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Augmented Matricies


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To say Gauss-Jordan elimination is the only way is a bit strong, to say the least. I rarely use Guass-Jordan elimination, and I deal with matrices a lot. I frequently use various decomposition techniques: LU, Cholesky, QR, SVD, ...

 

Guass-Jordan elimination is the only way to do it by hand, perhaps.

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it is however a mehtod for solving systems of equations...

 

which is what the poster originally asked, personally I don't see how it really benfits the user as you can solve an upper triangular matrix U using backsubstitution.

 

maye I' missing part of its ability.

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Speaking computationally, straight LU decomposition usually doesn't save much at all. But, there are incomplete LU decomposition algorithms that are much faster and usually give results that can be tuned in accuracy (whether you want 0.1% error or 0.001% or something else, for example). These are used for a lot of computational simulations -- especially when the linear system is an approximation of a non-linear system. In that case, you do not need to solve the linear system to really high accuracy (like a complete LU or Gaussion Elimination would), because that approximated linear solution is going to be used as the latest guess in the iterative method needed to solve the nonlinear problem.

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