Wire `gungraun` 0.18 as a dev-dependency and a `benches/` directory with instruction-count benchmarks for the algorithmic hot paths. Why gungraun and not criterion: heuropt's hot paths are deterministic numerical loops where wall-clock noise dominates real differences. gungraun runs each benchmark under valgrind/callgrind once and reports exact instruction counts — stable across machines and CI runners, detects sub-microsecond regressions cleanly. Benchmarks added: - pareto::non_dominated_sort (the inner loop of every Pareto MOEA) - pareto::crowding_distance (NSGA-II survival selection) - metrics::hypervolume_nd (HSO recursion, used by SMS-EMOA) - internal::cholesky (BO's per-step posterior factorization) - algorithms::nsga2 single generation (end-to-end smoke check) - algorithms::cma_es single generation (eigendecomposition cost) Tracked size only — these aren't part of the regular CI matrix because they need valgrind installed. Run with `cargo bench` locally. Wired via the standard `[[bench]]` Cargo entries with `harness = false` so gungraun's main_macro does the dispatch.
33 lines
862 B
TOML
33 lines
862 B
TOML
[package]
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name = "heuropt"
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version = "0.3.0"
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edition = "2024"
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rust-version = "1.85"
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authors = ["Stephen Waits <steve@waits.net>"]
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description = "A practical Rust toolkit for heuristic single-, multi-, and many-objective optimization."
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license = "MIT"
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readme = "README.md"
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repository = "https://github.com/swaits/heuropt"
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homepage = "https://github.com/swaits/heuropt"
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documentation = "https://docs.rs/heuropt"
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keywords = ["optimization", "evolutionary", "nsga", "pareto", "moead"]
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categories = ["algorithms", "science", "mathematics", "simulation"]
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[features]
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default = []
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serde = ["dep:serde"]
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parallel = ["dep:rayon"]
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[dependencies]
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rand = "0.9"
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rand_distr = "0.5"
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rayon = { version = "1", optional = true }
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serde = { version = "1", features = ["derive"], optional = true }
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[dev-dependencies]
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gungraun = "0.18"
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[[bench]]
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name = "hot_paths"
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harness = false
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