# Type unstable in NonlinearSolve.jl
**URL:** https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533
**Category:** Optimization (Mathematical)
**Tags:** code\_warntype, type-stability
**Created:** [September 18, 2024, 1:13am UTC](https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533 "2024-09-18T01:13:15Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@aaoo](https://discourse.julialang.org/u/aaoo)
#### Post date: [September 18, 2024, 1:13am UTC](https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533/1 "2024-09-18T01:13:15Z")
I am using NonlinearSolve.jl to solve a system of nonlinear equations. The `@code_warntype` macro gives `Any` type when I use `NonlinearProblem`, see code below. When I wrapped this code into a larger outer function, the type instability propagated. How can I solve this instability?
Thanks for your time.
```julia
using StaticArrays
using NonlinearSolve
x1 = SVector(5.410765991201068, -1.955858432452871, 1.236281448472692)
x2 = SVector(9.190671981350006, -0.858583463255322, 0.250942726005373)
x3 = SVector(8.86055136300633, 1.989613825349654, -0.69411438796056)
x4 = SVector(4.978916963046636, 1.770025873857406, 0.0)
x5 = SVector(6.56268038475586, -0.632855678126095, 4.82114644313298)
x6 = SVector(9.791606329419208, 0.304474410427785, 3.979436153631626)
x7 = SVector(9.446657214279373, 3.280608222903378, 2.991928566693289)
x8 = SVector(6.13083135660143, 3.093028628184183, 3.584864994660288)
xp = SVector(6.07558940271011, -1.08983308562881, 2.09667267469191)
x0 = SVector{3,Float64}((0, 0, 0))
targetfun(x, p) = @. (p.C + p.D * x[1]) * x[2] + (p.E + p.F * x[1]) * x[3] + (p.G + p.H * x[1]) * x[2] * x[3] - (p.A + p.B * x[1])
p = (A=xp - x6,
B=x6 - x5,
C=x7 - x6,
D=x6 - x5 + x8 - x7,
E=x2 - x6,
F=x6 - x5 + x1 - x2,
G=x3 - x2 + x6 - x7,
H=x2 - x1 + x5 - x6 + x7 - x8 + x4 - x3)
prob = NonlinearProblem(targetfun, x0, p)
solve(prob, SimpleNewtonRaphson())
@code_warntype NonlinearProblem(targetfun, x0, p) # type unstable
@code_warntype solve(prob, SimpleNewtonRaphson()) # type stable
function ttt(x0, p)
prob = NonlinearProblem(targetfun, x0, p)
return solve(prob, SimpleNewtonRaphson())
end
@code_warntype ttt(x0, p) # the wrapping function become unstable
```
---
### Author:  [@SteffenPL](https://discourse.julialang.org/u/SteffenPL)
#### Post date: [September 18, 2024, 1:32am UTC](https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533/2 "2024-09-18T01:32:01Z")
Just guessing, but these `XYProblem` types are pretty big such that the type might not be reasonable inferable beforehand.
You could consider passing the problem instance and using `remake` if you need to solve similar nonlinear problems repeatedly. If you only need to solve one, then the type instability might be irrelevant anyway.
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [September 18, 2024, 2:27am UTC](https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533/3 "2024-09-18T02:27:24Z")
> [@aaoo](#):
>
> `NonlinearProblem(targetfun, x0, p)`
`NonlinearProblem{false}(targetfun, x0, p)` setting the inplaceness directly can be required.
---
### Author:  [@aaoo](https://discourse.julialang.org/u/aaoo)
#### Post date: [September 18, 2024, 2:45am UTC](https://discourse.julialang.org/t/type-unstable-in-nonlinearsolve-jl/119533/4 "2024-09-18T02:45:17Z")
> [@SteffenPL](#):
>
> You could consider passing the problem instance and using `remake` if you need to solve similar nonlinear problems repeatedly.
> [@ChrisRackauckas](#):
>
> `NonlinearProblem{false}(targetfun, x0, p)` setting the inplaceness directly can be required.
Thanks to @SteffenPL and @ChrisRackauckas . Both solutions work. However, the second does not need an initial instance.