docs: 0.8.0 release polish — README, guide, changelog
Companion to the feat(async) commit. Brings every cross-referencing doc up to v0.8 currency, replaces marketing-flavored copy with plain prose, and replaces toy benchmark problems with relatable ones that include actual run output and interpretive narrative. - README: collapses the four-bullet "Read the user guide / API reference / Tested with N tests / Hot paths optimized" list into a single Docs links line. - README: replaces the Schaffer-N1 toy problem with a PickACar multi-objective design problem — three decision variables (displacement, weight, drag), four objectives (price, 0-60, fuel, noise), and *nonlinear* cost relationships so the Pareto front is a real surface, not a 1D sweep. Includes actual NSGA-III run output (representative slice across the 100-car front) and a narrative explaining what each row tells you and why hand- picking would miss the interesting tradeoffs. - README: removes rustdoc-style hidden `#` setup lines from code blocks. The README is rendered as plain markdown on GitHub / crates.io, where those lines are visible garbage instead of hidden setup. Code blocks are now self-contained. - Guide quickstart (getting-started.md): replaces Sphere ( Σ x² ) with a least-squares LineFit example. Same shape (single- objective continuous), but recognizable framing. Includes actual CMA-ES output, residual table, and narrative comparing the answer to standard regression. - Algorithm count audit: stale "35 algorithms" claim corrected to the actual 33 across README, src/lib.rs, introduction.md, and the comparison.md table cell. - Async feature flag listed in the optional-features sections of README, src/lib.rs, getting-started.md. - introduction.md, choosing-an-algorithm.md, comparison.md, stability.md, migration.md, cookbook/parallel.md, cookbook/custom-optimizer.md: cross-references updated to describe full async coverage and link the new cookbook recipe. - stability.md: removes the speculative "Observer / Snapshot / Checkpoint planned" bullet (those didn't ship); documents the AsyncProblem / AsyncPartialProblem trait stability. - migration.md: new "To 0.8" section with paths from 0.5.x and 0.7.x. - CHANGELOG: 0.8.0 entry capturing the async feature plus the documentation / governance / CI catch-up. - SECURITY.md: supported versions table reflects 0.8.x.
This commit is contained in:
+63
-18
@@ -9,39 +9,84 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [0.8.0] — 2026-05-06
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Theme: async evaluation. heuropt now supports problems where each
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evaluation is a `.await`-able operation — HTTP services, RPC clients,
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spawned subprocesses. This is the differentiating capability vs.
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pymoo / hyperopt / MOEA Framework, none of which ship first-class
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async support.
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Theme: async evaluation, plus the docs / governance / CI catch-up
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that came with finalizing the release.
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heuropt now supports problems where each evaluation is a
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`.await`-able operation — HTTP services, RPC clients, spawned
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subprocesses. This is the differentiating capability vs.
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pymoo / hyperopt / optuna / DEAP / MOEA Framework, none of which
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ship first-class async support at the *evaluation* level.
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No public-API breaks for synchronous users. The new surface is
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gated behind a new `async` feature flag.
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> **Note on version numbers.** Versions 0.6.0 and 0.7.0 were
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> published on crates.io but contained experimental observability
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> APIs and metrics that were rolled back. Both are yanked. 0.8.0
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> picks up cleanly from 0.5.0 with just the async additions; if
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> you were on 0.5.x, upgrading to 0.8 is a feature-additive bump.
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### Added
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#### Async evaluation (the headline feature)
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- New optional feature `async`, gated on
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[`futures`](https://crates.io/crates/futures).
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- `core::async_problem::AsyncProblem` trait — mirrors `Problem` but
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with `async fn evaluate_async(&self, decision)`. Adapt an
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existing sync `Problem` with a one-line wrapper.
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- `core::async_problem::AsyncPartialProblem` trait — mirrors
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`PartialProblem` for multi-fidelity (Hyperband) workloads with
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`async fn evaluate_at_budget_async(decision, budget)`.
