Announcing Basin: A Numerical Optimization Library for Rust

[u/johlars]

Hi!

I've been working on Basin, a numerical optimization library for Rust. It's heavily inspired by argmin; same overall shape (Executor driver loop, Solver/Problem trait split, per-solver State, pluggable termination). Basin exists because I wanted to push on a few specific design directions that were awkward to retrofit.

What's Different

• Framework-level termination, bound to state shape. max_iter, the *_tolerance family, max_time, eval budgets all live on the Executor and compose across solvers. Each criterion binds on the minimum state it needs, so asking for a GradientTolerance on a derivative-free solver is a compile error, not a runtime surprise.
• First-class constraints. Constraints describe the problem, so they live problem-side (not on the executor, never on state). A constrained problem handed to an unconstrained solver is a compile error; there are opt-in adapters (projection/log-barrier/augmented Lagrangian) to wrap unconstrained solvers. Box bounds and linear (in)equalities today; nonlinear is on the roadmap.
• Backend-generic linear algebra. Solvers are generic over Vec<f64> (no features), nalgebra, ndarray, and faer. A small universal vector tier keeps first-order/derivative-free solvers backend-generic; a richer linalg tier holds matrix ops that LA-heavy solvers bound on by the minimum subset they need.
• WASM in the default build. No BLAS/LAPACK, no threads, no std::time::Instant in default paths. CI enforces wasm32-unknown-unknown. Parallelism and BLAS-backed paths are opt-in features.

Current Solvers Supported

• First-order/quasi-Newton: gradient descent (with momentum + pluggable line searches), BFGS, L-BFGS, L-BFGS-B
• Derivative-free: Nelder--Mead, Brent
• Nonlinear least squares: Gauss--Newton, Levenberg--Marquardt, trust-region-reflective
• Global/stochastic: random search, CMA-ES, steady-state GA, memetic combinations
• Constrained: projected gradient, bounded Nelder--Mead/L-BFGS-B/CMA-ES, log-barrier, augmented Lagrangian

Example

use basin::{BasicState, CostFunction, Executor, Gradient, GradientDescent, GradientTolerance};

struct Rosenbrock;

impl CostFunction for Rosenbrock {
    type Param = Vec<f64>;
    type Output = f64;
    type Error = std::convert::Infallible;

    fn cost(&self, x: &Vec<f64>) -> Result<f64, std::convert::Infallible> {
        Ok((1.0 - x[0]).powi(2) + 100.0 * (x[1] - x[0].powi(2)).powi(2))
    }
}

impl Gradient for Rosenbrock {
    type Gradient = Vec<f64>;

    fn gradient(&self, x: &Vec<f64>) -> Result<Vec<f64>, std::convert::Infallible> {
        Ok(vec![
            -2.0 * (1.0 - x[0]) - 400.0 * x[0] * (x[1] - x[0].powi(2)),
            200.0 * (x[1] - x[0].powi(2)),
        ])
    }
}

let result = Executor::new(Rosenbrock, GradientDescent::new(1e-3), BasicState::new(vec![-1.2, 1.0]))
    .max_iter(50_000)
    .terminate_on(GradientTolerance(1e-6))
    .run()
    .unwrap();

Links

• Crate: https://crates.io/crates/basin (cargo add basin)
• Docs: https://basin.bz/docs/, API: https://docs.rs/basin
• In-browser solver visualizer: https://basin.bz/visualizer/
• Benchmarks vs other crates and across backends: https://basin.bz/benchmarks/
• Source: https://github.com/jolars/basin (MIT)

Feedback is very welcome, especially on the trait surface, naming, and any solver/backend combinations you'd want that aren't there yet.