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MadNLP currently only supports a second-order algorithm, which requires Hessian info. The current issue with user-registered functions in JuMP is that JuMP does not provide Hessian info for the user-registered functions. As such, we don't have a method to query the necessary information. We're planning to support the quasi-Newton method, which doesn't require Hessian, but we don't have a concrete timeline yet (see #39).
As described here: https://discourse.julialang.org/t/unsupported-feature-hess-with-user-defined-functions-using-jump-and-alpine/70204/2
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