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- Per-algorithm `run_async(&problem, concurrency).await` methods on
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`RandomSearch` and `DifferentialEvolution` — drives evaluations
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through whichever async runtime the caller is using (typically
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tokio). `concurrency` bounds in-flight evaluations.
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**every** algorithm in the catalog — all 33 of them — driving
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evaluations through whichever async runtime the caller is using
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(typically tokio). `concurrency` bounds in-flight evaluations.
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Population-based algorithms (NSGA-II, NSGA-III, SPEA2, MOEA/D,
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CMA-ES, DE, GA, PSO, IBEA, SMS-EMOA, HypE, ε-MOEA, PESA-II,
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AGE-MOEA, KnEA, GrEA, RVEA, MOPSO, TLBO, IPOP-CMA-ES, sNES, UMDA,
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Ant Colony, GA, Random Search) fan out per generation. Steady-state
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algorithms (Hill Climber, SA, (1+1)-ES, PAES, Nelder-Mead, Tabu
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Search) await each step sequentially. Surrogate algorithms (BO,
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TPE) batch the initial design and then await per-iteration
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acquisitions. Hyperband fans out each Successive-Halving rung
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through `AsyncPartialProblem`.
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- Internal `algorithms::parallel_eval_async::evaluate_batch_async`
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helper — uses `futures::stream::FuturesOrdered` with concurrency-
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bounded chunks, preserves input order so seeded determinism is
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preserved when evaluations are themselves deterministic.
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and `evaluate_batch_at_budget_async` helpers — use
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`futures::stream::FuturesOrdered` with concurrency-bounded chunks,
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preserve input order so seeded determinism is preserved when
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evaluations are themselves deterministic.
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- `examples/async_eval.rs` — worked example with a simulated 20 ms
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remote service. At concurrency = 1 it's serial; at concurrency = 4
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it's 2× faster; demonstrates DifferentialEvolution under tokio.
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it's 2× faster; demonstrates `DifferentialEvolution` under tokio.
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#### Documentation
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- New cookbook recipe **[Async evaluation](docs/book/src/cookbook/async.md)**
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— implementing `AsyncProblem`, picking concurrency, determinism
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guarantees, async vs. `parallel`.
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- Comparison-with-other-libraries chapter updated: `heuropt 0.8`
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row, `Async ✅ AsyncProblem + run_async` column, "When to pick
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heuropt" gains an explicit IO-bound bullet.
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- Stability chapter rewritten: removes the speculative "Observer /
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Checkpoint planned" bullet (those didn't ship), documents the new
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`async` feature flag.
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- Migration guide: new "To 0.8" section covering both
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`0.5.x → 0.8` (feature-additive — opt in by enabling the `async`
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feature) and `0.7 → 0.8` (the partial async surface from 0.7 is
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superseded by complete coverage; existing `run_async` callers
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keep working).
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- Runnable `cargo test --doc` examples added to every public
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operator (10), metric (3), and Pareto utility (7) — every
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public item across the crate now ships with at least one
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example. 55 doctests in total (was 45).
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#### CI / build
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- `.github/workflows/docs.yml` builds the mdbook user guide on
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every push and deploys to GitHub Pages on `main` /
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tag pushes.
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- `mdbook` book now uses `[rust] edition = "2021"` to satisfy
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`mdbook 0.4.40`.
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- `clamp_to_bounds` cargo-fuzz target tolerance loosened to
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`1e-4 · max(simplex_total, max_abs_x, 1)` so the fuzzer doesn't
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flag ULP-level slop in the simplex projection's
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`max(x_i − τ, 0)` clamp boundary.
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[0.8.0]: https://github.com/swaits/heuropt/releases/tag/v0.8.0
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@@ -7,85 +7,184 @@
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[](https://github.com/swaits/heuropt/actions/workflows/ci.yml)
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**A practical Rust toolkit for heuristic optimization.** Single-objective.
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Multi-objective. Many-objective. 35 algorithms. One small set of traits.
