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<p>This is the output:</p>
<p></p>
<div>
<blockquote> Residual norms for mg_levels_1_ solve.<br>
0 KSP Residual norm 2.249726733143e+00<br>
1 KSP Residual norm 1.433120400946e+00<br>
2 KSP Residual norm 1.169262560123e+00<br>
3 KSP Residual norm 1.323528716607e+00<br>
4 KSP Residual norm 5.006323254234e-01<br>
5 KSP Residual norm 3.569836784785e-01<br>
6 KSP Residual norm 2.493182937513e-01<br>
7 KSP Residual norm 3.038202502298e-01<br>
8 KSP Residual norm 2.780214194402e-01<br>
9 KSP Residual norm 1.676826341491e-01<br>
10 KSP Residual norm 1.209985378713e-01<br>
11 KSP Residual norm 9.445076689969e-02<br>
12 KSP Residual norm 8.308555284580e-02<br>
13 KSP Residual norm 5.472865592585e-02<br>
14 KSP Residual norm 4.357870564398e-02<br>
15 KSP Residual norm 5.079681292439e-02<br>
Linear mg_levels_1_ solve converged due to CONVERGED_ITS iterations 15<br>
Residual norms for mg_levels_1_ solve.<br>
0 KSP Residual norm 5.079681292439e-02<br>
1 KSP Residual norm 2.934938644003e-02<br>
2 KSP Residual norm 3.257065831294e-02<br>
3 KSP Residual norm 4.143063876867e-02<br>
4 KSP Residual norm 4.822471409489e-02<br>
5 KSP Residual norm 3.197538246153e-02<br>
6 KSP Residual norm 3.461217019835e-02<br>
7 KSP Residual norm 3.410193775327e-02<br>
8 KSP Residual norm 4.690424294464e-02<br>
9 KSP Residual norm 3.366148892800e-02<br>
10 KSP Residual norm 4.068015727689e-02<br>
11 KSP Residual norm 2.658836123104e-02<br>
12 KSP Residual norm 2.826244186003e-02<br>
13 KSP Residual norm 2.981793619508e-02<br>
14 KSP Residual norm 3.525455091450e-02<br>
15 KSP Residual norm 2.331539121838e-02<br>
Linear mg_levels_1_ solve converged due to CONVERGED_ITS iterations 15<br>
Residual norms for mg_levels_1_ solve.<br>
0 KSP Residual norm 2.421498365806e-02<br>
1 KSP Residual norm 1.761072112362e-02<br>
2 KSP Residual norm 1.400842489042e-02<br>
3 KSP Residual norm 1.419665483348e-02<br>
4 KSP Residual norm 1.617590701667e-02<br>
5 KSP Residual norm 1.354824081005e-02<br>
6 KSP Residual norm 1.387252917475e-02<br>
7 KSP Residual norm 1.514043102087e-02<br>
8 KSP Residual norm 1.275811124745e-02<br>
9 KSP Residual norm 1.241039155981e-02<br>
10 KSP Residual norm 9.585207801652e-03<br>
11 KSP Residual norm 9.022641230732e-03<br>
12 KSP Residual norm 1.187709152046e-02<br>
13 KSP Residual norm 1.084880112494e-02<br>
14 KSP Residual norm 8.194750346781e-03<br>
15 KSP Residual norm 7.614246199165e-03<br>
Linear mg_levels_1_ solve converged due to CONVERGED_ITS iterations 15<br>
Residual norms for mg_levels_1_ solve.<br>
0 KSP Residual norm 7.614246199165e-03<br>
1 KSP Residual norm 5.620014684145e-03<br>
2 KSP Residual norm 6.643368363907e-03<br>
3 KSP Residual norm 8.708642393659e-03<br>
4 KSP Residual norm 6.401852907459e-03<br>
5 KSP Residual norm 7.230576215262e-03<br>
6 KSP Residual norm 6.204081601285e-03<br>
7 KSP Residual norm 7.038656665944e-03<br>
8 KSP Residual norm 7.194079694050e-03<br>
9 KSP Residual norm 6.353576889135e-03<br>
10 KSP Residual norm 7.313589502731e-03<br>
11 KSP Residual norm 6.643320423193e-03<br>
12 KSP Residual norm 7.235443182108e-03<br>
13 KSP Residual norm 4.971292307201e-03<br>
14 KSP Residual norm 5.357933842147e-03<br>
15 KSP Residual norm 5.841682994497e-03<br>
Linear mg_levels_1_ solve converged due to CONVERGED_ITS iterations 15<br>
</blockquote>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Barry Smith <bsmith@petsc.dev><br>
<b>Sent:</b> 29 September 2025 15:56:33<br>
<b>To:</b> Moral Sanchez, Elena<br>
<b>Cc:</b> Mark Adams; petsc-users<br>
<b>Subject:</b> Re: [petsc-users] setting correct tolerances for MG smoother CG at the finest level</font>
<div> </div>
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<div>
<div><br>
</div>
I asked you to run with
<div><br>
<blockquote type="cite">
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<blockquote class="gmail_quote" style="margin: 0px 0px 0px 0.8ex; border-left-width: 1px; border-left-style: solid; border-left-color: rgb(204, 204, 204); padding-left: 1ex;">
-ksp_monitor -mg_levels_ksp_monitor -ksp_converged_reason -mg_levels_ksp_converged_reason</blockquote>
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</blockquote>
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</blockquote>
<div><br>
</div>
you chose not to, delaying the process of understanding what is happening.
