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Animation of Gaussian elimination. Red row eliminates the following rows, green rows change their order. In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of row-wise operations performed on the corresponding matrix of coefficients.
The field of elimination theory was motivated by the need of methods for solving systems of polynomial equations.. One of the first results was Bézout's theorem, which bounds the number of solutions (in the case of two polynomials in two variables at Bézout time).
In numerical linear algebra, the tridiagonal matrix algorithm, also known as the Thomas algorithm (named after Llewellyn Thomas), is a simplified form of Gaussian elimination that can be used to solve tridiagonal systems of equations. A tridiagonal system for n unknowns may be written as
In the case of n equations in n unknowns, it requires computation of n + 1 determinants, while Gaussian elimination produces the result with the same computational complexity as the computation of a single determinant. [8] [9] [verification needed] Cramer's rule can also be numerically unstable even for 2×2 systems. [10]
LU decomposition on Math-Linux. LU decomposition at Holistic Numerical Methods Institute; LU matrix factorization. MATLAB reference. Computer code. LAPACK is a collection of FORTRAN subroutines for solving dense linear algebra problems; ALGLIB includes a partial port of the LAPACK to C++, C#, Delphi, etc. C++ code, Prof. J. Loomis, University ...
In contrast, direct methods attempt to solve the problem by a finite sequence of operations. In the absence of rounding errors , direct methods would deliver an exact solution (for example, solving a linear system of equations A x = b {\displaystyle A\mathbf {x} =\mathbf {b} } by Gaussian elimination ).
An alternative way to eliminate taking square roots in the decomposition is to compute the LDL decomposition =, then solving = for y, and finally solving =. For linear systems that can be put into symmetric form, the Cholesky decomposition (or its LDL variant) is the method of choice, for superior efficiency and numerical stability.
Let the system of equations be written in matrix form as = where is the coefficient matrix, is the vector of unknowns, and is an vector of constants. In which case, if the system is indeterminate, then the infinite solution set is the set of all vectors generated by [4]