Brendan, have you used JuMP? It's primarily designed for constrained problems, and it works great. Don't be afraid of constraints if they help you express your problem in a cleaner way or with more of the structure preserved. For things like constant parameter values though, JuMP supports "fixed" variables which get sent to the solvers as if they're parameters. Most good constrained solvers have presolve routines that can detect trivial constraints like single-variable equalities and will do the substitution automatically, but doesn't hurt to provide information in a more specific way.
On Friday, October 23, 2015 at 8:03:27 AM UTC-7, Brendan Tracey wrote: > > > 2. (JuMP Specific) - Should I specify my known positions as model >> variables with equality constraints, or just normal julia variables that >> show up in my objective function? >> > > Don't specify them as equality constraints. Build your function with those > variables removed from the optimization altogether. Perhaps that's what you > meant by "normal julia variables"? In any event, unconstrained optimization > is much more robust and mature, and you will likely get better results the > fewer constraints you give. >
