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F-1 method

License: MIT License

Julia 100.00%
julia optimization solver iterative-methods dual-numbers hyperdual-numbers autodifferentiation sensitivity-analysis modeling optimizer

f1method.jl's Issues

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Make it faster by turning off iterative refinements

Maybe this should be turned off by default for this method? I am unsure how to deal with the non-sparse case, but this is clearly useful in the case where the Jacobian is sparse, and can be done via

SuiteSparse.UMFPACK.umf_ctrl[8] = 0 # turn iterative refinements off

See issue #31105
Maybe add this in the docs/readme?

Change from DiffEqBase to SciMLBase

DiffEqBase is the lowest common denominator for the DiffEq packages, not necessarily the whole SciML ecosystem, and so it has a lot DiffEq dependencies. These are generally not required by downstream packages. If what you're looking for is a way to define problems without having most dependencies, we recommend you use SciMLBase as the dependency since everything like ODEProblem, SteadyStateProblem, etc. is defined there. We basically recommend depending on SciMLBase for problem definitions, and solver packages for specific solvers, but generally most non-SciML packages should not be depending on DiffEqBase directly (given the split of SciMLBase in 2021)

For more details see: https://diffeq.sciml.ai/stable/features/low_dep/ and https://discourse.julialang.org/t/psa-the-right-dependency-to-reduce-from-differentialequations-jl/72757

Let me know if you need any help updating this, though for almost all dependents here it should be a trivial name change as you're actually using pieces from SciMLBase.

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