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22. (Reduced LU Factorization) With A as in the Practice Problem, i.e., \[A = \left[ {\begin{array}{*{20}{c}}{\bf{2}}&{ - {\bf{4}}}&{ - {\bf{2}}}&{\bf{3}}\\{\bf{6}}&{ - {\bf{9}}}&{ - {\bf{5}}}&{\bf{8}}\\{\bf{2}}&{ - {\bf{7}}}&{ - {\bf{3}}}&{\bf{9}}\\{\bf{4}}&{ - {\bf{2}}}&{ - {\bf{2}}}&{ - {\bf{1}}}\\{ - {\bf{6}}}&{\bf{3}}&{\bf{3}}&{\bf{4}}\end{array}} \right]\], find a \[{\bf{5}} \times {\bf{3}}\] matrix B and a \[{\bf{3}} \times {\bf{4}}\] matrix C such that \[A = BC\]. Generalize this idea to the case where A is \[m \times n\], \[A = LU\], and U has only three nonzero rows.

Short Answer

Expert verified

Matrix \[B = \left[ {\begin{array}{*{20}{c}}1&0&0\\3&1&0\\1&{ - 1}&1\\2&2&{ - 1}\\{ - 3}&{ - 3}&2\end{array}} \right]\] and matrix \[C = \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\0&3&1&{ - 1}\\0&0&0&5\end{array}} \right]\] such that \[A = BC\]. For the case A is \[m \times n\], and U has only three nonzero rows, let B be the first three columns of Land Cbe the first three rows of U.

Step by step solution

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01

Determine L

Since A has five rows, Lshould be \[5 \times 5\]. The first column of L is the first column of A, divided by the top pivot entry 2.

\[L = \left[ {\begin{array}{*{20}{c}}1&0&0&0&0\\3&1&0&0&0\\1&*&1&0&0\\2&*&*&1&0\\{ - 3}&*&*&*&1\end{array}} \right]\]

02

Reduce A into row echelon form

\[A = \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\6&{ - 9}&{ - 5}&8\\2&{ - 7}&{ - 3}&9\\4&{ - 2}&{ - 2}&{ - 1}\\{ - 6}&3&3&4\end{array}} \right]\]

At row 2, multiply row 1 by 3 and subtract it from row 2, i.e., \[{R_2} \to {R_2} - 3{R_1}\]; at row 3, subtract row 1 from row 3 i.e., \[{R_3} \to {R_3} - {R_1}\]; at row 4, multiply row 1 by 2 and subtract it from row 4, i.e., \[{R_4} \to {R_4} - 2{R_1}\]; and at row 5, multiply row 1 by 3 and add it to row 5, i.e., \[{R_5} \to {R_5} + 3{R_1}\]. Therefore,

\[ \sim \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\0&3&1&{ - 1}\\0&{ - 3}&{ - 1}&6\\0&6&2&{ - 7}\\0&{ - 9}&{ - 3}&{13}\end{array}} \right]\]

At row 3, add row 2 to row 3, i.e., \[{R_3} \to {R_2} + {R_3}\]; at row 4, multiply row 2 by 2 and subtract it from row 4, i.e., \[{R_4} \to {R_4} - 2{R_2}\]; and at row 5, multiply row 2 by 3 and add it to row 5, i.e., \[{R_5} \to {R_5} + 3{R_2}\]. Therefore,

\[ \sim \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\0&3&1&{ - 1}\\0&0&0&5\\0&0&0&{ - 5}\\0&0&0&{10}\end{array}} \right]\]

Here, row 3 has a pivot element 5. So, at row 4, add row 3 to row 4, i.e., \[{R_4} \to {R_3} + {R_4}\], and at row 5, multiply row 3 by 2 and subtract it from row 5, i.e., \[{R_5} \to {R_5} - 2{R_3}\]. Then,

\[ \sim \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\0&3&1&{ - 1}\\0&0&0&5\\0&0&0&0\\0&0&0&0\end{array}} \right] = U\]

At each pivot column \[\left[ {\begin{array}{*{20}{c}}2\\6\\2\\4\\{ - 6}\end{array}} \right]\], \[\left[ {\begin{array}{*{20}{c}}3\\{ - 3}\\6\\{ - 9}\end{array}} \right]\], and \[\left[ {\begin{array}{*{20}{c}}5\\{ - 5}\\{10}\end{array}} \right]\], divide them by pivots 2, 3, and 5, respectively. Thus, L can be written as:

\[L = \left[ {\begin{array}{*{20}{c}}1&0&0&0&0\\3&1&0&0&0\\1&{ - 1}&1&0&0\\2&2&{ - 1}&1&0\\{ - 3}&{ - 3}&2&0&1\end{array}} \right]\]

03

Determine B and C

Note that the last two rows of U contain only zeros. So, for LU, the last two columns of L will not be used. Thus, let B be the first three columns of L and C be the first three rows of U. That is

\[B = \left[ {\begin{array}{*{20}{c}}1&0&0\\3&1&0\\1&{ - 1}&1\\2&2&{ - 1}\\{ - 3}&{ - 3}&2\end{array}} \right]\]and \[C = \left[ {\begin{array}{*{20}{c}}2&{ - 4}&{ - 2}&3\\0&3&1&{ - 1}\\0&0&0&5\end{array}} \right]\]

Hence, \[A = LU = BC\].

