[Trilinos-Users] trillinos diagonalization

Alicia Klinvex aklinvex at purdue.edu
Tue Jul 7 22:50:02 EDT 2015


Hello,

I'm copying your message to the trilinos-users list, which includes the
other Trilinos developers.  Some of their backgrounds align more closely
with yours, so I wanted to make sure they can read what you're trying to do
(in case they have something to contribute to the discussion).

It looks to me like you're trying to solve the time-independent
Schroedinger equation, which I have limited experience with.  My
understanding is that you want the eigenvectors corresponding to the
smallest magnitude eigenvalues.  Just out of curiosity, is your Hamiltonian
matrix symmetric positive definite?

I don't quite understand your orthogonal condition, or the single vector
you mentioned.  Do you mean that you want the eigenvectors returned by
LOBPCG to be orthogonal to that single vector?  If that's what you're
trying to do, I can point you to an example on the Anasazi website.

Best wishes,
Alicia



On Tue, Jul 7, 2015 at 7:38 PM, 임재창 <ljchang94 at kaist.ac.kr> wrote:

>  Thank you for your response.
>
>
> As you say, it is true that i provides initial nxk MultiVector of rank 1
> as the initial subspace.(because each vectors are same)
>
>
> I am using LOBPCG solver to diagonalize hamiltonian matrix of DFT.
>
>
> and i know the initial information of density but don't have initial
> information of orbitals which is eigenvector of hamiltonian matrix
>
>
> and sum of square of orbitals should be density.
>
>
> But it is not easy to find these orbitals with orthogonal condition, so i
> just used same orbitals as an initial guess.
>
>
>
> -----------------------원본메일-----------------------
> 보낸사람 : Alicia Klinvex <aklinvex at purdue.edu>
> 받는사람: 임재창<ljchang94 at kaist.ac.kr>
> 참 조: "Hoemmen,Mark" <mhoemme at sandia.gov>,"trilinos-users at trilinos.org" <
> trilinos-users at trilinos.org>
> 보낸날짜: 2015-07-08 02:36:47 GMT +0900 (ROK)
>
> 제목: Re: [Trilinos-Users] trillinos diagonalization
>
>
> Hello,
>
> I can think of a couple of reasons you might be seeing that behavior, but
> I have a question first.  Each of the Anasazi eigensolvers needs an initial
> n x k MultiVector of rank k, which is used as the initial subspace.  (I
> think each eigensolver deals with rank deficiency differently; I'm not sure
> how the LOBPCG solver handles it.)  From your email, it sounds like your
> subspace is of rank 1.  Is that true, and if so, why are you providing a
> subspace of rank 1?
>
> Best wishes,
> Alicia Klinvex
>
> On Mon, Jul 6, 2015 at 7:48 PM, 임재창 <ljchang94 at kaist.ac.kr> wrote:
>
>>  i used LOBPCG solver and i didn't set any parameters.
>>
>> -----------------------원본메일-----------------------
>> 보낸사람 : "Hoemmen, Mark" <mhoemme at sandia.gov>
>> 받는사람: "trilinos-users at trilinos.org" <trilinos-users at trilinos.org>
>> 보낸날짜: 2015-06-30 08:33:52 GMT +0900 (ROK)
>> 제목: Re: [Trilinos-Users] trillinos diagonalization
>>
>>
>> On 6/29/15, 5:12 PM, "trilinos-users-request at trilinos.org"
>> <trilinos-users-request at trilinos.org> wrote:
>> >Message: 1
>> >Date: Mon, 29 Jun 2015 20:34:23 +0900 (KST)
>> >From: =?UTF-8?B?7J6E7J6s7LC9?=<ljchang94 at kaist.ac.kr>
>> >To: trilinos-users at trilinos.org
>> >Subject: [Trilinos-Users] trillinos diagonalization
>> >Message-ID: <55912e883fb6_ at _imoxion.com>
>> >Content-Type: text/plain; charset="utf-8"
>> >
>> >?Dear Trilinos developers and users,
>> >
>> >
>> >I'm using Anasazi package to diagonalize sparse matrices with
>> >Epetra_CrsMatrix.
>> >i gave same vectors as an initial eigenvectorw(so it is not orthogonal)
>> >and the time and iteration number of first step of diagnoalization were
>> >different between each trial with same input.
>> >however eigenvalues were almost same in tolerance value.
>> >
>> >and other steps didn't show this phenomena
>> >
>> >i guess that the program randomized the initial eigenvectors even i set
>> >some initial eigenvectors.
>> >
>> >is there any reason of this phenomena?
>>
>> Greetings!
>>
>> Which eigensolver did you use, and did you set any parameters?
>>
>> mfh
>>
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>>
>>
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>
>
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