### Overdetermined systems

A system of polynomial equations $f=(f_1(x_1,\ldots, x_m),\ldots, f_n(x_1,\ldots,x_m))$ is called *overdetermined*, if it has more equations than variables; i.e., when $n>m$. HomotopyContinuation.jl features Newtons method for overdetermined systems for tracking solutions. We show in an example how it can be used.

### Example: the rational normal curve

The rational normal curve is a 1-dimensional algebraic variety within the 3-dimensional complex space: $C = \{(x,y,z) \in \mathbb{C}^3 \mid xz-y^2 = 0,\, y-z^2=0, \, x-yz = 0\}.$ Since $C$ is cut out by 3 equations one might expect it to be 0-dimensional, but it is not. In fact, the rational normal curve is a first example of a non-complete intersection.

### Intersecting the rational normal curve with a linear space

Let $L$ be a random linear space of dimension 2. Then, almost surely, the intersection $C\cap L$ is finite. The goal of this example is to compute one of the points in $C\cap L$. First, let us generate the equations of $C$ and $L$.```
using HomotopyContinuation, LinearAlgebra
@polyvar x y z
f = [x*z-y^2, y-z^2, x-y*z]
ℓ = randn(ComplexF64, 4) ⋅ [x, y, z, 1];
```

One is tempted to compute $C\cap L$ by executing `solve([f;ℓ])`

and simply compute *all* solutions. But this command will return an error message.

```
solve([f;ℓ])
```

```
ERROR: The input system is overdetermined. Therefore it is necessary to provide an explicit startsystem.
See
https://www.JuliaHomotopyContinuation.org/guides/latest/overdetermined_tracking/
for details.
```

The problem here is that for overdetermined system we can’t construct generic starting systems. The reason is simple: a generic overdetermined system has no solutions at all. Nevertheless, if we have a starting system and some of its solutions at hand, we can track them towards $[f, \ell]$.

For instance, the point $p=(1, 1, 1)$ lies both on $C$ and $\{(x,y,z)\in \mathbb{C}^3 \mid x-y+z -1= 0\}$. We can track this solution towards $[f, \ell]$ by executing the following:

```
ℓ₁ = [1, -1, 1, -1] ⋅ [x, y, z, 1];
p = [1, 1, 1, 1];
solve([f; ℓ₁], [f; ℓ], [p])
```

```
AffineResult with 1 tracked paths
==================================
• 1 non-singular finite solution (0 real)
• 0 singular finite solutions (0 real)
• 0 solutions at infinity
• 0 failed paths
• random seed: 448284
```

The syntax `[p]`

is because solve takes as input an *array* of solutions.