Instead of using sum(x) = 1 as an equality constraint, perhaps you can directly
take x(1) = 1 - sum(x(2:end)) and substitute it into the problem directly.
Best,
Grey
> On Apr 27, 2016, at 2:31 AM, David Morris <otha...@othalan.net> wrote:
>
> Good idea, but no luck. But that does have me thinking on different ways to
> formulate the constraint. Maybe one of them will bypass AUGLAG limitations.
>
> David
>
> On Wed, Apr 27, 2016 at 12:43 PM, Jacob Biesinger <jake.biesin...@gmail.com>
> wrote:
> Again, I'm no expert, but you could always separate the equality constraint
> into two inequality constraints, yes? Maybe worth a try.
>
>
> On Tue, Apr 26, 2016, 10:37 PM David Morris <otha...@othalan.net> wrote:
> I've been looking through the NLOpt code for AUGLAG. If I understand
> correctly, it seems that my problem is AUGLAG will not efficiently find a
> solution which fits my constraints and so never finds a valid solution (not
> even a bad one) within time limits I have defined. It usually finds a
> solution eventually, just not within an acceptable time for our project. The
> difficulty appears to be that a constraint of "sum(x) == 100" is not a type
> of constraint which works well with the algorithm.
>
> In particular, I am looking at auglag.c lines 251 - 268 where results are
> saved, but which potentially is not called if interrupted too early by a
> timeout.
>
> Does this make sense? Any suggestions for alternative ways to write this
> constraint?
>
> David
>
> On Tue, Apr 26, 2016 at 7:57 PM, David Morris <otha...@othalan.net> wrote:
> 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
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