I am using NLOpt in a python application and am having problems when
setting an equality constraint when using LN_AUGLAG.
If I use LN_COBYLA, optimization works perfectly. However, if I use
LN_AUGLAG and set LN_COBYLA as the local optimizer, the result is the exact
same as my initial guess. My goal is to use experiment with other
optimization algorithms (for example, LN_SBPLX), and use AUGLAG to provide
the quality constraint.
Can anyone help determine why the equality constraint causes AUGLAG to fail?
Below is sample code showing how I use NLOpt.
###################################################################################
# CODE Sample:
args = ( ... )
kw = { ... }
# Optimization Function for NLOpt
def optfunc(x,grad):
# Big complex function which calculates a single return value:
val = model_p_mixer(x,*args,**kw)
return val
# Constraint Function for NLOpt
# x -> percentage of total content for each component
# sum(x) == 100 %
def opt_constraint(x, grad):
val = float(100.0 - x.sum())
return val
gopt = nlopt.opt(nlopt.LN_AUGLAG, len(guess))
lopt = nlopt.opt(nlopt.LN_COBYLA, len(guess))
gopt.set_min_objective(optfunc)
gopt.set_lower_bounds([98.62, 0.0, 0.0])
gopt.set_upper_bounds([99.5, 1.0, 1.0])
gopt.add_equality_constraint(opt_constraint, 0.001)
# Set tolerances to determine when the optimizer stops looking for
solutions
gopt.set_xtol_abs(1E-6)
gopt.set_ftol_abs(0.001)
lopt.set_xtol_abs(1E-6)
lopt.set_ftol_abs(0.001)
# Set initial step size
gopt.set_initial_step(0.01)
gopt.set_maxtime(8.0)
gopt.set_local_optimizer(lopt)
# Run the optimizer
mix = gopt.optimize([99.0, 0.5, 0.5])
# Initial Guess : [99.0, 0.5 , 0.5 ]
# Expected Result: [99.2, 0.35, 0.45]
# Actual Result: [99.0, 0.5 , 0.5 ]
###################################################################################
Thank you,
David
_______________________________________________
NLopt-discuss mailing list
NLopt-discuss@ab-initio.mit.edu
http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss
