I needed to add a call to TaoSetFromOptions to get the runtime options to work.
I also set grtol from the default value of 1.0e-7 to 1.0e-15. The calculation
goes for a few iterations, but it looks like it keeps pushing into territory
where the objective function blows up. It eventually quits with a line search
error. The complete output from both the runtime options and the TaoView
command looks like
4 TAO, Function value: 9.24265e+85, Residual: 3.11789e+76
TAO solve did not converge due to DIVERGED_LS_FAILURE iteration 4
Tao Object: 1 MPI process
type: cg
CG Type: prp
Gradient steps: 0
Reset steps: 4
TaoLineSearch Object: 1 MPI process
type: more-thuente
maximum function evaluations=30
tolerances: ftol=0.0001, rtol=1e-10, gtol=0.9
total number of function evaluations=0
total number of gradient evaluations=0
total number of function/gradient evaluations=0
Termination reason: -3
convergence tolerances: gatol=1e-08, steptol=0., gttol=0.
Residual in Function/Gradient:=3.11789e+76
Objective value=9.24265e+85
total number of iterations=4, (max: 100)
total number of function/gradient evaluations=20, (max: 4000)
Solver terminated: -6 Line Search Failure
I’ll have to bite the bullet and convert everything into ps, nm, and au to get
values down to a range where they are all of order 1 to get this to work.
Bruce
From: Jed Brown <[email protected]>
Date: Wednesday, June 26, 2024 at 3:02 PM
To: Palmer, Bruce J <[email protected]>, Barry Smith <[email protected]>
Cc: [email protected] <[email protected]>
Subject: Re: [petsc-users] Unconstrained optimization question
You can use the PETSC_OPTIONS environment variable to specify options if you
don't pass the command line arguments through.
You can set -tao_grtol smaller to handle this difference in scales between the
objective and the gradient, though applying some nondimensionalization/choice
of appropriate units is still recommended.
"Palmer, Bruce J via petsc-users" <[email protected]> writes:
> This is a fortran code that doesn’t make use of argc,argv (I tried running
> with the runtime options anyway, in case you implemented some magic I’m not
> familiar with, but didn’t see anything new in the output). I have a call to
> TaoView(tao, PETSC_VIEWER_STDOUT_SELF,ierr) in the code and it reports back
>
>
>
> Tao Object: 1 MPI process
>
> type: cg
>
> CG Type: prp
>
> Gradient steps: 0
>
> Reset steps: 0
>
> TaoLineSearch Object: 1 MPI process
>
> type: more-thuente
>
> maximum function evaluations=30
>
> tolerances: ftol=0.0001, rtol=1e-10, gtol=0.9
>
> total number of function evaluations=0
>
> total number of gradient evaluations=0
>
> total number of function/gradient evaluations=0
>
> Termination reason: 0
>
> convergence tolerances: gatol=1e-08, steptol=0., gttol=0.
>
> Residual in Function/Gradient:=7.54237e+75
>
> Objective value=2.96082e+86
>
> total number of iterations=0, (max: 100)
>
> total number of function/gradient evaluations=1, (max: 4000)
>
> Solution converged: ||g(X)||/|f(X)| <= grtol
>
>
>
> Bruce
>
> From: Barry Smith <[email protected]>
> Date: Wednesday, June 26, 2024 at 2:02 PM
> To: Palmer, Bruce J <[email protected]>
> Cc: [email protected] <[email protected]>
> Subject: Re: [petsc-users] Unconstrained optimization question
> Check twice before you click! This email originated from outside PNNL.
>
>
> Please run with -tao_monitor -tao_converged_reason and see why it has
> stopped.
>
> Barry
>
>
>
> On Jun 26, 2024, at 4:34 PM, Palmer, Bruce J via petsc-users
> <[email protected]> wrote:
>
> This Message Is From an External Sender
> This message came from outside your organization.
> Hi,
>
> I’m trying to do an unconstrained optimization on a molecular scale problem.
> Previously, I was looking at an artificial molecular problem where all
> parameters were of order 1 and so the objective function and variables were
> also in the range of 1 or at least within a few orders of magnitude of 1.
>
> More recently, I’ve been trying to apply this optimization to a real
> molecular system. Between Avogadro’s number (6.022e23) and Boltzmann’s
> constant (1.38e-16) combined with very small distances (1.0e-8 cm), etc. the
> objective function values and the values of the optimization variables have
> very large values (~1e86 and ~1e9, respectively). I’ve verified that the
> analytic gradients of the objective function that I’m calculating are correct
> by comparing them with numerical derivatives.
>
> I’ve tried using the LMVM and Conjugate Gradient optimizations, both of which
> worked previously, but I find that the optimization completes one objective
> function evaluation and then declares that the problem is converged and
> stops. I could find a set of units where everything is approximately 1 but I
> was hoping that there are some parameters I can set in the optimization that
> will get it moving again. Any suggestions?
>
> Bruce Palmer
