# Could SciML benefit from a DifferentiationProblem and solvers with caches?
**URL:** https://discourse.julialang.org/t/could-sciml-benefit-from-a-differentiationproblem-and-solvers-with-caches/86267
**Category:** Modelling & Simulations
**Created:** [August 24, 2022, 2:30pm UTC](https://discourse.julialang.org/t/could-sciml-benefit-from-a-differentiationproblem-and-solvers-with-caches/86267 "2022-08-24T14:30:08Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@jt1](https://discourse.julialang.org/u/jt1)
#### Post date: [August 24, 2022, 2:30pm UTC](https://discourse.julialang.org/t/could-sciml-benefit-from-a-differentiationproblem-and-solvers-with-caches/86267/1 "2022-08-24T14:30:08Z")
I’ve been using `SciML` with great success for over a year now. I’m a very big fan.
I hope this doesn’t seem rude, but I have 2 propositions which would improve SciML imo.
I noticed that my main struggle is calculating derivatives efficiently. Especially when sparsity is involved. My first question is, **would `SciML` benefit from a `DifferentiationProblem`?**. Which allows choosing the type of derivative by changing a single line similar to ODEs, PDEs… . (I know of `AbstractDifferentiation.jl`, but it was never finished).
**A problem with this** is that we don’t want to use the known `CommonSolve` interface:
```julia
prob = DifferentiationProblem(func)
solve(prob, SymbolicDerivative(), inputs1...)
solve(prob, SymbolicDerivative(), inputs2...)
```
Because that requires to recalculate the symbolic function for each repeated solve for different inputs.
So i beg the second question, **Would `SciML` benefit from solver caches?**.
i.e. change the `CommonSolve` interface to something like:
```julia
prob = Problem(...)
solver = SymbolicDerivative(prob)
solve(solver, inputs1...)
solve(solver, inputs2...)
```
Similar problems for inefficient repeated solver usage occurs in for example in `Optimization.jl`. Where some solvers require big array preallocations in each solve, for which it would be nice if solver caches exists. (Note: caches for derivatives exist in `OptimizationFunction`)
I know this latter remark seems nitpicky, but in most use-cases i have come accross, I want to reuse a solver many times for different initial conditions. And the custom solvers I use are relatively slow to initialise.
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [August 24, 2022, 3:06pm UTC](https://discourse.julialang.org/t/could-sciml-benefit-from-a-differentiationproblem-and-solvers-with-caches/86267/2 "2022-08-24T15:06:09Z")
> [@jt1](#):
>
> (I know of `AbstractDifferentiation.jl`, but it was never finished).
Really the big thing is to finish that project. Make it so it has cached versions, etc.
> [@jt1](#):
>
> Similar problems for inefficient repeated solver usage occurs in for example in `Optimization.jl`.
Yes, Optimization.jl is missing an `init` version like the other problem types have.
Basically, you’re not being nitpicky. We finally have arrived at interface ideas that work broadly well, but there are certain areas of the ecosystem (NonlinearSolve, Optimization, differentiation) which haven’t “gotten up to speed”. We need to apply all of the interface to those places and it’ll then flow nicely. That will just take work.