| Re: [eigen] Instability in LLT and LDLT methods. |
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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] Instability in LLT and LDLT methods.
- From: Keir Mierle <mierle@xxxxxxxxx>
- Date: Tue, 27 Jan 2009 15:43:57 -0800
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Probably it's better to do full pivoting. Apparently cholesky is
stable for semidefinite matrices when full pivoting is used:
http://eprints.ma.man.ac.uk/1101/01/covered/MIMS_ep2008_56.pdf
Keir
On Tue, Jan 27, 2009 at 3:05 PM, Gael Guennebaud
<gael.guennebaud@xxxxxxxxx> wrote:
> Hi,
>
> yes, it seems the test to check whether the matrix is positive
> definite was too strict. I changed the absolute tolerance a bit, but
> we still need something better. Basically, in Cholesky we compute at
> each iteration 1/sqrt(x), and so x must be >0 with some epsilon...
>
> Gael.
>
> On Tue, Jan 27, 2009 at 7:35 PM, Keir Mierle <mierle@xxxxxxxxx> wrote:
>> Here is a testcase that fails with LLT and LDLT but works fine with
>> all of LU, SVD, and QR solving. Depends on my previous patch for QR
>> solver (or comment out the qr().solve line).
>>
>> Keir
>>
>
>
>