<div dir="ltr"><div class="gmail_extra"><div class="gmail_quote">On Mon, Oct 12, 2015 at 1:49 AM, keguoyi <span dir="ltr"><<a href="mailto:coyigg@hotmail.com" target="_blank">coyigg@hotmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
<div><div dir="ltr">Dear Petsc developers and users, <br><br>This is Guoyi ke, a graduate student in Texas Tech University. I have a 2D Navier Stokes problem that has block matrices: J=[F B^T; B 0]. I want to build a pressure convection–diffusion preconditioner (PCD) P=[F B^T; 0 Sp]. Here, we let Sp=-Ap(Fp)^(-1)Mp approximate schur complement S=-BF^(-1)B^T, where Ap is pressure Laplacian matrix, Mp is pressure mass matrix, and Fp is convection-diffusion operators on pressure space.<br><br> We use right preconditioner J*P^(-1)=[F B^T; B 0] * [F B^T; 0 Sp]^(-1), and is it possible for Petsz to build and implement this precondioner P? Since (Sp)^(-1)=-(Mp)^(-1) Fp(Ap)^(-1), is it possible that we can solve (Mp)^(-1) and (Ap)^(-1) by CG method separately inside preconditioner P. <br></div></div></blockquote><div><br></div><div>Take a look at the PCFIELDSPLIT preconditioner. I think you want the LSC option for that (<a href="http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/PC/PCLSC.html">http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/PC/PCLSC.html</a>)</div><div>if I am reading your mail correctly.</div><div><br></div><div> Thanks,</div><div><br></div><div> Matt</div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div dir="ltr">Any suggestion will be highly appreciated. Thank you so much!<br><br>Best,<br>Guoyi <br> </div></div>
</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature">What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br>-- Norbert Wiener</div>
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