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View Code? Open in Web Editor NEWDifferential Inequality Algorithms for Parametric ODEs
License: Other
Differential Inequality Algorithms for Parametric ODEs
License: Other
Look into using Yingkai Song's adjoint method for generating subgradients discussed in the "Generating Subgradients for Bounding in Global Dynamic Optimization" talk at AIChE 2020.
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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.
(Note that your case is probably a bit different from the normal one because you're also depending on DiffEqSensitivity, which I would recommend and I would like to know why that's the case to hopefully fix it).
See SciML/SciMLSensitivity.jl#665 . It looks like the interfaces you use are unchanged so it should be a very simple update.
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