JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
This is a tool for developers who use Julia and need to compute how small changes in inputs affect the outputs of complex systems. It helps when standard automatic differentiation methods fail or are too slow, especially for systems involving external solvers, mutating operations, or iterative procedures like optimization algorithms. Developers using Julia for numerical analysis, machine learning, or scientific computing would find this useful.
141 stars.
Use this if you are a Julia developer and need to calculate derivatives for functions where the output is defined by underlying conditions, especially when standard automatic differentiation tools struggle.
Not ideal if you are not a Julia developer or if your functions are straightforward enough for standard automatic differentiation to work efficiently.
Stars
141
Forks
9
Language
Julia
License
MIT
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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