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Bit-identical seeded determinism. No trait objects, no GATs, no generic-RNG
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plumbing in the public API.
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Multi-objective. Many-objective. 33 algorithms — every one of them with a
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sync `run` and an async `run_async`. One small set of traits. Bit-identical
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seeded determinism. No trait objects, no GATs, no generic-RNG plumbing in
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the public API.
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If you can write a `Problem` impl and read `RandomSearch`, you can write your
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own optimizer. That's the whole pitch.
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- 📖 **Read the [user guide](https://swaits.github.io/heuropt/)** for tutorials,
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cookbook recipes, comparison with pymoo / hyperopt / MOEA Framework, and
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stability policy.
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- 🔧 **[API reference on docs.rs](https://docs.rs/heuropt)** has runnable
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` ```rust ` examples on every algorithm.
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- 🧪 Tested with **316+ unit / integration / property tests** plus 8
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cargo-fuzz targets running on every PR.
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- ⚡ Hot paths heavily optimized — comparison harness 3.27× faster as of
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v0.4.0, all bit-identical to the reference output.
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Docs: [user guide](https://swaits.github.io/heuropt/) · [API reference](https://docs.rs/heuropt).
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## Installation
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```toml
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[dependencies]
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heuropt = "0.5"
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heuropt = "0.8"
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# Optional features:
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# - "serde": derive Serialize/Deserialize on the core data types.
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# - "parallel": evaluate populations across rayon's thread pool.
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# Seeded runs stay bit-identical to serial mode.
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# heuropt = { version = "0.5", features = ["serde", "parallel"] }
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# - "async": AsyncProblem / AsyncPartialProblem traits and a
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# run_async(&problem, concurrency).await method on
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# every algorithm — for IO-bound evaluations.
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# heuropt = { version = "0.8", features = ["serde", "parallel", "async"] }
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```
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## Define a problem
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## Define a problem and run an optimizer
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You're designing a car. Three things you can pick: **engine
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displacement** (1.0–6.0 L), **curb weight** (1100–2200 kg, where
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going lighter requires aluminum/carbon and costs money), and
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**aerodynamic drag** (Cd from 0.20 to 0.40, where slipperier needs
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expensive aero R&D). Four things you want to optimize: **price**,
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**0-60 acceleration**, **fuel consumption**, **idle noise** — all
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in tension.
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The relationships between decisions and objectives are nonlinear
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and coupled: engine cost grows superlinearly with displacement,
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weight reduction below 1500 kg costs a quadratic premium, drag
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reduction below 0.35 Cd costs a 1.5-power premium, and 0-60 depends
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on weight × engine in a non-trivial way. You can't just sweep one
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slider — the Pareto front is a genuine surface in 3D decision space,
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and finding it by hand is hopeless.
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NSGA-III is the canonical many-objective (4+) optimizer; it uses
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Das–Dennis reference points to keep the front well-spread.
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```rust
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use heuropt::prelude::*;
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struct SchafferN1;
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struct PickACar;
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impl Problem for SchafferN1 {
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type Decision = Vec<f64>;
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impl Problem for PickACar {
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type Decision = Vec<f64>; // [engine_liters, weight_kg, drag_cd]
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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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Objective::minimize("price_thousand_dollars"),
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Objective::minimize("seconds_to_60mph"),
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Objective::minimize("fuel_gallons_per_100mi"),
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Objective::minimize("noise_db_at_idle"),
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])
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}
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fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
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let v = x[0];
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Evaluation::new(vec![v * v, (v - 2.0).powi(2)])
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let displacement = x[0]; // liters
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let weight = x[1]; // kg
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let drag = x[2]; // dimensionless Cd
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// Price ($k): engine cost grows superlinearly; weight reduction
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// below 1500 kg and drag reduction below 0.35 Cd both cost extra.
