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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,
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<p class="p1"><span style="font-size:11.0pt;font-family:"Aptos",sans-serif">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 </span><span class="s1">TaoView(tao, PETSC_VIEWER_STDOUT_SELF,ierr) in the code and it reports back<o:p></o:p></span></p>
<p class="p1"><span class="s1"><o:p> </o:p></span></p>
<p class="p1"><span class="s1">Tao Object: 1 MPI process</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">type: cg</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">CG Type: prp</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Gradient steps: 0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Reset steps: 0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">TaoLineSearch Object: 1 MPI process</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">type: more-thuente</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">maximum function evaluations=30</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">tolerances: ftol=0.0001, rtol=1e-10, gtol=0.9</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">total number of function evaluations=0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">total number of gradient evaluations=0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">total number of function/gradient evaluations=0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Termination reason: 0</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">convergence tolerances: gatol=1e-08,
</span><span class="apple-converted-space"> </span><span class="s1">steptol=0., </span>
<span class="apple-converted-space"> </span><span class="s1">gttol=0.</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Residual in Function/Gradient:=7.54237e+75</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Objective value=2.96082e+86</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">total number of iterations=0,</span><span class="apple-converted-space">
</span><span class="s1">(max: 100)</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">total number of function/gradient evaluations=1,</span><span class="apple-converted-space">
</span><span class="s1">(max: 4000)</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"> </span><span class="s1">Solution converged:</span><span class="apple-converted-space">
</span><span class="s1">||g(X)||/|f(X)| <= grtol</span><o:p></o:p></p>
<p class="p1"><o:p> </o:p></p>
<p class="p1">Bruce<o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><b><span style="color:black">From:
</span></b><span style="color:black">Barry Smith <bsmith@petsc.dev><br>
<b>Date: </b>Wednesday, June 26, 2024 at 2:02</span><span style="font-family:"Arial",sans-serif;color:black"> </span><span style="color:black">PM<br>
<b>To: </b>Palmer, Bruce J <Bruce.Palmer@pnnl.gov><br>
<b>Cc: </b>petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov><br>
<b>Subject: </b>Re: [petsc-users] Unconstrained optimization question<o:p></o:p></span></p>
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<span style="font-family:"Arial",sans-serif;color:black">Check twice before you click! This email originated from outside PNNL.</span><span style="font-family:"Arial",sans-serif"><o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"> Please run with -tao_monitor -tao_converged_reason and see why it has stopped.
<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"> Barry<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">On Jun 26, 2024, at 4:34<span style="font-family:"Arial",sans-serif"> </span>PM, Palmer, Bruce J via petsc-users <petsc-users@mcs.anl.gov> wrote:<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">Hi,<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">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.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">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.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">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?<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt"> <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">Bruce Palmer<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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