|Re: [eigen] Precision in Cholesky Decomposition|
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- Subject: Re: [eigen] Precision in Cholesky Decomposition
- From: Benoit Jacob <jacob.benoit.1@xxxxxxxxx>
- Date: Mon, 8 Jun 2009 16:07:37 +0200
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There's been discussion recently over this value and indeed we should
reduce it a bit, perhaps down to 1e-13.
There's no way that it can make sense to make it lower than the
machine epsilon for double which is roughly 1e-16. See the source code
of ei_isMuchSmallerThan, and ei_isApprox : basically, this would turn
these fuzzy comparisons into exact comparisons !!
2009/6/8 Scott Stephens <stephens.js@xxxxxxxxx>:
> I was having a problem with a Cholesky decomposition failing with the
> indication that the matrix was not positive definite. I was surprised
> because I was able to successfully perform the Cholesky decomposition
> in MATLAB. I tracked this down to the value returned by
> precision<double>() as defined in src/Core/MathFunctions.h, the square
> root of which is being used as the cut-off for numerically equivalent
> to zero in the LLT code. The return value is hard-coded to 1e-11; I
> was able to match MATLAB by setting this to 1e-18 instead.
If you really had to use such a small value, smaller than machine
epsilon, that probably means that your matrix isn't significantly
distinguishable from an indefinite one and then you should use LDLT
instead of LLT. Or else you found a bug in our code. It would be
interesting if you could run a SVD on this matrix and find out its
condition number. if it's greater than 1e15 then your matrix isn't
> Is there a particular reason 1e-11 was chosen for this value? Would
> it make sense to go with a smaller value by default, or at least offer
> the ability to configure this number as part of the LLT interface?
The current value is determined by a formula that Keir found in a
paper by Higham (see source code) and that we later rederived
experimentally (see thread "LU precision tuning"). So we can be very
confident that this is the right formula and there's hardly a chance
that the user might know better... can you check the condition number
of your matrix, check whether Matlab uses plain LLT or LDLT (that
would explain it), check the accuracy of the result it gives...
> Or is there a better way for me to solve this problem? (Maybe some kind
> of scaling before the decomposition, then rescaling when I'm done?)
Scaling won't change the condition number :)
Then I'm no big expert in numerical questions. All I can say is try
LDLT... maybe someone will know better.