Polynomial systems arising in application very often have a coefficients which are determined by some parameters.

We now want to show you how you can setup an efficient way to solve this polynomial system for many parameter values. Assume we have the following polynomial system:

```
using HomotopyContinuation, DynamicPolynomials
@polyvar x y z p[1:3]
F = [
x + 3 + 2y + 2y^2 - p[1],
(x - 2 + 5y)*z + 4 - p[2] * z,
(x + 2 + 4y)*z + 5 - p[3] * z
]
```

Since we need to solve `F`

many times for different values of `p`

it makes sense to first compute all
solutions for generic parameters and then to use a parameter homotopy.

```
# Generate generic parameters by sampling complex numbers from the normal distribution
p₀ = randn(ComplexF64, 3)
# Substitute p₀ for p
F_p₀ = subs(F, p => p₀)
# Compute all solutions for F_p₀
result_p₀ = solve(F_p₀)
```

```
Result with 2 solutions
==================================
• 2 non-singular solutions (0 real)
• 0 singular solutions (0 real)
• 8 paths tracked
• random seed: 652079
```

We see that our polynomial system $F$ has at most **2** isolated solutions (instead of the 8 possible ones).
The idea is to use these two generic solutions to find the solutions for the specific parameters we are interested in.
For this we are using a parameter homotopy from the generic parameters $p_0$ to the specific parameters.

In order to avoid to setup the necessary data structures over and over again we construct a `PathTracker`

once
and reuse it to track all paths.

```
# Let's store the generic solutions
S_p₀ = solutions(result_p₀)
# Construct the PathTracker
tracker = pathtracker(F; parameters=p, generic_parameters=p₀)
```

Since we do not have any real world data for our toy example, let’s generate some random data.

```
# generate some random data to simulate the parameters
data = [randn(3) for _ in 1:10_000]
```

Now we have everything to solve the equations for all parameters.

```
# Map over each parameter of our data
# and compute the corresponding solutions
data_solutions = map(data) do p
# We want to store all solutions. Create an empty array.
S_p = similar(S_p₀, 0)
for s in S_p₀
result = track(tracker, s; target_parameters=p)
# check that the tracking was successfull
if issuccess(result)
# only store the solutions
push!(S_p, solution(result))
end
end
end
```

In real applications you probably don’t want to store all solution but instead you have a measure of what the *best* solution is.

Also note that `track`

will assemble for each tracked path a `PathResult`

with additional informations. If you want to avoid to minimize this overhead you can add the keyword argument `details = :minimal`

, i.e.,

```
result = track(tracker, s; target_parameters=p, details=:minimal)
```