rafaqz
commented
4 months ago Ok here's a MWE with rosenbrock:
julia> rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
rosenbrock (generic function with 2 methods)
julia> x0 = zeros(2)
2-element Vector{Float64}:
0.0
0.0
julia> lb = fill(-1.0, 2)
2-element Vector{Float64}:
-1.0
-1.0
julia> ub = fill(200.0, 2)
2-element Vector{Float64}:
200.0
200.0
julia> _p = [1.0, 100.0]
2-element Vector{Float64}:
1.0
100.0
julia>
julia> l1 = rosenbrock(x0, _p)
1.0
julia> prob = OptimizationProblem(rosenbrock, x0, _p; lb, ub)
OptimizationProblem. In-place: true
u0: 2-element Vector{Float64}:
0.0
0.0
julia> solve(prob, NelderMead())
ERROR: MethodError: objects of type Nothing are not callable
Stacktrace:
[1] (::OptimizationOptimJL.var"#20#24"{OptimizationCache{β¦}})(G::Vector{Float64}, ΞΈ::Vector{Float64})
@ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/yMF3E/src/OptimizationOptimJL.jl:291
[2] gradient!!(obj::OnceDifferentiable{Float64, Vector{Float64}, Vector{Float64}}, x::Vector{Float64})
@ NLSolversBase ~/.julia/packages/NLSolversBase/kavn7/src/interface.jl:63
[3] gradient!
@ ~/.julia/packages/NLSolversBase/kavn7/src/interface.jl:51 [inlined]
[4] gradient!(obj::Optim.BarrierWrapper{OnceDifferentiable{Float64, Vector{Float64}, Vector{Float64}}, Optim.BoxBarrier{Vector{Float64}, Vector{Float64}}, Float64, Float64, Vector{Float64}}, x::Vector{Float
64})
@ Optim ~/.julia/packages/Optim/EJwLF/src/multivariate/solvers/constrained/fminbox.jl:118
[5] optimize(df::OnceDifferentiable{β¦}, l::Vector{β¦}, u::Vector{β¦}, initial_x::Vector{β¦}, F::Fminbox{β¦}, options::Optim.Options{β¦})
@ Optim ~/.julia/packages/Optim/EJwLF/src/multivariate/solvers/constrained/fminbox.jl:327
[6] __solve(cache::OptimizationCache{β¦})
@ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/yMF3E/src/OptimizationOptimJL.jl:306
[7] solve!
@ ~/.julia/packages/SciMLBase/wVDwN/src/solve.jl:179 [inlined]
[8] #solve#596
@ ~/.julia/packages/SciMLBase/wVDwN/src/solve.jl:96 [inlined]
[9] solve(::OptimizationProblem{β¦}, ::NelderMead{β¦})
@ SciMLBase ~/.julia/packages/SciMLBase/wVDwN/src/solve.jl:93
[10] top-level scope
@ REPL[104]:1
Some type information was truncated. Use `show(err)` to see complete types.
Vaibhavdixit02
JohannesNaegele
Describe the bug π
When optimizing with
NelderMead()things work completely fine unless I uselbandubkeywords. When I try to enforce bounds (between zero and one for all parameters) Optimizatin.jl for some reason callsgrad!some way into the optimization, but it looks likecache.f.gradhere isnothingforNelderMead: https://github.com/SciML/Optimization.jl/blob/038c7b6bd34111cd4a2fdb74886bf7c05a026c30/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl#L291I'm not sure why
grad!is called on a gradient free method.Expected behavior
The optimisation should run as normal with
lb/ubargsMinimal Reproducible Example π
I have a complicated simulation behind the objective function, and the error only comes half way through optimizing it. So, not sure how to make an MWE. I'm not sure how this can happen or what is triggering it.
But this is essentially it, without the exact objective func:
Error & Stacktrace β οΈ
Environment (please complete the following information):
using Pkg; Pkg.status()using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)versioninfo()Additional context
Add any other context about the problem here.