<div><br>
</div>
<div> Please run with those options and send the output. My guess is that you are computing the "residual norms" in your own monitor code, and it is doing so differently than what PETSc does, thus resulting in the appearance of a sufficiently small residual
norm, whereas PETSc may not have calculated something that small.</div>
<div><br>
</div>
<div>Barry</div>
<div><br>
<div><br>
<blockquote type="cite">
<div>On Sep 29, 2025, at 8:39 AM, Moral Sanchez, Elena <Elena.Moral.Sanchez@ipp.mpg.de> wrote:</div>
<br class="Apple-interchange-newline">
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<div>Thanks for the hint. I agree that the coarse solve should be much more "accurate". However, for the moment I am just trying to understand what the MG is doing exactly. </div>
<div><br>
I am puzzled to see that the fine grid smoother ("lvl 0") does not stop when the residual becomes less than 1e-1. It should converge due to the atol. </div>
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<div id="divRplyFwdMsg" dir="ltr" style="caret-color: rgb(0, 0, 0); font-family: Helvetica; font-size: 18px; font-style: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none;">
<font face="Calibri, sans-serif" style="font-size: 11pt;"><b>From:</b><span class="Apple-converted-space"> </span>Mark Adams <<a href="mailto:mfadams@lbl.gov">mfadams@lbl.gov</a>><br>
<b>Sent:</b><span class="Apple-converted-space"> </span>29 September 2025 14:20:56<br>
<b>To:</b><span class="Apple-converted-space"> </span>Moral Sanchez, Elena<br>
<b>Cc:</b><span class="Apple-converted-space"> </span>Barry Smith; petsc-users<br>
<b>Subject:</b><span class="Apple-converted-space"> </span>Re: [petsc-users] setting correct tolerances for MG smoother CG at the finest level</font>
<div> </div>
</div>
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<div dir="ltr">Oh I see the coarse grid solver in your full solver output now.
<div>You still want an accurate coarse grid solve. Usually (the default in GAMG) you use a direct solver on one process, and cousin until the coarse grid is small enough to make that cheap.</div>
</div>
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<div dir="ltr" class="gmail_attr">On Mon, Sep 29, 2025 at 8:07 AM Moral Sanchez, Elena <<a href="mailto:Elena.Moral.Sanchez@ipp.mpg.de">Elena.Moral.Sanchez@ipp.mpg.de</a>> wrote:<br>
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<div>Hi, I doubled the system size and changed the tolerances just to show a better example of the problem. This is the output of the callbacks in the first iteration:<br>
CG Iter 0/1 | res = 2.25e+00/1.00e-09 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 0/15 | res = 2.25e+00/1.00e-01 | 0.3 s<br>
MG lvl 0 (s=884): CG Iter 1/15 | res = 1.43e+00/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 2/15 | res = 1.17e+00/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 3/15 | res = 1.32e+00/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 4/15 | res = 5.01e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 5/15 | res = 3.57e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 6/15 | res = 2.49e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 7/15 | res = 3.04e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 8/15 | res = 2.78e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 9/15 | res = 1.68e-01/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 10/15 | res = 1.21e-01/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 11/15 | res = 9.45e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 12/15 | res = 8.31e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 13/15 | res = 5.47e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 14/15 | res = 4.36e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 15/15 | res = 5.08e-02/1.00e-01 | 0.1 s<br>
ConvergedReason MG lvl 0: 4<br>
MG lvl -1 (s=524): CG Iter 0/15 | res = 8.15e-02/1.00e-01 | 3.0 s<br>
ConvergedReason MG lvl -1: 3<br>
MG lvl 0 (s=884): CG Iter 0/15 | res = 5.08e-02/1.00e-01 | 0.3 s<br>
MG lvl 0 (s=884): CG Iter 1/15 | res = 2.93e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 2/15 | res = 3.26e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 3/15 | res = 4.14e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 4/15 | res = 4.82e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 5/15 | res = 3.20e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 6/15 | res = 3.46e-02/1.00e-01 | 0.3 s<br>
MG lvl 0 (s=884): CG Iter 7/15 | res = 3.41e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 8/15 | res = 4.69e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 9/15 | res = 3.37e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 10/15 | res = 4.07e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 11/15 | res = 2.66e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 12/15 | res = 2.83e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 13/15 | res = 2.98e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 14/15 | res = 3.53e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 15/15 | res = 2.33e-02/1.00e-01 | 0.2 s<br>
ConvergedReason MG lvl 0: 4<br>
CG Iter 1/1 | res = 2.42e-02/1.00e-09 | 5.6 s<br>
