is there distributions with variable parameters in julia?

Question

Is there any way that static distributions are used for objective function and constraints? if so, which solvers are suitable to optimize them? thanks for your kindly help:).

sig=0.86;

@variable(ALT,k>=0);
@variable(ALT,i>=0);

@constraint(ALT,c1,400*cdf(Normal(0,1),-k)<=1);
f=(1-cdf(Normal(0,1),k-sig*sqrt(i))+cdf(Normal(0,1),-k-sig*sqrt(i)));
@objective(ALT,Min,f);
status=solve(ALT);    ```

Answer 1

Use a user-defined function: https://jump.dev/JuMP.jl/v0.21.1/nlp/#User-defined-Functions-1

using JuMP, Distributions, Ipopt

f(x) = cdf(Normal(0, 1), x)

model = Model(Ipopt.Optimizer)
JuMP.register(model, :f, 1, f; autodiff = true)
@variable(model, k >= 0)
@variable(model, i >= 0)
@NLconstraint(model, f(-k) <= 1)
@NLobjective(model, Min, 1 - f(k - sqrt(i)) + f(-k - sqrt(i)))
optimize!(model)