Add consistent scaling for optimization problems #29571
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Job Documentation, step Docs: sync website on 904462c wanted to post the following: View the site here This comment will be updated on new commits. |
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| // This is to prevent automatic scaling of the adjoint system. Scaling is | ||
| // taken from the forward system | ||
| if (_nl_adjoint.hasVector("scaling_factors")) |
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Just to be clear, the above error is checking for manual scaling and this if statement is stripping away adjoint variable scaling when automatic_scaling=true.
| nl_abs_tol = 1e-10 | ||
| l_tol = 1e-10 | ||
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| automatic_scaling = false |
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does automatic_scaling=true override the manual variable scaling?
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Does all of this still work when they use residual_vs_jacobian scaling of somethign other than the default, like 1 or 0.5 or 0?
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Yes works with all of those
| input = main.i | ||
| jsondiff = main_out.json | ||
| recover = false | ||
| cli_args = "MultiApps/forward/cli_args='Executioner/automatic_scaling=true'" |
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Same question as above, does this override the manual scaling?
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Yes, when setting verbose=true, it outputs the new scalings which are different from the manual.
| @@ -52,6 +53,14 @@ AdjointSolve::AdjointSolve(Executioner & ex) | |||
| paramError("forward_system", "Forward system does not appear to be a 'NonlinearSystem'."); | |||
| if (!dynamic_cast<NonlinearSystem *>(&_nl_adjoint)) | |||
| paramError("adjoint_system", "Adjoint system does not appear to be a 'NonlinearSystem'."); | |||
| // Adjoint system should never perform it's own automatic scaling. Scaling is | |||
| // taken from the forward system | |||
| _nl_adjoint.automaticScaling(false); | |||
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do you need this and the below if statement on 197 or do you include both to be extra careful?
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I include both to be careful.
| @@ -95,6 +104,10 @@ AdjointSolve::solve() | |||
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| // Solve the adjoint system | |||
| solver.adjoint_solve(matrix, solution, rhs, tol, maxits); | |||
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Line 109 is a really smart way of doing this. For my clarification, the adjoint solve in line 106 is using the scaled system matrix from the forward system on line 90. Do you think it matters that our solution is from A^T=lambda.
We should try this with a nonsymmetric A. Regular scaling of the system Ax=b would be AD^-1Dx=b. The adjoint system would be A^Tx=b so (AD^-1D)^Tx=b -> (D^TD^-TA^T)x=b.
So I wonder if D needs to be transposed for the adjoint solve. Maybe this doesn't matter because D is for blocks and not columns of matrix.
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Do we have an example of a nonsymmetric A in the tests that I could "borrow"
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Also a diagonal matrix transposed is just the same diagonal matrix.
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I didn't think of that. You're right. I'll try to get a thermal mechanical example we can try.
| input = main.i | ||
| max_threads = 1 # Optimize executioner does not support multiple threads | ||
| cli_args = "MultiApps/forward/cli_args='Variables/T_real_adj/scaling=5'" | ||
| expect_err = 'User supplied scaling factors for adjoint variables. Adjoint system is scaled automatically by the forward system.' |
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This will fix the failing tests if I copied it in correctly.
| expect_err = 'User supplied scaling factors for adjoint variables. Adjoint system is scaled automatically by the forward system.' | |
| expect_err = 'User cannot supply scaling factors for adjoint variables. Adjoint system is scaled automatically by the forward system.' |
closes #29570