# [petsc-dev] About the problem of Lagrange multiplier

Fri Apr 8 00:34:31 CDT 2022

Dear Mr./Ms.，

In fact, I want to solve a discretized equation like this

where K, U=[u1u2 …un]Tand F are fields sit on the vertices, and can easily be created by ‘DMCreateMatrix’ or ‘DMCreateGlobalVector’. λ is the Lagrange multiplier vector. The augmented Q (non-square) is the constraint coefficient matrix and has the form as

The Q is employed here to satisfy the following constraints

So how to build the entire system in-place in one big matrix (Kλ)? Could you give me more specific suggestions on this problem?

Thank you very much!

Best regards,

Yahe

-----原始邮件-----

DMStag may also be useful for your needs (and far simpler to use than DMPLEX) depending on where your Lagrange multipliers live. Note that regardless you should not need to be copying entire large submatrices around into bigger matrices; you can build the entire system in-place in one big matrix. MatNest is also a possibility depending on exactly what you are doing.

If you explain what your Lagrange multipliers are (the constraints) we may be able to make more specific suggestions.

Barry

On Apr 7, 2022, at 8:26 AM, Matthew Knepley <knepley at gmail.com> wrote:

On Thu, Apr 7, 2022 at 8:16 AM 高亚贺 via petsc-users <petsc-users at mcs.anl.gov> wrote:

Dear Mr./Ms.，

I have used ‘DMCreateMatrix’ to create a matrix K, and also the ‘DMCreateGlobalVector’ to create two vectors U (to be solved) and F (right-hand side), i.e. KU=F. Now, I want to add some complex constraints to this system through lagrangian multiplier method, and the constraint matrix is Q. The KU=F transforms to

<1649328463919.png>

How to create Kλ, and how to effectively copy values K and Q to Kλ? Does the newly created Kλ and Fλ still have an advantage of DMDA? Or do you have any other good suggestions for this kind of problem?

DMDA can only really handle collocated discretizations, meaning all fields sit on the vertices. If you can discretize your problem this way, then just give it two fields and assemble K_\lambda as normal. If not, then you might look at DMPlex which supports a wider range of discretizations.

Thanks,

Matt

Thank you very much!

Best regards,

A PETSc user

--

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/

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20220408/420d33f4/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: 1649395840117.png
Type: image/png
Size: 3313 bytes
Desc: not available
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20220408/420d33f4/attachment-0003.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: 1649395860765.png
Type: image/png
Size: 2776 bytes
Desc: not available
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20220408/420d33f4/attachment-0004.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: 1649395881836.png
Type: image/png
Size: 4030 bytes
Desc: not available
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20220408/420d33f4/attachment-0005.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: About the problem of Lagrange multiplier.docx
Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
Size: 22052 bytes
Desc: not available
URL: <http://lists.mcs.anl.gov/pipermail/petsc-dev/attachments/20220408/420d33f4/attachment-0001.docx>