04

Generalize the idea

For the case A is \[m \times n\], U has only three nonzero rows. So, the first three columns of L are only required. Hence, let B be the first three columns of Land Cbe the first three rows of U.

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Most popular questions from this chapter

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.

Let \(X\) be \(m \times n\) data matrix such that \({X^T}X\) is invertible., and let \(M = {I_m} - X{\left( {{X^T}X} \right)^{ - {\bf{1}}}}{X^T}\). Add a column \({x_{\bf{0}}}\) to the data and form

\(W = \left[ {\begin{array}{*{20}{c}}X&{{x_{\bf{0}}}}\end{array}} \right]\)

Compute \({W^T}W\). The \(\left( {{\bf{1}},{\bf{1}}} \right)\) entry is \({X^T}X\). Show that the Schur complement (Exercise 15) of \({X^T}X\) can be written in the form \({\bf{x}}_{\bf{0}}^TM{{\bf{x}}_{\bf{0}}}\). It can be shown that the quantity \({\left( {{\bf{x}}_{\bf{0}}^TM{{\bf{x}}_{\bf{0}}}} \right)^{ - {\bf{1}}}}\) is the \(\left( {{\bf{2}},{\bf{2}}} \right)\)-entry in \({\left( {{W^T}W} \right)^{ - {\bf{1}}}}\). This entry has a useful statistical interpretation, under appropriate hypotheses.

In the study of engineering control of physical systems, a standard set of differential equations is transformed by Laplace transforms into the following system of linear equations:

\(\left[ {\begin{array}{*{20}{c}}{A - s{I_n}}&B\\C&{{I_m}}\end{array}} \right]\left[ {\begin{array}{*{20}{c}}{\bf{x}}\\{\bf{u}}\end{array}} \right] = \left[ {\begin{array}{*{20}{c}}{\bf{0}}\\{\bf{y}}\end{array}} \right]\)

Where \(A\) is \(n \times n\), \(B\) is \(n \times m\), \(C\) is \(m \times n\), and \(s\) is a variable. The vector \({\bf{u}}\) in \({\mathbb{R}^m}\) is the โ€œinputโ€ to the system, \({\bf{y}}\) in \({\mathbb{R}^m}\) is the โ€œoutputโ€ and \({\bf{x}}\) in \({\mathbb{R}^n}\) is the โ€œstateโ€ vector. (Actually, the vectors \({\bf{x}}\), \({\bf{u}}\) and \({\bf{v}}\) are functions of \(s\), but we suppress this fact because it does not affect the algebraic calculations in Exercises 19 and 20.)

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 \(AB\).

\(A = \left( {\begin{aligned}{*{20}{c}}{\bf{4}}&{ - {\bf{2}}}\\{ - {\bf{3}}}&{\bf{0}}\\{\bf{3}}&{\bf{5}}\end{aligned}} \right)\), \(B = \left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{3}}\\{\bf{2}}&{ - {\bf{1}}}\end{aligned}} \right)\)

Let Ube the \({\bf{3}} \times {\bf{2}}\) cost matrix described in Example 6 of Section 1.8. The first column of Ulists the costs per dollar of output for manufacturing product B, and the second column lists the costs per dollar of output for product C. (The costs are categorized as materials, labor, and overhead.) Let \({q_1}\) be a vector in \({\mathbb{R}^{\bf{2}}}\) that lists the output (measured in dollars) of products B and C manufactured during the first quarter of the year, and let \({q_{\bf{2}}}\), \({q_{\bf{3}}}\) and \({q_{\bf{4}}}\) be the analogous vectors that list the amounts of products B and C manufactured in the second, third, and fourth quarters, respectively. Give an economic description of the data in the matrix UQ, where \(Q = \left( {\begin{aligned}{*{20}{c}}{{{\bf{q}}_1}}&{{{\bf{q}}_2}}&{{{\bf{q}}_3}}&{{{\bf{q}}_4}}\end{aligned}} \right)\).

Let \(A = \left( {\begin{aligned}{*{20}{c}}3&{ - 6}\\{ - 1}&2\end{aligned}} \right)\). Construct a \({\bf{2}} \times {\bf{2}}\) matrix Bsuch that ABis the zero matrix. Use two different nonzero columns for B.

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