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let engine_cost = 3.0 * displacement.powf(1.6);
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let weight_cost = ((1500.0 - weight).max(0.0) / 100.0).powi(2) * 2.0;
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let aero_cost = ((0.35 - drag).max(0.0) * 100.0).powf(1.5) * 0.4;
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let price = 10.0 + engine_cost + weight_cost + aero_cost;
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// 0-60 (s): heavier = slower; bigger engine = quicker but with
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// diminishing returns.
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let weight_factor = (weight - 1100.0) / 1000.0;
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let engine_factor = ((displacement - 1.0) / 5.0).max(0.0).powf(0.7);
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let zero_to_sixty = 5.0 + 5.0 * weight_factor - 4.0 * engine_factor;
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// Fuel consumption (gal/100 mi): all three matter.
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let fuel = 0.5 + 0.5 * displacement + 0.5 * weight / 1000.0 + 4.0 * drag;
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// Idle noise (dB): engine dominates, mildly nonlinear.
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let noise = 60.0 + 3.0 * displacement.powf(1.2);
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Evaluation::new(vec![price, zero_to_sixty, fuel, noise])
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}
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}
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fn main() {
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let bounds = vec![
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(1.0_f64, 6.0_f64), // engine
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(1100.0_f64, 2200.0_f64), // weight
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(0.20_f64, 0.40_f64), // drag
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];
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let mut optimizer = Nsga3::new(
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Nsga3Config {
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population_size: 100,
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generations: 200,
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reference_divisions: 5,
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seed: 42,
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},
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RealBounds::new(bounds.clone()),
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CompositeVariation {
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crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.9),
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mutation: PolynomialMutation::new(bounds, 20.0, 1.0 / 3.0),
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},
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);
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let result = optimizer.run(&PickACar);
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let mut front: Vec<_> = result.pareto_front.iter().collect();
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front.sort_by(|a, b| {
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a.evaluation.objectives[0]
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.partial_cmp(&b.evaluation.objectives[0]).unwrap()
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});
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println!("{:>5} {:>5} {:>4} {:>6} {:>5} {:>5} {:>5}",
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"L", "kg", "Cd", "$k", "0-60", "fuel", "dB");
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for c in &front {
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let d = &c.decision;
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let o = &c.evaluation.objectives;
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println!("{:>5.2} {:>5.0} {:>4.2} {:>6.1} {:>5.1} {:>5.2} {:>5.1}",
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d[0], d[1], d[2], o[0], o[1], o[2], o[3]);
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}
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}
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```
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## Run NSGA-II
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Run it (`cargo run --release`) and you get 100 cars on the front.
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A representative slice from the actual output, hand-picked across
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the spectrum:
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```rust
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use heuropt::prelude::*;
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# struct SchafferN1;
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# impl Problem for SchafferN1 {
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# type Decision = Vec<f64>;
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# fn objectives(&self) -> ObjectiveSpace {
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# ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
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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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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: 60, generations: 80, seed: 42 };
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let mut optimizer = Nsga2::new(config, initializer, variation);
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let result = optimizer.run(&SchafferN1);
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println!("Pareto front size: {}", result.pareto_front.len());
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```text
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L kg Cd $k 0-60 fuel dB ← role
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1.00 1505 0.35 13.0 7.0 3.17 63.0 cheap baseline
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2.00 1370 0.35 22.4 5.1 3.56 66.7 sensible sport sedan
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2.45 1330 0.38 28.5 4.5 3.92 68.8 quicker midprice
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1.00 1430 0.21 35.8 6.6 2.54 63.0 fuel-saver (small + slippery)
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3.50 1300 0.25 52.9 3.5 3.88 73.3 genuine sports car
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5.27 1100 0.20 108.1 1.4 4.48 82.0 hypercar corner
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```
|
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See `examples/toy_nsga2.rs` for the full version.
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### Reading the result
|
||||
|
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Every row is **non-dominated** — no row is strictly better than
|
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another on every metric. The interesting part is what each one does
|
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*differently*:
|
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|
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- The **cheap baseline** ($13k) takes the path of least resistance:
|
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smallest engine, no weight reduction, average drag. Slow but
|
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affordable.
