This package is experimental. Expect frequent updates to the repository with breaking changes and infrequent releases.
Mathematical optimization and machine learning framework and algorithms.
Optimal provides a composable framework for mathematical optimization and machine learning from the optimization perspective, in addition to algorithm implementations.
The framework consists of runners,
optimizers,
and problems,
with a chain of dependency as follows:
runner -> optimizer -> problem.
Most optimizers can support many problems
and most runners can support many optimizers.
A problem defines a mathematical optimization problem. An optimizer defines the steps for solving a problem, usually as an infinite series of state transitions incrementally improving a solution. A runner defines the stopping criteria for an optimizer and may affect the optimization sequence in other ways.
Minimize the "count" problem using a derivative-free optimizer:
```rust use optimal::{prelude::*, BinaryDerivativeFreeConfig};
println!( "{:?}", BinaryDerivativeFreeConfig::startdefaultfor(16, |point| { point.iter().filter(|x| **x).count() as f64 }) .argmin() ); ```
Minimize the "sphere" problem using a derivative optimizer:
```rust use optimal::{prelude::*, RealDerivativeConfig};
println!( "{:?}", RealDerivativeConfig::startdefaultfor( 2, std::iter::repeat(-10.0..=10.0).take(2), |point| point.iter().map(|x| x.powi(2)).sum(), |point| point.iter().map(|x| 2.0 * x).collect(), ) .nth(100) .unwrap() .best_point() ); ```
For more control over configuration parameters, introspection of the optimization process, serialization, and specialization that may improve performance, see individual optimizer packages.
License: MIT