swaits
fa3f2e8fb0
Theme: documentation and project polish. No public-API changes; this is the v0.5 release that elevates heuropt's docs/onboarding/governance to bar-setting status. Adds: - mdbook user guide at docs/book/ with intro, getting-started, defining-problems, choosing-an-algorithm, cookbook (7 recipes), comparison vs other libraries, stability/SemVer, migration guides. Deploys to https://swaits.github.io/heuropt/ via .github/workflows/ docs.yml. - Runnable rustdoc examples on every algorithm (35 of them), all exercised by cargo test --doc. - Three real-world examples: portfolio.rs (multi-obj with budget constraint), hyperparam_tuning.rs (BO + TPE), scheduling.rs (permutation via SA + SwapMutation against Smith's-rule oracle). - Governance: CONTRIBUTING.md, SECURITY.md, CODE_OF_CONDUCT.md (adopting builderscode.org's Builder's Code of Conduct), GitHub issue templates, PR template. Polishes: - README hero with badges + user-guide link. - lib.rs crate-level docs. - CHANGELOG entry for 0.5.0. Bumps Cargo.toml to 0.5.0.
79 lines
2.5 KiB
Rust
79 lines
2.5 KiB
Rust
//! `heuropt` — a practical Rust toolkit for heuristic single-,
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//! multi-, and many-objective optimization.
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//!
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//! The crate aims to make three things obvious:
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//!
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//! 1. **Define a problem** by implementing [`Problem`](crate::core::Problem).
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//! 2. **Run a built-in optimizer** — pick from 35 algorithms in
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//! [`algorithms`] covering single-objective continuous (CMA-ES,
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//! Differential Evolution, Nelder-Mead, …), multi-objective
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//! (NSGA-II, MOPSO, IBEA, MOEA/D, …), many-objective (NSGA-III,
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//! GrEA, RVEA, …), and sample-efficient regimes (Bayesian
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//! Optimization, TPE, Hyperband).
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//! 3. **Or implement your own** by implementing
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//! [`Optimizer`](crate::traits::Optimizer). The trait is one
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//! method long.
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//!
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//! ## Where to read more
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//!
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//! - **User guide / cookbook / comparison vs pymoo & friends:**
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//! <https://swaits.github.io/heuropt/>.
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//! - **Algorithm selection:** the README's decision tree, or the
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//! "Choosing an algorithm" book chapter.
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//! - **Design rationale:** `docs/heuropt_tech_design_spec.md` in the
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//! repository.
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//!
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//! ## Optional features
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//!
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//! - `serde` — derives `Serialize` / `Deserialize` on the core data
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//! types ([`Candidate`](crate::core::Candidate),
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//! [`Population`](crate::core::Population),
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//! [`Evaluation`](crate::core::Evaluation), …).
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//! - `parallel` — rayon-backed parallel population evaluation in
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//! every population-based algorithm. Seeded runs stay bit-
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//! identical to serial mode.
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//!
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//! # Quick example
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//!
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//! ```
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//! use heuropt::prelude::*;
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//!
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//! struct Toy;
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//!
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//! impl Problem for Toy {
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//! type Decision = Vec<f64>;
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//!
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//! fn objectives(&self) -> ObjectiveSpace {
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//! ObjectiveSpace::new(vec![
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//! Objective::minimize("f1"),
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//! Objective::minimize("f2"),
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//! ])
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//! }
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//!
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//! fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
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//! Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
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//! }
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//! }
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//!
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//! let initializer = RealBounds::new(vec![(-5.0, 5.0)]);
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//! let variation = GaussianMutation { sigma: 0.2 };
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//! let config = Nsga2Config { population_size: 30, generations: 10, seed: 42 };
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//! let mut opt = Nsga2::new(config, initializer, variation);
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//! let result = opt.run(&Toy);
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//! assert_eq!(result.population.len(), 30);
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//! assert!(!result.pareto_front.is_empty());
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//! ```
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pub mod algorithms;
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pub mod core;
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pub(crate) mod internal;
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pub mod metrics;
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pub mod operators;
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pub mod pareto;
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pub mod prelude;
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pub mod selection;
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pub mod traits;
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#[cfg(test)]
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pub(crate) mod tests_support;
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