singular value decomposition?

As to how to implement it, I found my SVD algorithm in numerical recipies in C ( if I remember correctly ).

When I programmed the algorithm as the book present it, I had a little problem though, the order of the singular value was not 100% correct, so I had to correct it. Again, if I remember correctly, it was something along the line of swapping the last two values ( and corresponding row and column of left and right matrices ).

I don''t know if SVD is the solution for that kind of problem though, I personally used it in a very different context.

Even if SVD is faster in some case, which I doubt, it is not stable. Gaussian elimination with either sort of pivoting will be more stable.

What''s the purpose of the SVD, I want to know but I''m to lazy to clicky.

if you want to solve a linear system that is Ax=b ( A is matrix x,b :are vectors)
but A is singular you cannot use standart methods ( Gaussian Elimination etc fails ) That why SVD is used.
If you have more equation than independent variable I suggest you to use pseudoinverse. That is something like

x=inverse(AT*A)*AT*b, AT is transpose of A.

quote:

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Original post by leinad
What's the purpose of the SVD, I want to know but I'm to lazy to clicky.

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Don't be lazy. That will get you fired from McDonald's, .

Graham Rhodes
Senior Scientist
Applied Research Associates, Inc.


[edited by - grhodes_at_work on October 17, 2003 10:31:18 AM]