basin 0.12.0

//! basin — a numerical optimization library.
//!
//! The framework lives in [`core`]: problem traits the user implements
//! ([`CostFunction`], [`Gradient`], [`BoxConstraints`],
//! [`LinearInequalityConstraints`], [`LinearEqualityConstraints`]), state shapes
//! solvers iterate over ([`State`], [`GradientState`], [`SimplexState`]),
//! the [`Solver`] trait, a pluggable termination layer
//! ([`TerminationCriterion`]), and a read-only observer layer
//! ([`Observe`]). Concrete solvers are in [`solver`]; line searches in
//! [`line_search`].
//!
//! Start at [`Executor`] for the user-facing driver, or [`core`] for the
//! trait taxonomy and the iteration-loop contract.
//!
//! See `CONTRIBUTING.md` at the repo root for the design tenets that shape
//! these APIs (notably tenet 3 on framework-level termination, tenet 4
//! on first-class constraints, and tenet 5 on backend tiering).
//!
//! # Example
//!
//! Implement [`CostFunction`] (and [`Gradient`] when the solver needs
//! derivatives), then hand the problem, a solver, and an initial state to
//! the [`Executor`]:
//!
//! ```
//! use basin::{BasicState, CostFunction, Executor, Gradient, GradientDescent, GradientTolerance};
//!
//! struct Sphere;
//! impl CostFunction for Sphere {
//!     type Param = Vec<f64>;
//!     type Output = f64;
//!     type Error = std::convert::Infallible;
//!     fn cost(&self, x: &Vec<f64>) -> Result<f64, std::convert::Infallible> {
//!         Ok(x.iter().map(|xi| xi * xi).sum())
//!     }
//! }
//! impl Gradient for Sphere {
//!     type Gradient = Vec<f64>;
//!     fn gradient(&self, x: &Vec<f64>) -> Result<Vec<f64>, std::convert::Infallible> {
//!         Ok(x.iter().map(|xi| 2.0 * xi).collect())
//!     }
//! }
//!
//! let result = Executor::new(Sphere, GradientDescent::new(0.1), BasicState::new(vec![1.0, 1.0]))
//!     .max_iter(1_000)
//!     .terminate_on(GradientTolerance(1e-8))
//!     .run()
//!     .unwrap();
//! assert!(result.cost() < 1e-12);
//! ```
//!
//! # Error model
//!
//! basin distinguishes *three* outcomes a run can produce. The split is a
//! stable part of the public contract — downstream code can rely on it:
//!
//! - **Soft reject** — return `Ok(f64::INFINITY)` from [`CostFunction::cost`]
//!   to reject a *single point* without stopping the solve. Line searches treat
//!   `+∞` as worse and retreat; population solvers treat it as worst fitness.
//!   This is the channel for "this `x` is outside my domain, but the solve
//!   should continue."
//! - **Clean stop** — the run ends *normally* with a
//!   [`TerminationReason`], either
//!   because a [`TerminationCriterion`] fired or because the [`Solver`] reported
//!   a mid-iteration stop. [`Executor::run`] returns
//!   `Ok(`[`OptimizationResult`]`)` carrying that reason. This is **not** an
//!   error.
//! - **Hard abort** — return `Err(_)` from a problem-trait method to terminate
//!   the *entire* solve. The error is your own type and bubbles out of
//!   [`Executor::run`] untouched, typed as `Result<_, P::Error>`. Use it when
//!   the failure is not about a particular `x` — a downstream service vanished,
//!   the user pressed cancel, an early-stop condition in your own problem state
//!   fired.
//!
//! ## One error type, threaded through
//!
//! The hard-abort error is chosen *once*, on the problem
//! ([`CostFunction::Error`], or
//! [`Residual::Error`] for nonlinear
//! least squares). Every downstream trait mirrors it: [`Solver::Error`] and
//! [`LineSearch::Error`] are set to `P::Error`, so a custom problem error flows
//! through the solver and line search out to the caller with no conversion glue.
//! Problems that cannot fail pick [`std::convert::Infallible`]; its niche
//! optimization keeps `Result<f64, Infallible>` the same layout as a bare `f64`,
//! so the happy path stays zero-cost.
//!
//! The [`problem`](crate::core::problem) module docs carry the per-trait detail.
#![cfg_attr(docsrs, feature(doc_cfg), doc(auto_cfg))]
#![deny(missing_docs)]
#![deny(rustdoc::broken_intra_doc_links)]

