[Ipopt] Testing IPOPT on a black block problem

[Michael Stevens]

Dear IPOPT Users,

I am currently looking at IPOPT as a solver for NLP I have. The problem is fairly simple and currently I am just trying to 
determine how best to represent the problem and how efficiently it can be solved.

I order to go this I am trying to avoid (at least initially) determining analytical gradiants and Hessians. Effectively I would 
like to treat the problem function as a black box where gradiants and Hessians are not directly available.

Wadding though the documents I am almost clear on how to define the NLP problem so a Quasi-Newton method will be used fur the 
Hessian. However looking at the documentation for how the NLP is defined (in my case in C++) I cannot see how the 'eval_grad_f' 
function should be defined in the case where the Quasi-Newton evaluation of the function to be optimised should be used.

I guess there are two possibilities:
a) IPOPT always needs an analytical gradient
b) The eval_grad_f function is not called if IPOPT options are set for a Quasi-Newton solution.

Any help would be greatly appreciated.

Thanks,
	Michael Stevens
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Michael Stevens Systems Engineering

34128 Kassel, Germany
Phone/Fax: +49 561 5218038
Mobile: +49 1577 7807325

Navigation Systems, Estimation  and
                 Bayesian Filtering
    http://bayesclasses.sf.net
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