[Ipopt] how to get around an over-determined equality constrained NLP formulation
Ian Washington
washinid at mcmaster.ca
Fri Mar 1 15:45:10 EST 2013
Hello IPOPT community,
I am trying to pose an NLP where the number of equality constraints is
greater than the number of decision variables (i.e., an over-determined
system) and the solver immediately exits with a
Not_Enough_degrees_Of_Freedom error. Basically there are some equality
constraints that fix some of the variables.
Clearly my problem is formulation related and I could reformulate the
equality constraints to two inequality constraints using a small
tolerance, but I rather not if possible.
Note, I'm using the matlab interface to ipopt and directly specifying
all constraints/sparsity pattern, but I'm using the limited-memory
Hessian approximation.
My first question: does the matlab interface expose all possible ipopt
solver options to the user ? (I assume so, since I didn't receive any
errors)
Second, I tried to change the fixed_variable_treatment to relax_bounds
but it didn't seem to help. I thought this would at least prevent the
Not_Enough_degrees_Of_Freedom error flag from occurring.
Note, I've tried this problematic formulation using knitro and the
solver proceeds with only a warning stating that there are more equality
constraints than variables. Also, snopt handles the problem with no issues.
Does anyone have any ideas to get around this problem.
Thanks,
Ian.
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