# Iterator Interface

The iterator interface gives the possibility of stepping through the numerical steps of the continuation procedure. It thus allows to inject custom monitoring function (saving, plotting, bifurcation detection, ...) at will and during the continuation run. In short, it allows to completely re-write the continuation algorithm as one sees fit and this, in a straightforward manner.

The general method `continuation`

is built upon this iterator interface and we refer to the source code for a complete example of use.

The iterator provided below does not compute eigenvalues nor perform bifurcations detection.

## Initialization

More information about

iteratorscan be found on the page of julialang.

The interface is set by defining an iterator, pretty much in the same way one calls `continuation`

:

`iter = ContIterable(prob, alg, opts; kwargs...)`

## Stepping

Once an iterator `iter`

has been defined, one can step through the numerical continuation using a for loop:

```
for state in iter
println("Continuation step = ", state.step)
end
```

The `state::ContState`

has the following description. It is a mutable object which holds the current state of the continuation procedure from which one can step to the next state.

The for loop stops when `done(iter, state)`

returns `false`

. The condition which is implemented is basically that the number of iterations should be smaller than `maxIter`

, that the parameters should be in `(p_min, p_max)`

...

`BifurcationKit.ContState`

— Type`state = ContState(ds = 1e-4,...)`

Returns a variable containing the state of the continuation procedure. The fields are meant to change during the continuation procedure.

**Arguments**

`z_pred`

current solution on the branch`converged`

Boolean for newton correction`τ`

tangent predictor`z`

previous solution`itnewton`

Number of newton iteration (in corrector)`step`

current continuation step`ds`

step size`stopcontinuation`

Boolean to stop continuation

**Useful functions**

`copy(state)`

returns a copy of`state`

`copyto!(dest, state)`

returns a copy of`state`

`getsolution(state)`

returns the current solution (x, p)`getx(state)`

returns the x component of the current solution`getp(state)`

returns the p component of the current solution`getpreviousp(state)`

returns the p component of the previous solution`is_stable(state)`

whether the current state is stable

You can also call `continuation(iter)`

to have access to the regular continuation method used throughout the tutorials.

## Basic example

We show a quick and simple example of use. Note that it is not very optimized because of the use of global variables.

```
using BifurcationKit, Plots
const BK = BifurcationKit
k = 2
# functional we want to study
F(x, p) = (@. p + x - x^(k+1)/(k+1))
# bifurcation problem
prob = BifurcationProblem(F, [0.8], 1., (@lens _))
# parameters for the continuation
opts = ContinuationPar(dsmax = 0.1, dsmin = 1e-3, ds = -0.001, max_steps = 130, p_min = -3., p_max = 3., newton_options = NewtonPar(tol = 1e-8))
# we define an iterator to hold the continuation routine
iter = BK.ContIterable(prob, PALC(), opts; verbosity = 2)
resp = Float64[]
resx = Float64[]
# this is the PALC algorithm
for state in iter
# we save the current solution on the branch
push!(resx, getx(state)[1])
push!(resp, getp(state))
end
# plot the result
plot(resp, resx; label = "", xlabel = "p")
```

## Additional information

If you want to customize the iterator to your needs, perhaps the best source of inspiration is the code of the function `continuation!(it::ContIterable, state::ContState, contRes::ContResult)`

where the iterator is used at its fullest. You will see how the eigen-elements and the stability are computed, how bifurcations are detected and how results are saved.