Hello, I am using Optim.jl to solve a constrained optimization problem. The gradient is not specified, so finite differences are the default. This works nicely for the objective, but not for the constraints. Thank you! Here ist my mwe: using Optim, Test function fun(x) # objective (1.0…

here is a function that implements autodiff on contraints: function con_c!(c, x) c[1]= x[1]^2 + x[2]^2 # 1st constraint c[2]= x[2]* sin(x[1])-x[1] # 2nd constraint c end function constraint_derivatives(fn!, lx, ux, lc, uc) nc = length(lc) nx = length(lx) cached_c = zeros(eltype(…

Optim: gradient and Hessian of constraints

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pkofod November 25, 2019, 4:15pm 2

No one has implemented AD for those. It should be relatively simple though.

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How to properly specify Jacobian & Hessian functions of inequality constraints (Optim)