specular-differentiation 1.0.7
Specular differentiation in normed vector spaces and its applications
License: MIT License
Requires Python: >=3.11

# Specular Differentiation

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The Python package `specular` implements *specular differentiation* which generalizes classical differentiation.
This implementation strictly follows the definitions, notations, and results in [[1]](#references) and [[2]](#references).

A specular derivative (the red line) can be understood as the average of the inclination angles of the right and left derivatives. 
In contrast, a symmetric derivative (the purple line) is the average of the right and left derivatives.
Their difference is illustrated as in the following figure.

![specular-derivative-animation](https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://github.com/kyjung2357/specular-differentiation/blob/main/docs/figures/specular-derivative-animation.gif)

## Table of Contents
* [Introduction](#installation)
* [Applications](#applications)
* [Documentation](#documentation)
* [LaTeX macro](#latex-macro)
* [Citing specular-differentiation](#citing-specular-differentiation)
* [References](#references)

## Installation

### Requirements

`specular-differentiation` requires:

* **Python** >= 3.11
* `ipython` >= 8.12.3
* `matplotlib` >= 3.10.8
* `numpy` >= 2.4.0
* `pandas` >= 2.3.3
* `tqdm` >= 4.67.1

### User installation

**Standard Installation (NumPy backend)**

```bash
pip install specular-differentiation
```

**Advanced Installation (JAX backend)**

```bash
pip install "specular-differentiation[jax]"
```

See [the documentation](https://github.com/kyjung2357/specular-differentiation/blob/main/docs/api/jax.md) for advanced installation (JAX backend, Pytest).

### Quick start

The following simple example calculates the specular derivative of the [ReLU function](https://en.wikipedia.org/wiki/Rectified_linear_unit) $f(x) = max(0, x)$ at the origin.

```python
import specular

ReLU = lambda x: max(x, 0)
print(specular.derivative(ReLU, x=0))
```

```text
0.41421356237309515
```

## Applications

Specular differentiation is defined in normed vector spaces, allowing for applications in higher-dimensional Euclidean spaces. 
The `specular` package includes the following applications.

### [Ordinary differential equation](https://github.com/kyjung2357/specular-differentiation/blob/main/docs/api/ode.md)

* **Directory**: `examples/ode/`
* **References**: [[1]](#references), [[3]](#references), [[4]](#references)

In [[1]](#references), seven schemes are proposed for solving ODEs numerically:

* the *specular Euler* scheme of Type 1~6
* the *specular trigonometric* scheme

The following example shows that the specular Euler schemes of Type 5 and 6 yield more accurate numerical solutions than classical schemes: the explicit and implicit Euler schemes and the Crank-Nicolson scheme.

![ODE-example](https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://github.com/kyjung2357/specular-differentiation/blob/main/docs/figures/ODE-example.png)

### [Optimization](https://github.com/kyjung2357/specular-differentiation/blob/main/docs/api/optimization.md)

* **Directory**: `examples/optimization/`
* **References**: [[2]](#references), [[5]](#references)

In [[2]](#references), three methods are proposed for optimizing nonsmooth convex objective functions:

* the *specular gradient (SPEG)* method
* the *stochastic specular gradient (S-SPEG)* method
* the *hybrid specular gradient (H-SPEG)* method

The following example compares the three proposed methods with the classical methods: [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) (GD), [Adaptive Moment Estimation](https://arxiv.org/abs/1412.6980) (Adam), and [Broyden-Fletcher-Goldfarb-Shanno](https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm) (BFGS).

![optimization-example](https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://raw.githubusercontent.com/kyjung2357/specular-differentiation/main/https://github.com/kyjung2357/specular-differentiation/blob/main/docs/figures/optimization-example.png)

## [Documentation](https://kyjung2357.github.io/specular-differentiation/)

### [Getting Started](https://kyjung2357.github.io/specular-differentiation/started/)
### [API Reference](https://kyjung2357.github.io/specular-differentiation/api/)
### [Examples](https://kyjung2357.github.io/specular-differentiation/examples/)

## LaTeX macro

To use the specular differentiation symbol in your LaTeX document, add the following code to your preamble (before `\begin{document}`):

```latex
% Required packages
\usepackage{graphicx}
\usepackage{bm}

% Definition of Specular Differentiation symbol
\newcommand\sd[1][.5]{\mathbin{\vcenter{\hbox{\scalebox{#1}{\,$\bm{\wedge}$}}}}}
```

### Usage examples 

Use the symbol in your document (after `\begin{document}`):

```latex
% A specular derivative in the one-dimensional Euclidean space
$f^{\sd}(x)$

% A specular directional derivative in normed vector spaces
$\partial^{\sd}_v f(x)$
```

## Citing specular-differentiation

To cite this repository:

```bibtex
@software{Jung_specular-differentiation_2026,
  author = {Jung, Kiyuob},
  doi = {10.5281/zenodo.18246734},
  license = {MIT},
  month = jan,
  title = {{specular-differentiation}},
  url = {https://github.com/kyjung2357/specular-differentiation},
  version = {1.0.0},
  year = {2026},
}
```

## References

[1] K. Jung. [*Nonlinear numerical schemes using specular differentiation for initial value problems of first-order ordinary differential equations*](https://arxiv.org/abs/2601.09900). arXiv preprint arXiv:2601.09900, 2026.

[2] K. Jung. [*Specular differentiation in normed vector spaces and its applications to nonsmooth convex optimization*](https://arxiv.org/abs/2601.10950). arXiv preprint arXiv:2601.10950, 2026. 

[3] K. Jung and J. Oh. [*The specular derivative*](https://arxiv.org/abs/2210.06062). arXiv preprint arXiv:2210.06062, 2022.

[4] K. Jung and J. Oh. [*The wave equation with specular derivatives*](https://arxiv.org/abs/2210.06933). arXiv preprint arXiv:2210.06933, 2022.

[5] K. Jung and J. Oh. [*Nonsmooth convex optimization using the specular gradient method with root-linear convergence*](https://arxiv.org/abs/2412.20747). arXiv preprint arXiv:2210.06933, 2024.