mewilhel
commented
2 years ago Thanks, that sounds easy enough. I'll take a look into this shortly.
With respect to DiffEqSensitivity:
- Basically, we're using DiffEqSensitivity to get sensitivity information. For lower bounding problems in optimization, we can use this to construct subgradients of relaxations of composite functions which let us linearize a relaxed optimization problem (and solve a linear program vs a nonlinear program).
- For discretize-then-relax methods that account for truncation error or continuous time methods, sensitivity information is required by a local NLP solver in order to solve the problem to feasibility (and construct upper bounds).
Is there a preferred way to depend on DiffEqSensitivity.jl?
ChrisRackauckas
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).