Add more Optimization / Minimization algorithms

**Is your feature request related to a problem? Please describe.**
Currently, there is no way to find the argument / function value that minimizes a target function. Such operations are often useful.

**Describe the solution you'd like**
I suggest implementing a relatively small set of algorithms that can handle a wide range of target functions. Specifically, I suggest implementing
* The [Nelder-Mead Method](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method), as it does not require any special properties of the target function or any derivative-information. Also, it is relatively simple.
* A very general solver for equality and inequality constrained problems. In particular, I suggest using [Sequential Quadratic Programming](https://en.wikipedia.org/wiki/Sequential_quadratic_programming).

This Sequential Quadratic Approach is also used in the default method chosen by the [scipy.optimize.minimize function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html).
The scipy package is however relying on a Fortran implementation, and I'm not sure if F# native performance will be enough.
There is a big suite of [Test functions for optimization](https://en.wikipedia.org/wiki/Test_functions_for_optimization), and benchmarking could be done with a handful of these.

**Describe alternatives you've considered**
Use MathNET.Numerics instead, and write some glue code.
Also, it could be discussed if a different set of algorithms would make more sense. I'm not sure about the performance tradeoffs of using SQP as the de-facto standard for everything, that cannot be managed with Nelder-Mead.

**Additional context**
Add any other context or screenshots about the feature request here.