Hello Again,

Yes I would like to find for the initial 10% and 20% of the eigenpairs. But in addition to this,I also want to check for the full spectrum.

So, how shall I proceed with it?

Thank You.

Regards,
savneet

Le 25/04/2018 à 11:33, Matthew Knepley a écrit :
On Wed, Apr 25, 2018 at 5:10 AM, Savneet Kaur <[email protected] <mailto:[email protected]>> wrote:

    Hello,

    Warm Regards

    I am Savneet Kaur, a master student at University Paris Saclay and
    currently pursuing an internship at CEA Saclay (France).

    I have recently started to understand the slepc and petsc solvers,
    by taking up the tutorials for eigenvalue problems. In my
    internship work I have to develop a laplacian matrix from a given
    transition rate matrix and solve it using SLEPC and PETSC and to
    evaluate the lowest eigenvalue.

    I was wondering if I could get some information. I need to
    diagonalize a 2D Laplacian matrix. And writing a code in C and
    diagonalizing it is easy. But I am not getting it how to
    accommodate with the SLEPC Program. Am i suppose to use any
    packages or it will is done by a loop? Or I need to read some
    other manual to understand how does it work. Please kindly let me
    know.

I would just like to clarify some terminology. "Diagonalize" would usually mean find all eigenvalues and eigenvectors. Is this what you mean? Often, SLEPc users want only a portion of the spectrum since the matrices are enormous.

  Thanks,

     Matt

    The diagonalizing a matrix will be preliminary step of my work.

    I will be highly obliged to the the team, if I could get help.

    Hoping for a favorable response from your side.

    Thank you for the time and consideration.


    Sincerely,

    *Savneet Kaur*

    *Intern at DEN/DANS/DMN/SRMP*

    *CEA - Centre de Saclay ǀ Bâtiment 520
    *

    *91191 Gif-sur-Yvette Cedex*

    *France*

    *Tel: +33 (0) 666 749 000*

    *Email: [email protected] <mailto:[email protected]>
    *






--
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

https://www.cse.buffalo.edu/~knepley/ <http://www.caam.rice.edu/%7Emk51/>

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