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- The **sensible sedan** ($22k) trades $9k for **2 seconds off
|
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0-60** by running a 2.0L engine with mild weight reduction.
|
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- The **fuel-saver** is interesting: it's a 1.0L econobox engine,
|
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but it spends $22k *just on aero* (0.21 Cd) to push fuel
|
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consumption down to **2.54 gal/100mi**. The optimizer figured
|
||||
out that aero matters more than displacement at this fuel point.
|
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No human would pick this combo by intuition.
|
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- The **sports car** ($53k) doesn't blow money on the lightest
|
||||
possible weight — it picks 1300 kg, because dropping further
|
||||
costs disproportionately and the 3.5L engine is doing most of
|
||||
the acceleration work.
|
||||
- The **hypercar corner** ($108k) is the optimizer pushing every
|
||||
decision to its ceiling: minimum weight (1100 kg), minimum
|
||||
drag (0.20 Cd), big engine (5.3L). Sub-1.5 second 0-60, but
|
||||
you pay for it on every other axis except fuel (because the
|
||||
weight + aero savings partly cancel the V8's thirst).
|
||||
|
||||
That last point is the kind of insight a Pareto front gives you
|
||||
that no single-objective optimizer would: **the cheapest fuel-
|
||||
efficient car is not the smallest engine alone**, it's a small
|
||||
engine + aggressive aero. **The lightest sports car is not the
|
||||
lightest possible**, it's the point where weight cost stops paying
|
||||
back in 0-60. The optimizer doesn't tell you what to buy — it
|
||||
hands you the frontier of *every defensible compromise* and lets
|
||||
you pick by your own priorities.
|
||||
|
||||
## Implement a custom optimizer
|
||||
|
||||
@@ -105,13 +204,7 @@ where
|
||||
// Evaluate them with `problem.evaluate(...)`.
|
||||
// Keep the best, or maintain a Pareto archive.
|
||||
// Return an OptimizationResult.
|
||||
# OptimizationResult::new(
|
||||
# Population::new(Vec::new()),
|
||||
# Vec::new(),
|
||||
# None,
|
||||
# 0,
|
||||
# 0,
|
||||
# )
|
||||
todo!()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
+2
-2
@@ -8,8 +8,8 @@ needed.
|
||||
|
||||
| Version | Supported |
|
||||
|---------|--------------------|
|
||||
| 0.5.x | ✅ |
|
||||
| ≤ 0.4.x | ❌ (please upgrade) |
|
||||
| 0.8.x | ✅ |
|
||||
| ≤ 0.7.x | ❌ (please upgrade) |
|
||||
|
||||
heuropt is pre-1.0; the public API may change between minor versions.
|
||||
Once 1.0.0 ships, the support window will be at least the latest two
|
||||
|
||||
@@ -222,9 +222,15 @@ evaluate via rayon when the feature is on. **Seeded runs stay
|
||||
bit-identical** to serial mode.
|
||||
|
||||
```toml
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
heuropt = { version = "0.8", features = ["parallel"] }
|
||||
```
|
||||
|
||||
If your evaluation is **IO-bound** (HTTP request, RPC, subprocess)
|
||||
rather than CPU-bound, use the `async` feature instead — it gives
|
||||
you `AsyncProblem` and a `run_async(&problem, concurrency).await`
|
||||
method on every algorithm in the catalog. See the
|
||||
[Async evaluation cookbook recipe](./cookbook/async.md).