MG lvl 0 (s=884): CG Iter 0/15 | res = 2.42e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 1/15 | res = 1.76e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 2/15 | res = 1.40e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 3/15 | res = 1.42e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 4/15 | res = 1.62e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 5/15 | res = 1.35e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 6/15 | res = 1.39e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 7/15 | res = 1.51e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 8/15 | res = 1.28e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 9/15 | res = 1.24e-02/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 10/15 | res = 9.59e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 11/15 | res = 9.02e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 12/15 | res = 1.19e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 13/15 | res = 1.08e-02/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 14/15 | res = 8.19e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 15/15 | res = 7.61e-03/1.00e-01 | 0.1 s<br>
ConvergedReason MG lvl 0: 4<br>
MG lvl -1 (s=524): CG Iter 0/15 | res = 1.38e-02/1.00e-01 | 5.2 s<br>
ConvergedReason MG lvl -1: 3<br>
MG lvl 0 (s=884): CG Iter 0/15 | res = 7.61e-03/1.00e-01 | 0.2 s<br>
MG lvl 0 (s=884): CG Iter 1/15 | res = 5.62e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 2/15 | res = 6.64e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 3/15 | res = 8.71e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 4/15 | res = 6.40e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 5/15 | res = 7.23e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 6/15 | res = 6.20e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 7/15 | res = 7.04e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 8/15 | res = 7.19e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 9/15 | res = 6.35e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 10/15 | res = 7.31e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 11/15 | res = 6.64e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 12/15 | res = 7.24e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 13/15 | res = 4.97e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 14/15 | res = 5.36e-03/1.00e-01 | 0.1 s<br>
MG lvl 0 (s=884): CG Iter 15/15 | res = 5.84e-03/1.00e-01 | 0.1 s<br>
ConvergedReason MG lvl 0: 4 <br>
CG ConvergedReason: -3 <br>
<br>
For completeness, I add here the -ksp_view of the whole solver:<br>
KSP Object: 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=1, nonzero initial guess<br>
tolerances: relative=1e-08, absolute=1e-09, divergence=10000.<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: 1 MPI process<br>
type: mg<br>
type is MULTIPLICATIVE, levels=2 cycles=v<br>
Cycles per PCApply=1<br>
Not using Galerkin computed coarse grid matrices<br>
Coarse grid solver -- level 0 -------------------------------<br>
KSP Object: (mg_coarse_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=15, nonzero initial guess<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_coarse_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=524, cols=524<br>
Python: Solver_petsc.LeastSquaresOperator<br>
Down solver (pre-smoother) on level 1 -------------------------------<br>
KSP Object: (mg_levels_1_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=15, nonzero initial guess<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_levels_1_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=884, cols=884<br>
Python: Solver_petsc.LeastSquaresOperator<br>
Up solver (post-smoother) same as down solver (pre-smoother)<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=884, cols=884<br>
Python: Solver_petsc.LeastSquaresOperator<br>
<br>
Regarding Mark's Email: What do you mean with "the whole solver doesn't have a coarse grid"? I am using my own Restriction and Interpolation operators.<br>
Thanks for the help,<br>
Elena</div>
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<div id="m_-3704621968652170651divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size: 11pt;"><b>From:</b><span class="Apple-converted-space"> </span>Mark Adams <<a href="mailto:mfadams@lbl.gov" target="_blank">mfadams@lbl.gov</a>><br>
<b>Sent:</b><span class="Apple-converted-space"> </span>28 September 2025 20:13:54<br>
<b>To:</b><span class="Apple-converted-space"> </span>Barry Smith<br>
<b>Cc:</b><span class="Apple-converted-space"> </span>Moral Sanchez, Elena; petsc-users<br>
<b>Subject:</b><span class="Apple-converted-space"> </span>Re: [petsc-users] setting correct tolerances for MG smoother CG at the finest level</font>
<div> </div>
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<div dir="ltr">Not sure why your "whole"solver does not have a coarse grid but this is wrong:
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<blockquote>
<div>KSP Object: (mg_coarse_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=100, initial guess is zero<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30</div>
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<div>The coarse grid has to be accurate. The defaults are a good place to start: max_it=10.000, rtol=1e-5, atol=1e-30 (ish)</div>
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<div dir="ltr" class="gmail_attr">On Fri, Sep 26, 2025 at 3:21 PM Barry Smith <<a href="mailto:bsmith@petsc.dev" target="_blank">bsmith@petsc.dev</a>> wrote:<br>
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<div> Looks reasonable. Send the output running with
