LU Factorization: Motivating Explanation & Real World Impact

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SUMMARY

LU factorization is a method for decomposing a matrix A into a lower triangular matrix L and an upper triangular matrix U, facilitating the efficient solution of linear equations. The process allows for solving Ax = b by transforming it into LUx = b, which simplifies to Ux = y and then L^-1b = y, enabling back substitution. This technique not only streamlines the solving of equations but also optimizes computational efficiency by preserving matrix information during repeated calculations, making it a fundamental concept in linear algebra.

PREREQUISITES
  • Understanding of matrix operations and properties
  • Familiarity with linear equations and their solutions
  • Knowledge of back substitution methods
  • Basic concepts of triangular matrices
NEXT STEPS
  • Study LU factorization algorithms and their implementations
  • Learn about matrix decomposition techniques, including QR and Cholesky decompositions
  • Explore numerical methods for solving linear systems
  • Investigate the computational complexity of matrix operations
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Mathematicians, data scientists, engineers, and anyone involved in computational mathematics or numerical analysis who seeks to enhance their understanding of matrix factorization and its applications in solving linear equations efficiently.

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What is the most motivating way to introduce LU factorization of a matrix? I am looking for an example or explanation which has a real impact.
 
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One motivation is that it makes solving equations very easy! If A= LU where, of course, L is a "lower diagonal" and U is "upper diagonal", then we can solve Ax= b by writing it as LUx= b so that Ux= y= L-1b, which can be done by 'back substitution" and then solving x= U-1y again by back substitution.

For example, if
A= LU= \begin{bmatrix}2 & 0 \\ 1 & 3\end{bmatrix}\begin{bmatrix}1 & 3 \\ 0 & 2\end{bmatrix}\begin{bmatrix}x_1 \\ x_2 \end{bmatrix}= \begin{bmatrix}2 \\ 1\end{bmatrix}
We can let Ux= y so the equation becomes
Ly= \begin{bmatrix}2 & 0 \\ 1 & 3\end{bmatrix}\begin{bmatrix}y_1 \\ y_2\end{bmatrix}= \begin{bmatrix}2 & 1 \end{bmatrix}

The first row is equivalent to the equation 2y_1= 2 so we have immediately y_1= 1. With that value, the second equation, y_1+ 3y_2= 1 becomes 1+ 3y_2= 1 so that 3y_2= 0 and y_2= 0.

Then, since we defined y to be Ux, we have
x= \begin{bmatrix} 1 & 3 \\ 0 & 2 \end{bmatrix}\begin{bmatrix}x_1 \\ x_2\end{bmatrix}= \begin{bmatrix}1 \\ 0 \end{bmatrix}

Now, the second row gives the equation 2x_2= 0 so that x_2= 0 and then the top row becomes x_1+ 3x_2= x_1= 1.
 
Much of linear algebra can be seen as factoring operators. Triangular equations are easy to solve so it is helpful to solve an equation by factoring it into two triangular equations. Another views is to thing about solving an equation by a usual method like row reduction. In doing so we throw away a lot of of the matrix, and as a result if we solve many equations with the same operator we needlessly repeat work. We can by slight modification of row reduction keep this information at almost no cost. If we solve the equation many times we can reuse the information each time. This is the LU decomposition.
 

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