&{ - .0126}&{ - .0196}&{ - .0098}&{ - .0064}&{ - .0132}&{0.9988\,\,\,\,\,5000;}\end{array}\end{array} \right];\\ > > {\rm{ U}} = {\rm{rref}}\left( {\rm{A}} \right)\end{array}\]

\(\left[ {\begin{array}{*{20}{c}}1&0&0&0&0&0&0&{99576}\\0&1&0&0&0&0&0&{97703}\\0&0&1&0&0&0&0&{51231}\\0&0&0&1&0&0&0&{131570}\\0&0&0&0&1&0&0&{49488}\\0&0&0&0&0&1&0&{329554}\\0&0&0&0&0&0&1&{13835}\end{array}} \right]\)

04

Find the production level

The production level for the seven categories is

\(x\left( {\begin{array}{*{20}{c}}{99576}&{97703}&{51231}&{131570}&{49488}&{329554}&{13835}\end{array}} \right)\).

The entries of \({\bf{x}}\) suggest greater precision when approximated to the nearest thousand. So, the realistic answer is

\({\bf{x}} = \left( {100000,98000,51000,132000,49000,330000,14000} \right)\).

So, the production level of seven categories is \({\bf{x}} = \left( {100000,98000,51000,132000,49000,330000,14000} \right)\).

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Most popular questions from this chapter

When a deep space probe launched, corrections may be necessary to place the probe on a precisely calculated trajectory. Radio elementary provides a stream of vectors, \({{\bf{x}}_{\bf{1}}},....,{{\bf{x}}_k}\), giving information at different times about how the probe’s position compares with its planned trajectory. Let \({X_k}\) be the matrix \(\left[ {{x_{\bf{1}}}.....{x_k}} \right]\). The matrix \({G_k} = {X_k}X_k^T\) is computed as the radar data are analyzed. When \({x_{k + {\bf{1}}}}\) arrives, a new \({G_{k + {\bf{1}}}}\) must be computed. Since the data vector arrive at high speed, the computational burden could be serve. But partitioned matrix multiplication helps tremendously. Compute the column-row expansions of \({G_k}\) and \({G_{k + {\bf{1}}}}\) and describe what must be computed in order to update \({G_k}\) to \({G_{k + {\bf{1}}}}\).

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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.

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?

Let \(A = \left( {\begin{aligned}{*{20}{c}}{\bf{2}}&{ - {\bf{3}}}\\{ - {\bf{4}}}&{\bf{6}}\end{aligned}} \right)\) and \(B = \left( {\begin{aligned}{*{20}{c}}{\bf{8}}&{\bf{4}}\\{\bf{5}}&{\bf{5}}\end{aligned}} \right)\) and \(C = \left( {\begin{aligned}{*{20}{c}}{\bf{5}}&{ - {\bf{2}}}\\{\bf{3}}&{\bf{1}}\end{aligned}} \right)\). Verfiy that \(AB = AC\) and yet \(B \ne C\).
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