# Miscellaneous

## Storing solutions and parameters

`HomotopyContinuation.write_solutions`

— Function`write_solutions(filename, solutions)`

Stores the as a plain text file onto disk. The storage format is as follows. The first line indicates the number of solutions stored followed by a blank line. Then the solutions are stored where each solution is separated by a blank line. Note that the solutions are *always* considered as complex numbers. See `read_solutions`

for reading the solution file back.

Note that this is the same file format as used in Bertini.

**Example**

```
julia> write_solutions("solutions.txt", [[1, 1], [-1, 2]])
shell> cat solutions.txt
2
1 0
1 0
-1 0
2 0
julia> read_solutions("solutions.txt")
2-element Array{Array{Complex{Int64},1},1}:
[1 + 0im, 1 + 0im]
[-1 + 0im, 2 + 0im]
```

`HomotopyContinuation.read_solutions`

— Function`read_solutions(filename)`

Read the solutions stored with `write_solutions`

.

**Example**

```
julia> write_solutions("solutions.txt", [[1, 1], [-1, 2]])
julia> read_solutions("solutions.txt")
2-element Array{Array{Complex{Int64},1},1}:
[1 + 0im, 1 + 0im]
[-1 + 0im, 2 + 0im]
```

`HomotopyContinuation.write_parameters`

— Function`write_parameters(filename, parameters)`

Stores the parameters as a plain text file onto disk. The storage format is as follows. The first line indicates the number of parameter values stored followed by a blank line. Then the parameter values are stored.

See `read_parameters`

Note that this is the same file format as used in Bertini.

**Example**

```
julia> write_parameters("parameters.txt", [2.0, -3.2 + 2im])
shell> cat parameters.txt
2
2.0 0.0
-3.2 2.0
julia> read_parameters("parameters.txt")
2-element Array{Complex{Float64},1}:
2.0 + 0.0im
-3.2 + 2.0im
```

`HomotopyContinuation.read_parameters`

— Function`read_parameters(filename)`

Read the parameters stored with `write_parameters`

.

**Example**

```
julia> write_parameters("parameters.txt", [2.0, -3.2 + 2im])
julia> read_parameters("parameters.txt")
2-element Array{Complex{Float64},1}:
2.0 + 0.0im
-3.2 + 2.0im
```

## Newton's method

`HomotopyContinuation.newton`

— Function```
newton(
F::AbstractSystem,
x₀::AbstractVector,
p = nothing,
norm::AbstractNorm = InfNorm(),
cache::NewtonCache = NewtonCache(F);
options...
)
```

An implemenetation of a local Newton's method with various options to specify convergence criteria. Returns a `NewtonResult`

. The computations are always performed in complex arithmetic with double precision, i.e., using `Complex{Float64}`

. The optional `cache`

argument pre-allocates the necessary memory. This is useful if the method is called repeatedly.

**Options**

`atol::Float64 = 1e-8`

: The method is declared converged if`norm(xᵢ₊₁ - xᵢ) < atol`

.`rtol::Float64 = atol`

: The method is declared converged if`norm(xᵢ₊₁ - xᵢ) < max(atol, rtol * norm(x₀))`

.`max_iters::Int = 20`

: The maximal number of iterations.`extended_precision::Bool = false`

: An optional use of extended precision for the evaluation of`F(x)`

. This can increase the achievable accuracy.`contraction_factor::Float64 = 1.0`

: The Newton updates have to satisfy $|xᵢ₊₁ - xᵢ| < a^{2^{i-1}}|x₁ - x₀|$ for $i ≥ 1$ where $a$ is`contraction_factor`

.`min_contraction_iters::Int = typemax(Int)`

: The minimal number of iterations the`contraction_factor`

has to be satisfied. If after`min_contraction_iters`

many iterations the contraction factor is not satisfied the step is accepted anyway.`max_abs_norm_first_update::Float64 = Inf`

: The initial guess`x₀`

is rejected if`norm(x₁ - x₀) > max_abs_norm_first_update`

`max_rel_norm_first_update::Float64 = max_abs_norm_first_update`

: The initial guess`x₀`

is rejected if`norm(x₁ - x₀) > max_rel_norm_first_update * norm(x₀)`

`HomotopyContinuation.NewtonResult`

— Type`NewtonResult`

Result returned by `newton`

.

**Fields**

`return_code::Symbol`

: Can be`:success`

,`:rejected`

or`:max_iters`

.`x::Vector{ComplexF64}`

: The last value obtained.`accuracy::Float64`

: Estimate of the absolute distance of`x`

to a true zero.`iters::Int`

Number of iterations performed.`contraction_ratio::Float64`

: The value`|xᵢ - xᵢ₋₁| / |xᵢ₋₁ - xᵢ₋₂|`

.

`HomotopyContinuation.is_success`

— Method`is_success(R::NewtonResult)`

Returns `true`

if the `newton`

was successfull.

`HomotopyContinuation.NewtonCache`

— Type`NewtonCache(F::AbstractSystem; optimize_data_structure = true)`

Pre-allocates the necessary memory for `newton`

.

## Norms

`HomotopyContinuation.AbstractNorm`

— Type`AbstractNorm`

An `AbstractNorm`

represents any norm of a vector space. All norms are callable. `norm(x)`

computes the norm of `x`

and `norm(x,y)`

computes the distance `norm(x - y).`

`HomotopyContinuation.InfNorm`

— Type`InfNorm <: AbstractNorm`

The infinity or maximum norm.

`HomotopyContinuation.EuclideanNorm`

— Type`EuclideanNorm <: AbstractNorm`

The Euclidean resp. 2-norm.

`HomotopyContinuation.WeightedNorm`

— Type```
WeightedNorm(d::AbstractVector, norm::AbstractNorm; options...)
WeightedNorm(norm::AbstractNorm, n::Integer; options...)
WeightedNorm(norm::AbstractNorm, x::AbstractVector; options...)
```