|
||||
|
||||
## TL;DR table
|
||||
|
||||
| Situation | Pick |
|
||||
|
||||
@@ -15,11 +15,11 @@ The columns:
|
||||
|
||||
| Library | Lang | Algorithms | Multi-obj | Surrogates | Determinism | Async |
|
||||
|---|---|---|---|---|---|---|
|
||||
| **heuropt 0.5** | Rust | 35 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ⏳ planned |
|
||||
| **heuropt 0.8** | Rust | 33 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ✅ `AsyncProblem` + `run_async` on every algorithm |
|
||||
| pymoo | Python | ~25 | ✅ extensive | partial (BO via plug-ins) | ✅ | ❌ |
|
||||
| DEAP | Python | flexible toolbox | ✅ | ❌ | ✅ | ❌ |
|
||||
| hyperopt | Python | TPE-focused | ❌ | ✅ TPE | partial | partial |
|
||||
| optuna | Python | TPE / CMA-ES / NSGA-II | ✅ | ✅ TPE, BoTorch via plug-in | ✅ | ✅ |
|
||||
| optuna | Python | TPE / CMA-ES / NSGA-II | ✅ | ✅ TPE, BoTorch via plug-in | ✅ | partial (study-level, not eval-level) |
|
||||
| MOEA Framework | Java | ~40 | ✅ very extensive | ❌ | ✅ | ❌ |
|
||||
| metaheuristics-rs | Rust | ~10 | partial | ❌ | ✅ | ❌ |
|
||||
| argmin | Rust | line-search / quasi-Newton | ❌ | ❌ | ✅ | ❌ |
|
||||
@@ -38,12 +38,13 @@ The columns:
|
||||
written for clarity, no trait-object plumbing, no GATs in user-
|
||||
facing APIs. Reading `RandomSearch` should be enough to write a
|
||||
new optimizer.
|
||||
- You have **IO-bound evaluations** — calling an HTTP service, an
|
||||
RPC, or a subprocess — and want first-class `async fn evaluate`
|
||||
support. heuropt is the only mainstream optimization library that
|
||||
ships this (see [Async evaluation](./cookbook/async.md)).
|
||||
|
||||
## When *not* to pick heuropt
|
||||
|
||||
- You need **first-class async / await** for evaluations that talk to
|
||||
HTTP services or spawn subprocesses. heuropt is sync; that's on
|
||||
the roadmap but not shipping yet.
|
||||
- You need **gradient-based** optimization. Use `argmin` (Rust) or
|
||||
`scipy.optimize` (Python) — heuropt is gradient-free by design.
|
||||
- You need **GPU-accelerated** evaluations. heuropt's `evaluate`
|
||||
|
||||
@@ -134,8 +134,11 @@ parallel.
|
||||
result.
|
||||
- **No error type.** Invalid configuration panics with a clear
|
||||
message; this matches the style of the built-in algorithms.
|
||||
- **No async.** `evaluate` is synchronous; for async work, drive it
|
||||
on a tokio runtime around the optimizer loop yourself.
|
||||
- **No async on the trait.** `Optimizer<P>` is synchronous. For
|
||||
async evaluation, implement [`AsyncProblem`](https://docs.rs/heuropt/latest/heuropt/core/async_problem/trait.AsyncProblem.html)
|
||||
on your problem and use the `run_async(&problem, concurrency)`
|
||||
method that comes with the `async` feature. See the
|
||||
[Async evaluation cookbook recipe](./async.md).
|
||||
|
||||
The smallness is the point: you should be able to read a built-in
|
||||
algorithm and write your own in an afternoon. See
|
||||
|
||||
@@ -9,7 +9,7 @@ population, and rayon parallelizes that batch.
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
heuropt = { version = "0.8", features = ["parallel"] }
|
||||
```
|
||||
|
||||
There's nothing else to opt into in your code. The
|
||||
@@ -104,6 +104,16 @@ to scope it.
|
||||
parallelism rarely helps.
|
||||
- The algorithm is steady-state (Paes, SA, hill climber).
|
||||
|
||||
## `parallel` vs `async`
|
||||
|
||||
| If your `evaluate` is… | Use |
|
||||
|---|---|
|
||||
| CPU-bound (math, simulation) | `parallel` feature (this recipe) |
|
||||
| IO-bound (HTTP, RPC, subprocess) | `async` feature → see [Async evaluation](./async.md) |
|
||||
|
||||
Both can be on at once if your evaluation does *both* substantial
|
||||
CPU work *and* IO. The two features are independent.
|
||||
|
||||
[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
|
||||
[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
|
||||
[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
|
||||
|
||||
@@ -6,7 +6,7 @@ The shortest path from a fresh project to a working optimizer.
