Amando

`autodiff`: re-structure to avoid lifetimes.

8

The `Variable` struct has a reference to the `Graph` struct. This is causing a lot of issues, namely it prevents: - Adding support for `ndarray` and `nalgebra`. - Python bindings...

`Interpolator`s to-do list: - [x] Linear - [x] Exponential - [ ] B-Splines - [ ] Polynomial

`autodiff`: improve the Graphviz visualisations.

4

There are problems with the Graphviz plotting of the computation graphs in `autodiff`: - [ ] The nodes don't indicate the operation that took place (e.g. `+`, `sin`). - [...

Need to write doc-tests for the remaining distributions: - [x] Bernoulli - [x] Binomial - [x] Chi-Squared - [x] Exponential

Implement `num_traits::identities::{One, Zero}` for `Variable`

14

I need to find a reasonable way to implement the `num_traits::identities::{One, Zero}` traits for the `Variable` type in `RustQuant::autodiff`. The traits are [here](https://docs.rs/num-traits/0.2.15/num_traits/identities/index.html)

`autodiff`: add support for Jacobian and Hessian matrices.

4

Currently only gradients can be computed via the `RustQuant::autodiff` module. Adding support for full Jacobians and also higher-order derivatives like the Hessian would be nice.

Types of curves: - [ ] Yield curve. - [x] Zero/spot curve. - [x] Discount curve. - [x] Flat curve. - [x] Forward curve. - [ ] Inflation curve.