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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
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<p class="MsoNormal">Hi,</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">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?</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Bruce Palmer</p>
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