pub mod core;
pub mod line_search;
/// Catalogue of test problems used by the example tests and benchmarks.
#[cfg(feature = "problems")]
pub mod problems;
/// Concrete solver implementations.
pub mod solver;

pub use crate::core::augmented_lagrangian::AugmentedLagrangian;
pub use crate::core::barrier::LogBarrier;
pub use crate::core::constraint::{
    BoxConstraints, LinearEqualityConstraints, LinearInequalityConstraints,
};
pub use crate::core::executor::{Executor, OptimizationResult, StepOutcome, Stepper, run_loop};
pub use crate::core::inner::{InnerExecutor, WarmStart};
pub use crate::core::math::{
    ClampInPlace, ComponentMulAssign, DenseMatrix, DenseMatrixFromFn, Dot, GramMatrix,
    LinearSolveError, LinearSolveLstsq, LinearSolveSpd, MatTransposeVec, MatVec,
    MatrixFromDiagonal, MatrixIdentity, NegInPlace, NormInfinity, NormSquared,
    SampleStandardNormal, SampleUniformBox, Scalar, ScaleInPlace, ScaledAdd, SymmetricEigen,
    SymmetricEigenError, VectorIndex, VectorLen,
};
pub use crate::core::numdiff::{
    FiniteDiff, Method, central_difference_gradient, central_difference_hessian,
    central_difference_jacobian, forward_difference_gradient, forward_difference_hessian,
    forward_difference_jacobian,
};
pub use crate::core::observer::{Observe, ObserverMode};
pub use crate::core::problem::{
    CostFunction, EvalCounts, Gradient, Hessian, Jacobian, MiniBatchGradient, Problem, Residual,
};
pub use crate::core::solver::Solver;
#[cfg(feature = "faer")]
pub use crate::core::state::FaerQuasiNewtonState;
#[cfg(feature = "nalgebra")]
pub use crate::core::state::NalgebraQuasiNewtonState;
pub use crate::core::state::{
    BasicPopulationState, BasicSimplexState, BasicState, CmaEsState, CountsMirror, GradientState,
    IntoInitialSimplex, LbfgsState, NllsState, PopulationState, ScalarState, SimplexState, State,
};
pub use crate::core::state::{DenseQuasiNewtonState, QuasiNewtonState};
pub use crate::core::termination::{
    CmaEsTolerance, CostTolerance, GradientTolerance, MaxCostEvals, MaxGradientEvals, MaxIter,
    MaxTime, NoImprovement, ParamTolerance, ProjectedGradientTolerance, RelativeCostTolerance,
    RelativeGradientTolerance, RelativeParamTolerance, SimplexTolerance, TargetCost,
    TerminationCriterion, TerminationReason,
};
pub use crate::line_search::{Backtracking, Constant, LineSearch, MoreThuente, Wolfe};
#[allow(deprecated)]
pub use crate::solver::BFGS;
pub use crate::solver::Bfgs;
#[allow(deprecated)]
pub use crate::solver::lbfgs::{LBFGS, LBFGSB};
pub use crate::solver::lbfgs::{Lbfgs, Lbfgsb};
pub use crate::solver::{
    AugmentedLagrangianMethod, BarrierMethod, BoundedCmaEs, BoundedCmaInject, Brent, ClosureInner,
    CmaEs, CmaInject, De, DeInject, GaussNewton, GoldenSection, GradientDescent,
    LevenbergMarquardt, MaLsChCma, MaLsChState, MemeticInner, NelderMead, ProjectedGradientDescent,
    RandomSearch, Sgd, Ssga, Trf,
};