[Ipopt] IPOPT Questions on DOF and feasibility

[Andreas Waechter]

Hi David,

1) You can try to set the option fixed_variable_treatment to relax_bounds. 
This will not eliminate the variables and Ipopt should at least run 
iterations, but it's not clear how it will behavoe... good luck!

2) Beside Uwe's suggestion, you can also try to decrease the values of the 
bound_push and bound_frac options to permute the starting point less.

Andreas

On Fri, 20 May 2011, David Veerasingam wrote:

> Hello all,
>
> I have a few questions about IPOPT's operation.
>
> 1) IPOPT sometimes exits when the number of constraints exceed the number of
> variables (it complains that there are too few degrees of freedom). This
> sometimes happens in the final steps of a shrinking horizon MPC application
> (where variables at each instance are successively fixed as one marches
> toward the endpoint). GRG solvers like CONOPT however continue to chug on,
> even when there are limited degrees of freedom. I understand IPOPT's
> behavior arises because it drops fixed variables from the problem. Is there
> a setting to prevent IPOPT from dropping the fixed variables when solving an
> NLP?
>
> When IPOPT exits on a "too few degrees of freedom" error, I do kind of think
> it's the correct response from a mathematical point of view (the problem is
> probably incorrectly modeled in some sense). But it would be great if it
> could be made to proceed gracefully nonetheless, especially in control
> applications.
>
> 2) IPOPT sometimes converges to an infeasible point from a mere change in
> objective function. I'm having trouble understanding why this can happen.
> For instance, I solve an NLP once with a setpoint tracking objective, and it
> converges to an optimal solution. Using that solution as an initial guess
> (the default behavior in AMPL), I change the objective function to an
> economic objective function and re-solve (everything else stays exactly the
> same), and it converges to an infeasible point. If I start from a feasible
> point, doesn't the filter method ensure I don't lose feasibility?
>
> Extra info: my ipopt.opt file is as follows.
>
> linear_solver ma27
> max_iter 12000
> print_level 4
> tol 1e-8
> acceptable_tol 1e-6
> expect_infeasible_problem yes
> linear_system_scaling mc19
> linear_scaling_on_demand no
> warm_start_init_point yes
> mu_strategy adaptive
>
> I'd be grateful if someone could shed some light on these issues. Thank you
> so much.
>