kwargs in continuation cause illegal instruction about bifurcationinference.jl

[rveltz]

This call

ContinuationPar{eltype(u₀),AbstractLinearSolver,EigenSolver}

is strange. You should never use AbstractLinearSolver, it is used for dispatch. Looking at Newton.jl, I understand why you where mislead.

Something like the following should be favoured:

u₀, _, stable = Cont.newton( u -> f(u,p₀), u₀, Cont.NewtonPar(verbose=false) )
optCont = Cont.ContinuationPar{eltype(u₀),typeof(DefaultLS()), typeof(DefaultEig())}(
			pMin=kwargs[:pMin],pMax=kwargs[:pMax],ds=kwargs[:ds],
			maxSteps=kwargs[:maxSteps])

[rveltz]

It seems they did not implement broadcasting:

u0 = param([-2.0,0.0])
u1 = copy(u0)

All of this fails except the last one:

u0 .= u0 .- u1
axpy!(2,u0,u1)
u0 = u0 .- u1

It comes from the fact that copyto! is not implemented. This is troublesome because typeof(u0) <: AbstractArray and hence, all my BorderedArrays methods will be called, like this one.

So you have to extend minus!(x::TrackedArray, y::TrackedArray) = x = x .- y. However, in newton, the return type of d by the linear solver is not TrackedArray. So I define:

minus!(x::TrackedArray, y) = x = x .- y

Now the code works but newton does not converge. At least it does not error!

[rveltz]

The branch errors because of finiteDifferences. You can either import finiteDifferences and redefine it for TrackedArrays or use the function fdTracked I put on master.