# ForwardDiff failing on simple polynomial

**URL:** <https://discourse.julialang.org/t/forwarddiff-failing-on-simple-polynomial/6127>\
**Category:** New to Julia\
**Created:** [September 28, 2017, 12:47am UTC](https://discourse.julialang.org/t/forwarddiff-failing-on-simple-polynomial/6127 "2017-09-28T00:47:44Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Stuart](https://avatars.discourse-cdn.com/v4/letter/s/cab0a1/32.png) [@Stuart](https://discourse.julialang.org/u/Stuart)\
**Post date:** [September 28, 2017, 12:47am UTC](https://discourse.julialang.org/t/forwarddiff-failing-on-simple-polynomial/6127/1 "2017-09-28T00:47:44Z")

</div>

Hello everybody,

I am trying to use ForwardDiff for a project, but I am having problems getting it to work even for a simple case. Please see screen scrape below. It’s entirely possible I don’t know exactly how to use ForwardDiff, so I am appealing for help in case I am doing something wrong.

Note that I have played around for a while with different types just in case something fiddly is happening, but I haven’t found a magic set of Float64s and Ints which appease the gradient method.

Is it possible that when I installed ForwardDiff, I got an obsolete version of the package?

Thanks for your insights,  
Stuart

julia\> using ForwardDiff

julia\> g = x::Vector → 1 + 2.0_x + 3.0_x.^2.0  
(::#1) (generic function with 1 method)

julia\> phi = x → ForwardDiff.gradient(g, x)  
(::#3) (generic function with 1 method)

julia\> y = [2.0, 3.0, 4.0]  
3-element Array{Float64,1}:  
2.0  
3.0  
4.0

julia\> g(y)  
3-element Array{Float64,1}:  
17.0  
34.0  
57.0

julia\> phi(y)  
ERROR: MethodError: no method matching extract\_gradient!(::Array{Array{ForwardDiff.Dual{3,Float64},1},1}, ::Array{ForwardDiff.Dual{3,Float64},1})  
Closest candidates are:  
extract\_gradient!(::AbstractArray{T,N}, ::ForwardDiff.Dual{N,T\<:Real}) at /home/sbrorson/.julia/v0.5/ForwardDiff/src/gradient.jl:40  
extract\_gradient!(::AbstractArray{T,N}, ::Real) at /home/sbrorson/.julia/v0.5/ForwardDiff/src/gradient.jl:39  
in vector\_mode\_gradient(::##1#2, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{3,Float64,Array{ForwardDiff.Dual{3,Float64},1}}) at /home/sbrorson/.julia/v0.5/ForwardDiff/src/gradient.jl:62  
in gradient(::##1#2, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{3,Float64,Array{ForwardDiff.Dual{3,Float64},1}}) at /home/sbrorson/.julia/v0.5/ForwardDiff/src/gradient.jl:7  
in gradient(::##1#2, ::Array{Float64,1}) at /home/sbrorson/.julia/v0.5/ForwardDiff/src/gradient.jl:6  
in (::##3#4)(::Array{Float64,1}) at ./REPL[3]:1

julia\> versioninfo()  
Julia Version 0.5.0-rc4+0  
Commit 9c76c3e\* (2016-09-09 01:43 UTC)  
Platform Info:  
System: Linux (x86\_64-redhat-linux)  
CPU: Intel(R) Core™ i5-3320M CPU @ 2.60GHz  
WORD\_SIZE: 64  
BLAS: libopenblas (USE64BITINT DYNAMIC\_ARCH NO\_AFFINITY Sandybridge)  
LAPACK: libopenblas64\_  
LIBM: libopenlibm  
LLVM: libLLVM-3.7.1 (ORCJIT, ivybridge)

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<div class="post-metadata">

**Author:** ![ohsonice](https://avatars.discourse-cdn.com/v4/letter/o/d6d6ee/32.png) [@ohsonice](https://discourse.julialang.org/u/ohsonice)\
**Post date:** [September 28, 2017, 1:27am UTC](https://discourse.julialang.org/t/forwarddiff-failing-on-simple-polynomial/6127/2 "2017-09-28T01:27:00Z")

</div>

The answer is in the documentation for gradient: (type `?` into the repl to access the documentation)

```julia
help?> ForwardDiff.gradient
  ForwardDiff.gradient(f, x::AbstractArray, cfg::GradientConfig = GradientConfig(f, x))

  Return ∇f evaluated at x, assuming f is called as f(x).

  This method assumes that isa(f(x), Real).

```

The method assumes the return of `g` is a `Real`. The gradient operator is most commonly used to represent the differential of a function of the form: f: \mathbb R^n \to \mathbb R .

Your g returns a vector- try instead using the `jacobian` function:

```julia
julia> g = x::Vector -> 1 + 2.0x + 3.0x.^2.0
(::#1) (generic function with 1 method)

julia> phi = x-> ForwardDiff.jacobian(g,x)
(::#5) (generic function with 1 method)

julia> y = [2.0, 3.0, 4.0]
3-element Array{Float64,1}:
 2.0
 3.0
 4.0

julia> phi(y)
3×3 Array{Float64,2}:
 14.0 0.0 0.0
  0.0 20.0 0.0
  0.0 0.0 26.0

```