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Find a LU factorization of the matrices in Exercises 7-16 (with L unit lower triangular). Note that MATLAB will usually produce a permuted LU factorization because it uses partial pivoting for numerical accuracy.

8. \(\left[ {\begin{array}{*{20}{c}}6&9\\4&5\end{array}} \right]\)

Short Answer

Expert verified

The LU factorization of the matrices is \(L = \left[ {\begin{array}{*{20}{c}}1&0\\{\frac{2}{3}}&1\end{array}} \right]\).

Step by step solution

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01

Apply the row operation to determine matrix U

Place the first pivot column of \(\left[ {\begin{array}{*{20}{c}}6&9\\4&5\end{array}} \right]\) in the first column of L after dividing the column by top pivot entry 6. Then at row two, multiply row one by \(\frac{2}{3}\) and subtract it from row two to produce matrix U.

\[A = \left[ {\begin{array}{*{20}{c}}6&9\\4&5\end{array}} \right] \sim \left[ {\begin{array}{*{20}{c}}6&9\\0&{ - 1}\end{array}} \right] = U\]

02

Determine matrix L

The first pivot column of L is the first column of Adivided by the top pivot entry.

Divide the first column of \(\left[ {\begin{array}{*{20}{c}}6&9\\4&5\end{array}} \right]\)by pivot entry 6.

Also, divide row two of matrix U by \( - 1\), as shown below.

\[\begin{array}{l}\left[ {\begin{array}{*{20}{c}}{\frac{6}{6}}\\{\frac{4}{6}}\end{array}} \right]\,\left[ {\frac{{ - 1}}{{ - 1}}} \right]\\\,\,\, \downarrow \,\,\,\,\, \downarrow \\\left[ {\begin{array}{*{20}{c}}1&{}\\{\frac{2}{3}}&1\end{array}} \right]\end{array}\]

Place the result in L.

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

Thus, the LU factorization of the matrices is \(L = \left[ {\begin{array}{*{20}{c}}1&0\\{\frac{2}{3}}&1\end{array}} \right]\).

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Most popular questions from this chapter

(M) Read the documentation for your matrix program, and write the commands that will produce the following matrices (without keying in each entry of the matrix).

  1. A \({\bf{5}} \times {\bf{6}}\) matrix of zeros
  2. A \({\bf{3}} \times {\bf{5}}\) matrix of ones
  3. The \({\bf{6}} \times {\bf{6}}\) identity matrix
  4. A \({\bf{5}} \times {\bf{5}}\) diagonal matrix, with diagonal entries 3, 5, 7, 2, 4

Let \(T:{\mathbb{R}^n} \to {\mathbb{R}^n}\) be an invertible linear transformation. Explain why T is both one-to-one and onto \({\mathbb{R}^n}\). Use equations (1) and (2). Then give a second explanation using one or more theorems.

Suppose Ais an \(n \times n\) matrix with the property that the equation \[A{\mathop{\rm x}\nolimits} = 0\] has at least one solution for each b in \({\mathbb{R}^n}\). Without using Theorem 5 or 8, explain why each equation Ax = b has in fact exactly one solution.

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?

[M] Suppose memory or size restrictions prevent your matrix program from working with matrices having more than 32 rows and 32 columns, and suppose some project involves \(50 \times 50\) matrices A and B. Describe the commands or operations of your program that accomplish the following tasks.

a. Compute \(A + B\)

b. Compute \(AB\)

c. Solve \(Ax = b\) for some vector b in \({\mathbb{R}^{50}}\), assuming that \(A\) can be partitioned into a \(2 \times 2\) block matrix \(\left[ {{A_{ij}}} \right]\), with \({A_{11}}\) an invertible \(20 \times 20\) matrix, \({A_{22}}\) an invertible \(30 \times 30\) matrix, and \({A_{12}}\) a zero matrix. [Hint: Describe appropriate smaller systems to solve, without using any matrix inverse.]

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