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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| README.md | 2025-04-08 | 1.2 kB | |
| v0.7.0 source code.tar.gz | 2025-04-08 | 18.5 kB | |
| v0.7.0 source code.zip | 2025-04-08 | 32.7 kB | |
| Totals: 3 Items | 52.4 kB | 0 | |
What's Changed
- Complete revamp by @gdalle in https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl/pull/160
- Remove mention of (now unsupported) kwargs by @gdalle in https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl/pull/162
- Delete build directory by @gdalle in https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl/pull/163
Main breaking changes
- The
forwardfunction (now calledsolver) must return a byproductzin all cases, which will usually benothing. It used to be optional. For most users, adaptation will only require replacingforward(x) = ybyforward(x) = (y, nothing). - Support for keyword arguments has been removed.
- Forward rules for Enzyme have been dropped (temporarily).
Please refer to the API reference and FAQ for more details.
Main new features
- The ability to run preparation on the differentiation of
conditions. This is useful e.g. to leverage a sparse backend and build Jacobian matrices instead of calling lazy pushforwards and pullbacks
Full Changelog: https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl/compare/v0.6.3...v0.7.0