# Non-simple branch point

The general method is exposed in Golubitsky, Martin, David G Schaeffer, and Ian Stewart. **Singularities and Groups in Bifurcation Theory**. New York: Springer-Verlag, 1985, VI.1.d page 295

An example of use of the methods presented here is provided in 2d generalized Bratu–Gelfand problem.

We expose our method to study *non-simple branch points*. Such branch point $(x_0,p_0)$ for the problem $F(x,p)=0$ satisfies $d=\dim \ker dF(x_0,p_0) > 1$ and the eigenvalues have zero imaginary part. At such point, we can apply **Lyapunov-Schmidt** reduction to transform the initial problem in large dimensions to a $d$-dimensional polynomial equation, called the **reduced equation**.

More precisely, it is possible to write $x = u + v$ where $u\in \ker dF(x_0,p_0)$ and $v\approx 0$ belongs to a vector space complement of $\ker dF(x_0,p_0)$. It can be shown that $u$ solves $\Phi(u,\delta p)=0$ with $\Phi(u,\delta p) = (I-\Pi)F(u+\psi(u,\delta p),p_0+\delta p)$ where $\psi$ is known implicitly and $\Pi$ is the spectral projector on $\ker dF(x_0,p_0)$. Fortunately, one can compute the Taylor expansion of $\Phi$ up to order 3. Computing the bifurcation diagram of this $d$-dimensional multivariate polynomials can be done using brute force methods.

Once the zeros of $\Phi$ have been located, we can use them as initial guess for `continuation`

but for the original $F$ !!

## Reduced equation computation

The reduced equation (E) can be automatically computed as follows

`get_normal_form(br::ContResult, ind_bif::Int ; verbose = false, ζs = nothing, lens = getlens(br))`

where `prob`

is a bifurcation problem. `br`

is a branch computed after a call to `continuation`

with detection of bifurcation points enabled and `ind_bif`

is the index of the bifurcation point on the branch `br`

. The above call returns a point with information needed to compute the bifurcated branch. For more information about the optional parameters, we refer to `get_normal_form`

. It returns a point with all requested information:

```
mutable struct NdBranchPoint{Tv, T, Tevl, Tevr, Tnf} <: BranchPoint
"bifurcation point"
x0::Tv
"Parameter value at the bifurcation point"
p::T
"Right eigenvectors"
ζ::Tevr
"Left eigenvectors"
ζstar::Tevl
"Normal form coefficients"
nf::Tnf
"Type of bifurcation point"
type::Symbol
end
```

## Using the reduced equation

Once a branch point has been computed `bp = get_normal_form(...)`

, you can do all sort of things.

- For example, quoted from the file
`test/testNF.jl`

, you can print the 2d reduced equation as follows:

```
julia> BifurcationKit.nf(bp2d)
2-element Array{String,1}:
" + (3.23 + 0.0im) * x1 * p + (-0.123 + 0.0im) * x1^3 + (-0.234 + 0.0im) * x1 * x2^2"
" + (-0.456 + 0.0im) * x1^2 * x2 + (3.23 + 0.0im) * x2 * p + (-0.123 + 0.0im) * x2^3"
```

You can evaluate the reduced equation as

`bp2d(Val(:reducedForm), rand(2), 0.2)`

. This can be used to find all the zeros of the reduced equation by sampling on a grid or using a general solver like`Roots.jl`

.Finally, given a $d$-dimensional vector $x$ and a parameter $\delta p$, you can have access to an initial guess $u$ (see above) by calling

`bp2d(rand(2), 0.1)`

## Predictor

The predictor for a non trivial guess at distance $\delta p$ from the bifurcation point is provided by the method

Missing docstring for `BifurcationKit.predictor(bp::BifurcationKit.NdBranchPoint, δp::T; verbose::Bool = false, ampfactor = T(1), nbfailures = 30, maxiter = 100, perturb = identity, J = nothing, normN = norminf, optn::NewtonPar) where T`

. Check Documenter's build log for details.

Missing docstring for `BifurcationKit.predictor(bp::BifurcationKit.NdBranchPoint, ::Val{:exhaustive}, δp::T; verbose::Bool, ampfactor, nbfailures, maxiter, perturb, J, igs, normN, optn::NewtonPar) where T`

. Check Documenter's build log for details.