[petsc-users] Diagonalization of a 3D dense matrix

[Ketan Maheshwari]

Matthew,

I am probably not using the right language but I meant that each element
has three indices associated with it: x, y, z.

Here is a snapshot:

1 10 55    5.7113635929515209e-03
 1 10 56    4.2977490038287334e-03
 1 10 57    2.8719519782193204e-03
 1 10 58    1.4380140927001712e-03
 1 10 59    9.9299930690365083e-17
 1 11  0    0.0000000000000000e+00
 1 11  1    1.5658614070601917e-03
 1 11  2    3.1272842098367562e-03
 1 11  3    4.6798423857521204e-03

Where the first three columns are the coordinates and the last one is value.

Could you clarify the meaning of "diagonalization is not a clear concept"
if it is applicable to this case.

Thank you,
--
Ketan


On Mon, Jul 11, 2016 at 1:15 PM, Matthew Knepley <knepley at gmail.com> wrote:

> On Mon, Jul 11, 2016 at 12:05 PM, Ketan Maheshwari <
> ketancmaheshwari at gmail.com> wrote:
>
>> Hello PETSC-ers,
>>
>> I am a research faculty at Univ of Pittsburgh trying to use PETSC/SLEPC
>> to
>> obtain the diagonalization of a large matrix using Lanczos or Davidson
>> method.
>>
>> The matrix is a 3 dimensional dense matrix with a total of 216000
>> elements.
>>
>> After looking into some of the examples in PETSC as well SLEPC
>> implementations
>> it seems like most of the implementations are with 2 dimensional matrices.
>>
>
> You will have to explain what you mean by a "3D matrix". A matrix, by
> definition, has only
> rows and columns. You may mean a matrix generated from a 3D problem. That
> should pose
> no extra difficulty. You may mean a 3-index tensor, in which case
> diagonalization is not a clear
> concept.
>
>   Thanks,
>
>      Matt
>
>
>> So, I was wondering if it is possible to express a 3 dimensional matrix
>> object
>> compatible to PETSC so that the SLEPC API could be used to obtain
>> diagonalization.
>>
>> Any suggestions or pointers to documentation or examples would be of great
>> help.
>>
>> Best,
>> --
>> Ketan
>>
>>
>
>
> --
> What most experimenters take for granted before they begin their
> experiments is infinitely more interesting than any results to which their
> experiments lead.
> -- Norbert Wiener
>



-- 
Ketan
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