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<div> -ksp_monitor -mg_levels_ksp_monitor -ksp_converged_reason -mg_levels_ksp_converged_reason<br id="m_-3704621968652170651m_5159186700595764030lineBreakAtBeginningOfMessage">
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<div>On Sep 26, 2025, at 1:19 PM, Moral Sanchez, Elena <<a href="mailto:Elena.Moral.Sanchez@ipp.mpg.de" target="_blank">Elena.Moral.Sanchez@ipp.mpg.de</a>> wrote:</div>
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<div style="margin-top: 0px; margin-bottom: 0px;">Dear Barry,</div>
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<div style="margin-top: 0px; margin-bottom: 0px;">This is -ksp_view for the smoother at the finest level:</div>
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<blockquote>KSP Object: (mg_levels_1_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=10, nonzero initial guess<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_levels_1_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=524, cols=524<br>
Python: Solver_petsc.LeastSquaresOperator<br>
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And at the coarsest level:<br>
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<div>KSP Object: (mg_coarse_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=100, initial guess is zero<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_coarse_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=344, cols=344<br>
Python: Solver_petsc.LeastSquaresOperator</div>
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And for the whole solver:</div>
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<div>KSP Object: 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=100, nonzero initial guess<br>
tolerances: relative=1e-08, absolute=1e-09, divergence=10000.<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: 1 MPI process<br>
type: mg<br>
type is MULTIPLICATIVE, levels=2 cycles=v<br>
Cycles per PCApply=1<br>
Not using Galerkin computed coarse grid matrices<br>
Coarse grid solver -- level 0 -------------------------------<br>
KSP Object: (mg_coarse_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=100, initial guess is zero<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_coarse_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=344, cols=344<br>
Python: Solver_petsc.LeastSquaresOperator<br>
Down solver (pre-smoother) on level 1 -------------------------------<br>
KSP Object: (mg_levels_1_) 1 MPI process<br>
type: cg<br>
variant HERMITIAN<br>
maximum iterations=10, nonzero initial guess<br>
tolerances: relative=0.1, absolute=0.1, divergence=1e+30<br>
left preconditioning<br>
using UNPRECONDITIONED norm type for convergence test<br>
PC Object: (mg_levels_1_) 1 MPI process<br>
type: none<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=524, cols=524<br>
Python: Solver_petsc.LeastSquaresOperator<br>
Up solver (post-smoother) same as down solver (pre-smoother)<br>
linear system matrix = precond matrix:<br>
Mat Object: 1 MPI process<br>
type: python<br>
rows=524, cols=524<br>
Python: Solver_petsc.LeastSquaresOperator</div>
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Best,</div>
<div>Elena</div>
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<font face="Calibri, sans-serif" style="font-size: 11pt;"><b>From:</b><span> </span>Barry Smith <<a href="mailto:bsmith@petsc.dev" target="_blank">bsmith@petsc.dev</a>><br>
<b>Sent:</b><span> </span>26 September 2025 19:05:02<br>
<b>To:</b><span> </span>Moral Sanchez, Elena<br>
<b>Cc:</b><span> </span><a href="mailto:petsc-users@mcs.anl.gov" target="_blank">petsc-users@mcs.anl.gov</a><br>
<b>Subject:</b><span> </span>Re: [petsc-users] setting correct tolerances for MG smoother CG at the finest level</font>
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<div>Send the output using -ksp_view </div>
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<div>Normally one uses a fixed number of iterations of smoothing on level with multigrid rather than a tolerance, but yes PETSc should respect such a tolerance.</div>
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<div>Barry</div>
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<div>On Sep 26, 2025, at 12:49 PM, Moral Sanchez, Elena <<a href="mailto:Elena.Moral.Sanchez@ipp.mpg.de" target="_blank">Elena.Moral.Sanchez@ipp.mpg.de</a>> wrote:</div>
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<div>Hi, <br>
I am using multigrid (multiplicative) as a preconditioner with a V-cycle of two levels. At each level, I am setting CG as the smoother with certain tolerance.<br>
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What I observe is that in the finest level the CG continues iterating after the residual norm reaches the tolerance (atol) and it only stops when reaching the maximum number of iterations at that level. At the coarsest level this does not occur and the CG stops
when the tolerance is reached.<br>
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I double-checked that the smoother at the finest level has the right tolerance. And I am using a Monitor function to track the residual.<br>
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Do you know how to make the smoother at the finest level stop when reaching the tolerance?<br>
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Cheers,<br>
Elena.</div>
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