# Warning: First function call produced NaNs and ERROR: ArgumentError: matrix contains Infs or NaNs

**URL:** <https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397>\
**Category:** New to Julia\
**Tags:** question, turing, differentialequation\
**Created:** [August 2, 2023, 1:19pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397 "2023-08-02T13:19:08Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [August 2, 2023, 1:19pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/1 "2023-08-02T13:19:08Z")

</div>

Hello everyone,

I’m running inference for my model using Turing.jl. I’m getting this warning and error but i’m not sure from where its coming and how can I get to that point.

Can someone explain what it is about? and how can I troubleshoot.

As it was a long error msg i trimmed it by removing some from middle.

```julia
┌ Warning: First function call produced NaNs. Exiting. Double check that none of the initial conditions, parameters, or timespan values are NaN.
└ @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/SUoOD/src/initdt.jl:131
ERROR: ArgumentError: matrix contains Infs or NaNs
Stacktrace:
  [1] chkfinite
    @ ~/.local/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lapack.jl:86 [inlined]
  [2] generic_lufact!(A::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, pivot::RowMaximum; check::Bool)
    @ LinearAlgebra ~/.local/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:135
  [3] generic_lufact!
    @ ~/.local/julia-1.9.0/share/julia/stdlib/v1.9/LinearAlgebra/src/lu.jl:133 [inlined]
  [4] do_factorization
    @ ~/.julia/packages/LinearSolve/3dHoj/src/factorization.jl:97 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/LinearSolve/3dHoj/src/LinearSolve.jl:96 [inlined]
  [6] solve!(cache::LinearSolve.LinearCache{Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, SciMLBase.NullParameters, LinearSolve.DefaultLinearSolver, LinearSolve.DefaultLinearSolverInit{LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, LinearAlgebra.QRCompactWY{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, Tuple{LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, Vector{Int64}}, Nothing, Nothing, Nothing, SVD{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Cholesky{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Cholesky{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}}, LinearSolve.InvPreconditioner{Diagonal{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}}, Diagonal{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Float64, Bool}, alg::GenericLUFactorization{RowMaximum}; kwargs::Base.Pairs{Symbol, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Tuple{Symbol}, NamedTuple{(:reltol,), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}})
    @ LinearSolve ~/.julia/packages/LinearSolve/3dHoj/src/LinearSolve.jl:92
  [7] solve!
    @ ~/.julia/packages/LinearSolve/3dHoj/src/LinearSolve.jl:92 [inlined]
  [8] macro expansion
    @ ~/.julia/packages/LinearSolve/3dHoj/src/default.jl:313 [inlined]
  [9] solve!(::LinearSolve.LinearCache{Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, SciMLBase.NullParameters, LinearSolve.DefaultLinearSolver, LinearSolve.DefaultLinearSolverInit{LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, LinearAlgebra.QRCompactWY{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, Tuple{LU{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{Int64}}, Vector{Int64}}, Nothing, Nothing, Nothing, SVD{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Cholesky{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Cholesky{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}}, LinearSolve.InvPreconditioner{Diagonal{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}}, Diagonal{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}, Float64, Bool}, ::LinearSolve.DefaultLinearSolver; assump::OperatorAssumptions{Nothing}, kwargs::Base.Pairs{Symbol, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}, Tuple{Symbol}, NamedTuple{(:reltol,), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 11}}}})
    @ LinearSolve ~/.julia/packages/LinearSolve/3dHoj/src/default.jl:306
 [10] solve!
    @ ~/.julia/packages/LinearSolve/3dHoj/src/default.jl:306 [inlined]
 [11] #solve!#6
    @ ~/.julia/packages/LinearSolve/3dHoj/src/common.jl:197 [inlined]
 [12] solve!
    @ ~/.julia/packages/LinearSolve/3dHoj/src/common.jl:196 [inlined]
 [13] #dolinsolve#3
    @ ~/.julia/packages/OrdinaryDiffEq/SUoOD/src/misc_utils.jl:107 [inlined]
 [14] dolinsolve
    @ ~/.julia/packages/OrdinaryDiffEq/SUoOD/src/misc_utils.jl:83 [inlined]
 [59] sample
    @ ~/.julia/packages/Turing/FSBW7/src/inference/Inference.jl:242 [inlined]
 [60] #sample#7
    @ ~/.julia/packages/Turing/FSBW7/src/inference/Inference.jl:238 [inlined]
 [61] sample
    @ ~/.julia/packages/Turing/FSBW7/src/inference/Inference.jl:229 [inlined]
 [62] #sample#6
    @ ~/.julia/packages/Turing/FSBW7/src/inference/Inference.jl:225 [inlined]
 [63] sample(model::DynamicPPL.Model{typeof(fit_CTL), (:data, :prob), (), (), Tuple{Vector{Matrix{Union{Missing, Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, true, Vector{Float64}, ODEFunction{true, SciMLBase.AutoSpecialize, typeof(Affinity), UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}}, Tuple{}, DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, ensemble::MCMCSerial, N::Int64, n_chains::Int64)
    @ Turing.Inference ~/.julia/packages/Turing/FSBW7/src/inference/Inference.jl:217

```

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [August 2, 2023, 1:21pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/2 "2023-08-02T13:21:10Z")

</div>

This is a Julia Base issue that should be fixed in the next patch release (1.9.3?). For now a good way around it is to hardcode `linsolve = LUFactorization()` in the implicit solver choice.

---

<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [August 2, 2023, 1:28pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/3 "2023-08-02T13:28:04Z")

</div>

I did this ` alg = Rodas5P(linsolve = LUFactorization())` but still getting same error.

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [August 2, 2023, 1:29pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/4 "2023-08-02T13:29:57Z")

</div>

`alg = Rodas5P(linsolve = RFLUFactorization())`?

---

<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [August 2, 2023, 1:32pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/5 "2023-08-02T13:32:12Z")

</div>

“For now a good way around it is to hardcode `linsolve = LUFactorization()` in the implicit solver choice.”

you meant that right to put `linsolve = LUFactorization()` in solver?

---

<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [August 2, 2023, 1:38pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/6 "2023-08-02T13:38:28Z")

</div>

Also, I have used same code for inference with different data and i don’t see any error or warning in that case. With this current data that I’m using i face this issue.

Can you explain what is the basis for this error?

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [August 2, 2023, 1:40pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/7 "2023-08-02T13:40:33Z")

</div>

Did you try RFLUFactorization? I forget which one exactly avoided the issue.

> [@Sahil\_Khan](#):
>
> Can you explain what is the basis for this error?

Julia’s Base linear algebra for the backsolve portion did not correctly omit the `check` when you do `check=false`, so the error handling of the solver is avoided because Julia just avoids an error. In all other places we set `check=false` so we don’t get an error and handle the issue manually (by rejecting the time step), but somehow this one spot didn’t get the error disabling correct.

But we already put a patch in for Julia, it’ll come in the next release. For now you just need to use one of the methods that doesn’t hit the dispatch that has the error check disabling disabled.

---

<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [August 2, 2023, 2:13pm UTC](https://discourse.julialang.org/t/warning-first-function-call-produced-nans-and-error-argumenterror-matrix-contains-infs-or-nans/102397/8 "2023-08-02T14:13:32Z")

</div>

`RFLUFactorization` worked thanks :).