Hyper-dual numbers can be used to compute first and second derivatives numerically without the cancellation errors of finite-differencing schemes. This Julia implementation is directly based on the C++ implementation by Jeffrey Fike and Juan J Alonso, both of Stanford University, department of Aeronautics and Astronautics and is described in the paper:

The Development of Hyper-Dual Numbers for Exact Second Derivative Calculations

The Julia version was derived/written by Rob J Goedman (goedman@icloud.com). Latest version tagged as v1.0.0 (7/12/2016)

The Julia package is structured similar to the DualNumbers package, which aims for complete support for `HyperDual`

types for numerical functions within Julia's `Base`

. Currently, basic mathematical operations and trigonometric functions are supported.

The following functions are specific to hyperdual numbers:

`Hyper`

,`Hyper256`

,2`Hyper128`

,`hyper`

,`hyper256`

,`hyper128`

,`eps1`

,`eps2`

,`eps1eps2`

,`ishyper`

,`hyper_show`

Many other Julia packages related to Automatic Differentiation are available, e.g. PowerSeries.jl, TaylorSeries.jl and DualNumbers.jl. More are under development.

The example below demonstrates basic usage of hyperdual numbers by employing them to
perform automatic differentiation. The code for this example can be found in
`test/runtests.jl`

.

First install the package by using the Julia package manager:

```
Pkg.add("HyperDualNumbers")
```

Then make the package available via

```
using HyperDualNumbers
```

Use the `hyper()`

function to define a hyperdual number, e.g.:

```
hd0 = hyper()
hd1 = hyper(1.0)
hd2 = hyper(3.0, 1.0, 1.0, 0.0)
hd3 = hyper(3//2, 1//1, 1//1,0//1)
```

Let's say we want to calculate the first and second derivative of

```
f(x) = e^x / (sqrt(sin(x)^3 + cos(x)^3))
```

To calculate these derivatives at a location `x`

, evaluate your function at `hyper(x, 1.0, 1.0, 0.0)`

. For example:

```
t0 = hyper(1.5, 1.0, 1.0, 0.0)
y = f(t0)
```

For this example, you'll get the result

```
4.497780053946162 + 4.053427893898621ϵ1 + 4.053427893898621ϵ2 + 9.463073681596601ϵ1ϵ2
```

The first term is the function value, the coefficients of both `ϵ1`

and `ϵ2`

(which correspond to the second and third arguments of `hyper`

) are equal to the first derivative, and the coefficient of `ϵ1ϵ2`

is the second derivative.

You can extract these coefficients from the hyperdual number using the functions `real()`

, `eps1()`

or `eps2()`

and `eps1eps2()`

:

```
println("f(1.5) = ", f(1.5))
println("f(t0) = ", real(f(t0)))
println("f'(t0) = ", eps1(f(t0)))
println("f'(t0) = ", eps2(f(t0)))
println("f''(t0) = ", eps1eps2(f(t0)))
```

Wish list:

- A generic hyper-number package to support multiple types (Complex, Double & Dual) and variable order (e.g. order 1 is dual, order 2 is as in this package, etc.)
- Graphs (as in Jeffrey Fike's paper)
- Profiling
- Compare with Standard Library
- Cross-validate with other relevant Julia packages
- Make it a proper package, e.g. dependencies. etc.

03/27/2014

about 1 month ago

121 commits