Steven G. Johnson wrote:
>> Regarding constraints, the suggestion was to "manually" substitute my
>> variables with combinations of exp()-expressions that would implicitly
>> take care of the r_i>0 and 0<A_j<1 constraints.
>> Question: Does NLopt allow to do those optimizations in a more direct,
>> less "manual" and still easy-to-use way ?
I just noticed that the constraints you mentioned are not even
nonlinear; they are just bound constraints on the variables. All of the
NLopt algorithms support bound constraints; these are relatively easy to
include in an optimization algorithm. (Penalty functions for bound
constraints are definitely overkill.)
In your email, you also mentioned a constraint
A1+A2+A3 == 1 and 0<=Ai<=1.
An equality constraint like this is much easier to handle via
elimination. Just set A3 = 1 - A1 - A2, eliminating the A3 parameter,
and include inequality constraints 0 <= 1 - A1 - A2 <= 1 (this is not
a bound constraint, so you would have to use NLopt's general
inequality-constraint support).
Steven
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