both sparse and dense.
there are a few steps for the whole calculation:. inverse of M. get degree of
freedom. multiplication. addition
in fact i need to support both double and complex double for either distributed
memory based or out-of-core.
I found one MR based solution for large matrix inversion
https://github.com/JingenXiang/MatrixInversion and I have modified the code to
support complex double. Execution seems ok but i do not understand the final
output format. It seems that the columns are swapped. thanks, canal
On Monday, October 5, 2015 12:26 PM, Allen McIntosh
<amcint...@appcomsci.com> wrote:
1) Is m sparse?
2) Once you have computed "inverse", what are you going to do with it?
On 10/04/2015 10:31 PM, go canal wrote:
> Thank you all, the solver is something like this, am I correct:
> Matrix m = ....
> Matrix inverse = new QRDecomposition(m).solve(new DiagonalMatrix(1,
> m.rowSize()));
>
> The problem I have is that the matrix is too big, I need distributed, or
> out-of-core solution.
>
> thanks, canal
>
>
> On Monday, October 5, 2015 6:25 AM, Peter Jaumann
><peter.jauma...@gmail.com> wrote:
>
>
> This should be done with a matrix solver indeed!!!
>
>
>
> On Oct 4, 2015 11:53 AM, "Ted Dunning" <ted.dunn...@gmail.com> wrote:
>>
>>
>> It is almost certain that starting with an inversion is a serious error.
>>
>> Are you sure you don't want a matrix solver instead?
>>
>> Sent from my iPhone
>>
>>> On Oct 3, 2015, at 20:09, go canal <goca...@yahoo.com.INVALID> wrote:
>>>
>>> oh, it is so unfortunate that the first step of my project requires the
> inversion of a very large matrix. will have to revert back to scalapack or
> MR based solutions I guess.
>>> thanks, canal
>>>
>>>
>>> On Saturday, October 3, 2015 11:31 PM, Ted Dunning <
> ted.dunn...@gmail.com> wrote:
>>>
>>>
>>> I doubt seriously that Samsara will support matrix inversion per se. The
>>> problem is
>>>
>>> a) it densifies sparse matrices
>>>
>>> b) it is much more costly than solving a linear system
>>>
>>> Samsara is roughly memory based, but different back-ends will try to
> spill
>>> to disk if necessary. It is likely that the resulting degradation in
>>> performance would be dramatic and thus unacceptable to most users.
>>>
>>>
>>>
>>>> On Fri, Oct 2, 2015 at 8:47 PM, go canal <goca...@yahoo.com.invalid>
> wrote:
>>>>
>>>> HiI saw some distributed matrix functions included in Samsara now.
>>>> Wondering if we have a plan to support matrix inversion ?BTW, am I
> correct
>>>> that it is distributed memory based, not out-of-core ? thanks, canal
>>>
>>>
>
>
>
>