A `WeightedNorm`

represents a weighted variant of norm `norm`

of a `n`

-dimensional vector space.`A norm`

`||x||`

`is weighted by introducing a vector of additional weights`

d`such that the new norm is`

`||D⁻¹x||`

`where`

`D`

`is the diagonal matrix with diagonal`

`d`

. The `WeightedNorm`

is desigened to change the weights dynamically by using `init!(::WeightedNorm, x)`

and `update!(::WeightedNorm, x)`

. The weights are there constructed such that $||D⁻¹x|| ≈ 1.0$. The weights can be accessed and changed by indexing.

**Options**

`scale_min = sqrt(eps())`

: The minimal size of`dᵢ`

is`scale_min`

time the (weighted) norm of`x`

.`scale_abs_min = min(scale_min^2, 200 * sqrt(eps()))`

: The absolute minimal size of`dᵢ`

.`scale_max = 1.0 / eps() / sqrt(2)`

: The absolute maximal size of`dᵢ`

`HomotopyContinuation.distance`

— Method`distance(u, v, norm::AbstractNorm)`

Compute the distance ||u-v|| with respect to the given norm `norm`

.

`LinearAlgebra.norm`

— Method`norm(u, norm::AbstractNorm)`

Compute the norm ||u|| with respect to the given norm `norm`

.

`HomotopyContinuation.init!`

— Method`init!(w::WeightedNorm, x::AbstractVector)`

Setup the weighted norm `w`

for `x`

.

`HomotopyContinuation.update!`

— Method`update!(w::WeightedNorm, x)`

Update the weighted norm `w`

for `x`

, this will interpolate between the previous weights and the norm of `x`

.

## Unique points, group actions and multiplicities

`HomotopyContinuation.UniquePoints`

— Type`UniquePoints{T, Id, M}`

A data structure for assessing quickly whether a point is close to an indexed point as determined by the given distances function `M`

. The distance function has to be a *metric*. The indexed points are only stored by their identifiers `Id`

.

```
UniquePoints(v::AbstractVector{T}, id::Id;
metric = EuclideanNorm(),
group_actions = nothing)
```

Initialize the data structure. This *does not* initialize the data structure with the point.

**Example**

```
x = randn(ComplexF64, 4)
permutation(x) = ([x[2]; x[1]; x[3]; x[4]],)
group_actions = GroupActions(permutation)
X = group_actions(x)
# without group actions
unique_points = UniquePoints(x, 1)
HC.add!.(unique_points, X, 1:length(X), 1e-5)
length(unique_points) # 2
unique_points = UniquePoints(x, 1, group_actions = group_actions)
HC.add!.(unique_points, X, 1:length(X), 1e-5)
length(unique_points) # 1
```

`HomotopyContinuation.search_in_radius`

— Method`search_in_radius(unique_points, v, tol)`

Search whether `unique_points`

contains a point `p`

with distances at most `tol`

from `v`

. Returns `nothing`

if no point exists, otherwise the identifier of `p`

is returned.

`HomotopyContinuation.add!`

— Method```
add!(unique_points, v, id; atol = 1e-14, rtol = sqrt(eps()))
add!(unique_points, v, id, atol)
```

Search whether `unique_points`

contains a point `p`

with distances at most `max(atol, norm(v)rtol)`

from `v`

. If this is the case the identifier of `p`

and `false`

is returned. Otherwise `(id, true)`

is returned.

`HomotopyContinuation.multiplicities`

— Function`multiplicities(vectors; metric = EuclideanNorm(), atol = 1e-14, rtol = 1e-8, kwargs...)`

Returns a `Vector{Vector{Int}}`

`v`

. Each vector `w`

in 'v' contains all indices `i`

,`j`

such that `w[i]`

and `w[j]`

have `distance`

at most `max(atol, rtol * metric(0,w[i]))`

. The remaining `kwargs`

are things that can be passed to `UniquePoints`

.

```
julia> multiplicities([[1,0.5], [1,0.5], [1,1]])
[[1,2]]
```

This is the same as

`multiplicities([[1,0.5], [1,0.5], [1,1]]; distance=(x,y) -> LinearAlgebra.norm(x-y))`

Here is an example for using group actions.

```
julia> X = [[1, 2, 3, 4], [2,1,3,4], [1,2,4,3], [2,1,4,3]]
julia> permutation(x) = [x[2], x[1], x[3], x[4]]
julia> m = multiplicities(X, group_action = permutation)
[[1,2], [3,4]]
```

`HomotopyContinuation.unique_points`

— Function`unique_points(vectors; metric = EuclideanNorm(), atol = 1e-14, rtol = 1e-8, kwargs...)`

Returns all elements in `vector`

for which two elements have `distance`

at most `max(atol, rtol * metric(0,w[i]))`

. Note that the output can depend on the order of elements in vectors. The remaining `kwargs`

are things that can be passed to `UniquePoints`

.

## Debugging

`HomotopyContinuation.path_info`

— Function`path_info(tracker::Tracker, x₀, t₁ = 1.0, t₀ = 0.0; debug::Bool = false, kwargs...)`

Track a path using the given `tracker`

and start value `x₀`

. This returns a struct containing detailed information about the tracked path.