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Constraint Optimization by Linear Approximation

Constraint Optimization by Linear Approximation

Constraint Optimization by Linear Approximation

Hi Fortran users!

I am trying to solve an optimization problem where I need to choose 150 variables to maximize an objective function subject to some non-linear constraints. In my program there is one main constraint and all the other ones just require that the choice variables be strictly positive.

Is there a way to take care of the strictly positive constraints without having to add 150 constraints to the code (some kind of a common requirement to keep the choice variables greater or equal to, say 10^-6?

Also, how is it related to the code provided online at NLopt:
"call nlo_create(opt, NLOPT_LD_MMA, 2)
call nlo_get_lower_bounds(ires, opt, lb)
lb(2) = 0.0
call nlo_set_lower_bounds(ires, opt, lb)
call nlo_set_min_objective(ires, opt, myfunc, 0)"

What is lb(2) here? Where is it coming from?

Thanks for any help with this!

RE: Constraint Optimization by Linear Approximation

Not sure what you're asking - are you asking for the meaning of lb(2) or what is lb(2).

lb(2) is the 2nd element of the array lb - where it comes from and what is it (real, integer, double precision): check your declarations.

As for the meaning of lb: no idea whatsoever: check your code comments

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