# Is there a gradient descent-based optimization algorithm that works with non-linear constraints?

I have a function to optimize with ca. 200 parameters + one constraint (sum of squares of the parameters must be equal one)

This problem can be solved using Lagrange Multipliers and my intuition tells me, that methods that do that must be readily available.

If I had a choice, I would prefer an algorithm existing on JuMP.jl