cmaes 0.1.1

An implementation of the CMA-ES optimization algorithm.
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# cmaes

A Rust implementation of the CMA-ES optimization algorithm. It is used to minimize the value of an objective function and performs well on high-dimension, non-linear, non-convex, ill-conditioned, and/or noisy problems. See [this paper][0] for details on the algorithm itself. This library is based on the linked paper and the [pycma][1] implementation.

## Dependencies

`cmaes` uses some external libraries, so the following dependencies are required:

- CMake
- A C compiler
- A Fortran compiler (GCC's gfortran works)
- Rust (tested with rustc 1.57, earlier versions may work)

Dependencies may differ depending on the selected LAPACK implementation. Building is currently only supported on Linux (see [issue #4][4]).

## Quick Start

Add this to your Cargo.toml:

```
[dependencies]
cmaes = "0.1.0"
```

The LAPACK implementation used may be selected through Cargo features (see `Cargo.toml`). `netlib` is built from source by default.

Then, to optimize a function:
```rust
use cmaes::{CMAESOptions, DVector, PlotOptions};

let sphere = |x: &DVector<f64>| x.iter().map(|xi| xi.powi(2)).sum();

let dim = 10;
let mut cmaes_state = CMAESOptions::new(dim)
    .initial_mean(vec![1.0; dim])
    .enable_printing(200)
    .enable_plot(PlotOptions::new(0, false))
    .build(sphere)
    .unwrap();

let max_generations = 20000;
let result = cmaes_state.run(max_generations);

cmaes_state.get_plot().unwrap().save_to_file("plot.png", true).unwrap();
```

The produced plot will look like this:

<a href="https://github.com/pengowen123/cmaes/tree/master/images/plot_sphere.png">
    <img src="https://pengowen123.github.io/cmaes/images/plot_sphere.png"
        width=750 height=750 />
</a>

For more information, see the [documentation][2] and [examples][3].

## Contributing

Contributions are welcome! You can contribute by reporting any bugs or issues you have with the library, adding documentation, or opening pull requests.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as below, without any additional terms or conditions.

## License

Licensed under either of

    Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
    MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

[0]: https://arxiv.org/pdf/1604.00772.pdf
[1]: https://github.com/CMA-ES/pycma
[2]: https://docs.rs/cmaes/latest/cmaes
[3]: https://github.com/pengowen123/cmaes/tree/master/examples
[4]: https://github.com/pengowen123/cmaes/issues/4