[Ipopt] accessing Lagrange multipliers in all callback routines

[Joel Andersson]

Hi Ian!

Why not add a slack variable and minimize a linear objective?

minimize c
subject to
(x_min,-inf) <= (x,c) <= (x_max,inf),
c-f(x) = 0,
g_min <= g(x) <= g_max

Best regards,
Joel



2014-06-26 2:41 GMT+02:00 Ian Washington <washinid at mcmaster.ca>:

> Hello All,
>
> I currently have an NLP with expensive constraint function evaluations
> which requires the solution of an embedded ODE or DAE (via an adaptive
> integration solver) (i.e., dynamically-constrained NLP).
>
> Ideally I would like to evaluate the embedded DAE states/sensitivities
> only once for a given set of NLP variables (and only if these variables
> change) per major optimization function eval (i.e., objective,
> constraint, jacobian, hessian callbacks). This is pretty straight
> forward using global variables where, for example, I would evaluate all
> required functions (objective, constraints) and first derivatives in
> lets say ipopt's objective function callback and then make these results
> available in the remaining callback routines via global variables.
>
> However, when generating exact second order information (of the
> constraints involving the embedded model states) using, for example, a
> forward over adjoint second order sensitivity analysis, I need to pass
> the Lagrange multipliers of the constraints in question to the embedded
> DAE sensitivity solver. So, if I am to generate this second order
> information within ipopt's objective function callback I would need
> access to the internal Lagrange multipliers. Currently it seems only
> possible to get this information directly within the Lagrangian hessian
> callback.
>
> So my question is, does anybody know of any possible way to make the
> internal Lagrange multipliers available to callback routines other than
> just the hessian callback.
>
> Thanks,
> Ian.
>
>
>
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Joel Andersson, PhD
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