Hi Martijn -<br><br>Using the new_x flag is definitely the correct approach. I have a problem similar to yours in that evaluating the system state for a given set of optimization variables is costly, but once this is done, evaluating each of f, g, grad_f, jac_g is relatively easy. <br>
<br>I include the following as class variables in MyNLP.cpp : my_f, my_g, my_grad_f, my_jac_g. Then, if new_x is true, I call a function to update my model and evaluate all of (my_f, my_g, etc.), and then for example the function eval_f would copy my_f to f. If new_x is false, then the function just needs to copy what is currently in my_f to f.<br>
<br>In case I wasn't clear above, here is pseudocode of eval_f:<br><br>bool MyNLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)<br>{<br> if(new_x)<br> {<br> evaluate(<lots of variables>, my_f, my_g, my_df, my_dg);<br>
}<br> obj_value = my_f;<br>}<br><br>Since these are class variables, they are visible to the class functions, so you don't need to explicitly create a structure or pass these variables into the functions. It is a fairly clean implementation.<br>
<br>- Seth<br><br><div class="gmail_quote">On Mon, Sep 10, 2012 at 3:57 AM, Martijn Disse <span dir="ltr"><<a href="mailto:M.W.Disse@student.tudelft.nl" target="_blank">M.W.Disse@student.tudelft.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div>
<div style="direction:ltr;font-size:10pt;font-family:Tahoma">Hi,<br>
<br>
Thank you for your response. Just for sake of completeness on this topic: the documentation mentions x_new as:
<div><br>
<i>"new_x: (in), false if any evaluation method was previously called with the same values in x, true otherwise."</i>
<div><i><br>
</i></div>
<div>I will investigate how to implement this for my particular case.</div>
<div><br>
</div>
<div>Best regards,</div>
<div><br>
</div>
<div>-Martijn</div>
<div><i><br>
</i>________________________________________<br>
Van: Stefan Vigerske [<a href="mailto:stefan@math.hu-berlin.de" target="_blank">stefan@math.hu-berlin.de</a>]<br>
Verzonden: maandag 10 september 2012 10:39<br>
To: Martijn Disse<br>
Cc: <a href="mailto:ipopt@list.coin-or.org" target="_blank">ipopt@list.coin-or.org</a><br>
Onderwerp: Re: [Ipopt] Algorithm flow calling f(x) and g(x), same decision variables?<div><div class="h5"><br>
<br>
Hi,<br>
<br>
I think Ipopt does not want to give promises on which order functions<br>
are evaluated.<br>
You can check the newx flag that is passed with each function evaluation<br>
to see whether *some* evaluation function has been called for the same<br>
point already.<br>
<br>
Stefan<br>
<br>
On 09/10/2012 10:12 AM, Martijn Disse wrote:<br>
> Dear all,<br>
><br>
> In both my cost and constraint function I simulate the same system with an input based on the decision variables and some other non-varying user data.<br>
><br>
> In the case when the cost and constraint function are called with the same decision variables, to avoid redundant computations, I would would not like to simulate the system twice, but only once.<br>
><br>
> I want to use a userdata structure to pass on the simulation results from cost to constraints or vice versa. Therefore I am trying to find out how the algorithm flow works:<br>
><br>
> Does the IPOPT flow ever call cost and constraint with the same decision variables? If so, which one is called first?<br>
><br>
> I have modified the hs071 example and I found out that the constraint function is called first and then the cost function is called with the same decision variables. Is this generally the case?<br>
><br>
> I have looking into the publications, but was not very succesfull in finding an answer to my question. I am guessing it is quite a common problem, that why I am asking you :).<br>
><br>
> I appreciate you help,<br>
><br>
> Best regards,<br>
><br>
> Martijn Disse<br>
><br>
><br>
><br>
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</div>
</div>
</div>
</blockquote></div><br>