# Basin <picture><source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/jolars/basin/main/images/logo-dark.png" /><img src="https://raw.githubusercontent.com/jolars/basin/main/images/logo.png" align="right" width="189" alt="basin logo" /></picture>
[](https://github.com/jolars/basin/actions/workflows/ci.yml)
[](https://crates.io/crates/basin)
[](https://docs.rs/basin)
A numerical optimization library for Rust, inspired by [argmin]. It pairs a
small generic core, problem traits you implement, a pluggable termination layer,
and a driver loop (`Executor`), with a growing set of solvers spanning
first-order, derivative-free, nonlinear least-squares, and evolutionary methods.
Solvers are generic over the linear-algebra backend, constraints are
first-class, and the default build compiles to `wasm32-unknown-unknown` with no
BLAS/LAPACK or threads.
Narrative documentation lives at [basin.rs/docs]; the rustdoc reference is at
[docs.rs/basin]. There is also an in-browser [solver visualizer] and a
[benchmarks site] comparing Basin against competing crates and across backends
and solvers.
## Install
```sh
cargo add basin
```
Basin works on plain `Vec<f64>` out of the box. Linear-algebra backends are
opt-in, one feature each:
```sh
cargo add basin --features nalgebra # or: ndarray, faer
```
Basin's minimum supported Rust version (MSRV) is **1.87.0**. It is held
deliberately conservative to keep the planned CRAN (R) bindings buildable, so it
moves rarely and only after checking downstream toolchains.
## Example
Implement `CostFunction` (and `Gradient`, when the solver needs derivatives),
then hand the problem, a solver, and an initial state to the `Executor`:
```rust
use basin::{BasicState, CostFunction, Executor, Gradient, GradientDescent, GradientTolerance};
use std::convert::Infallible;
struct Rosenbrock;
impl CostFunction for Rosenbrock {
type Param = Vec<f64>;
type Output = f64;
type Error = Infallible;
fn cost(&self, x: &Vec<f64>) -> Result<f64, Self::Error> {
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>, Self::Error> {
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();
println!("x = {:?}, f = {}, stopped: {:?}", result.param(), result.cost(), result.reason);
```
Termination criteria are framework-level: the same ones compose across solvers,
and they are bound to the state a solver actually exposes, so asking for a
gradient tolerance on a derivative-free solver is a compile error, not a runtime
surprise.
## Solvers
- **First-order, quasi-Newton, and Newton:** gradient descent (with momentum and
pluggable line searches), SGD, BFGS, L-BFGS, L-BFGS-B, and a Newton
trust-region method.
- **Derivative-free:** Nelder-Mead; Brent, Brent-with-derivatives, and
golden-section search (1D); Powell's model-based family (NEWUOA, BOBYQA,
LINCOA, COBYLA); and MADS (OrthoMADS).
- **Nonlinear least squares:** Gauss-Newton, Levenberg-Marquardt, trust-region
reflective.
- **Global and stochastic:** random search, CMA-ES, differential evolution, a
steady-state genetic algorithm, and memetic combinations (MA-LS-Chain, plus
CMA-ES and DE injection wrappers).
- **Constrained:** box bounds via projected gradient descent, bounded
Nelder-Mead, L-BFGS-B, and bounded CMA-ES; LINCOA for linear constraints and
COBYLA for nonlinear inequalities; log-barrier and augmented Lagrangian
wrappers for more general constraints.
See [Solvers] for which backends each one supports.
## Backends
Parameters and linear algebra are generic over the backend. `Vec<f64>` needs no
features; [nalgebra], [ndarray], and [faer] are enabled one feature each, each
pinning a single major version. First-order and derivative-free solvers run on
any backend; linear-algebra-heavy solvers may require a specific one and say so
in their docs.
Basin pins one major version per backend. Each basin 1.x release supports
exactly these versions:
| [nalgebra] | `nalgebra` | 0.34 (with `nalgebra-sparse` 0.11) |
| [ndarray] | `ndarray` | 0.17 |
| [faer] | `faer` | 0.24 |
`Vec<f64>` is built in and needs no features. A backend major-version bump is a
breaking change and ships only in a basin major release; within the 1.x series
these pins are fixed.
Two backends have opt-in, BLAS/LAPACK-backed acceleration. Both are off by
default and not wasm-compatible (each links a Fortran/BLAS toolchain), and both
expect you to bring your own BLAS/LAPACK source crate:
| `ndarray-blas` | Forwards `ndarray/blas` for BLAS-backed ndarray linear algebra. |
| `nalgebra-lapack` | Swaps the nalgebra backend's Cholesky and symmetric eigendecomposition for LAPACK-backed ones (pins `nalgebra-lapack` 0.27, the release tracking nalgebra 0.34). |
The default build is wasm-friendly: no BLAS/LAPACK and no threads. Parallelism
is behind the opt-in `parallel` feature; BLAS/LAPACK acceleration is behind
`ndarray-blas` and `nalgebra-lapack`.
## Acknowledgements
Basin owes a substantial intellectual debt to [argmin]: the overall shape of the
crate: the `Executor` driver loop, the `Solver`/`Problem` trait split, and
per-solver `State` are borrowed from it, and several solver implementations and
test-problem conventions were modeled on argmin's. Thanks to the argmin authors
and contributors for a library that is a pleasure to learn from.
The Powell-family derivative-free solvers (COBYLA, NEWUOA, BOBYQA, LINCOA) are
derived from [PRIMA], Zaikun Zhang's modern-Fortran reference implementation of M. J. D.
Powell's methods, used as the authoritative source for the exact formulas and as
the cross-validation oracle. PRIMA is distributed under the BSD 3-Clause
License; its notice is retained in
[COPYRIGHT](https://github.com/jolars/basin/blob/main/crates/basin/COPYRIGHT).
The bound-constrained L-BFGS-B solver is a port of the [L-BFGS-B] version 3.0
Fortran code by Ciyou Zhu, Richard H. Byrd, Peihuang Lu, and Jorge Nocedal (ACM
TOMS Algorithm 778), with the v3.0 improvements by José Luis Morales and Jorge
Nocedal. It is released under the New BSD (BSD 3-Clause) License; its notice is
likewise retained in
[COPYRIGHT](https://github.com/jolars/basin/blob/main/crates/basin/COPYRIGHT).
[PRIMA]: https://github.com/libprima/prima
[L-BFGS-B]: https://users.iems.northwestern.edu/~nocedal/lbfgsb.html
## License
Licensed under either of
- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or
<https://www.apache.org/licenses/LICENSE-2.0>)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or
<https://opensource.org/licenses/MIT>)
at your convenience.
### Contribution
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 above, without any additional terms or conditions.
[argmin]: https://github.com/argmin-rs/argmin
[nalgebra]: https://nalgebra.rs
[ndarray]: https://github.com/rust-ndarray/ndarray
[faer]: https://faer.veganb.tw
[basin.rs/docs]: https://basin.rs/docs/
[docs.rs/basin]: https://docs.rs/basin
[solver visualizer]: https://basin.rs/visualizer/
[benchmarks site]: https://basin.rs/benchmarks/
[Solvers]: https://basin.rs/docs/solvers/