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Chapter 4: LU and PLU decompositions

Christoph Börgers

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Abstract

You will see in this chapter that Gaussian elimination can be interpreted as decomposing the matrix A, or at least a matrix obtained from A by permuting rows, into triangular factors—a lower triangular factor L and an upper triangular factor U. This is a neat theoretical fact, but it is also practically useful. Once you have solved a system Ax = b, you know the decomposition into triangular factors, and, if you wanted to solve another system with the same matrix A, you could use that to make the computation much faster.

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You will see in this chapter that Gaussian elimination can be interpreted as decomposing the matrix A, or at least a matrix obtained from A by permuting rows, into triangular factors—a lower triangular factor L and an upper triangular factor U. This is a neat theoretical fact, but it is also practically useful. Once you have solved a system Ax = b, you know the decomposition into triangular factors, and, if you wanted to solve another system with the same matrix A, you could use that to make the computation much faster.

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Available abstract

You will see in this chapter that Gaussian elimination can be interpreted as decomposing the matrix A, or at least a matrix obtained from A by permuting rows, into triangular factors—a lower triangular factor L and an upper triangular factor U. This is a neat theoretical fact, but it is also practically useful. Once you have solved a system Ax = b, you know the decomposition into triangular factors, and, if you wanted to solve another system with the same matrix A, you could use that to make the computation much faster.

Key concepts: Triangular matrix, LU decomposition, Gaussian elimination, Factor (programming language), Computation, Matrix (chemical analysis), Decomposition, Mathematics

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