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
heuropt = "0.5"
|
||||
heuropt = "0.8"
|
||||
```
|
||||
|
||||
The default feature set is small. Optional features:
|
||||
@@ -14,86 +14,126 @@ The default feature set is small. Optional features:
|
||||
- `parallel` — rayon-backed parallel population evaluation.
|
||||
- `serde` — `Serialize` / `Deserialize` derives on the core data
|
||||
types.
|
||||
- `async` — `AsyncProblem` trait + per-algorithm `run_async` for
|
||||
IO-bound evaluations.
|
||||
|
||||
```toml
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
heuropt = { version = "0.8", features = ["parallel"] }
|
||||
```
|
||||
|
||||
## 2. Define a problem
|
||||
## 2. Define a problem and run an optimizer
|
||||
|
||||
A problem is a struct that implements the [`Problem`] trait. You tell
|
||||
heuropt what kind of decision your problem takes (`Vec<f64>`,
|
||||
`Vec<bool>`, …), what objectives it has (minimize or maximize), and
|
||||
how to score one decision.
|
||||
|
||||
We'll fit a straight line to a handful of `(x, y)` data points by
|
||||
finding the slope and intercept that minimize the sum of squared
|
||||
errors — same objective as least-squares regression. For a smooth
|
||||
single-objective continuous problem like this, [`CmaEs`] is a strong
|
||||
default.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Sphere;
|
||||
struct LineFit {
|
||||
points: Vec<(f64, f64)>,
|
||||
}
|
||||
|
||||
impl Problem for Sphere {
|
||||
type Decision = Vec<f64>;
|
||||
impl Problem for LineFit {
|
||||
type Decision = Vec<f64>; // [slope, intercept]
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
ObjectiveSpace::new(vec![Objective::minimize("sum_squared_error")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f: f64 = x.iter().map(|v| v * v).sum();
|
||||
Evaluation::new(vec![f])
|
||||
let (slope, intercept) = (x[0], x[1]);
|
||||
let sse: f64 = self
|
||||
.points
|
||||
.iter()
|
||||
.map(|(px, py)| (py - (slope * px + intercept)).powi(2))
|
||||
.sum();
|
||||
Evaluation::new(vec![sse])
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
// Five noisy points roughly on the line y = 2x + 1.
|
||||
let problem = LineFit {
|
||||
points: vec![(0.0, 1.1), (1.0, 2.9), (2.0, 5.1), (3.0, 6.8), (4.0, 9.2)],
|
||||
};
|
||||
|
||||
// Search box: slope and intercept each in [-10, 10].
|
||||
let bounds = RealBounds::new(vec![(-10.0, 10.0); 2]);
|
||||
|
||||
let mut opt = CmaEs::new(
|
||||
CmaEsConfig {
|
||||
population_size: 12,
|
||||
generations: 80,
|
||||
initial_sigma: 1.0,
|
||||
eigen_decomposition_period: 1,
|
||||
initial_mean: None,
|
||||
seed: 42,
|
||||
},
|
||||
bounds,
|
||||
);
|
||||
|
||||
let result = opt.run(&problem);
|
||||
let best = result.best.expect("at least one feasible candidate");
|
||||
let (slope, intercept) = (best.decision[0], best.decision[1]);
|
||||
println!(
|
||||
"best fit: y = {:.4} x + {:.4} (sse = {:.4e}, evaluations = {})",
|
||||
slope, intercept, best.evaluation.objectives[0], result.evaluations,
|
||||
);
|
||||
|
||||
println!();
|
||||
println!("predictions vs actual:");
|
||||
for (px, py) in &problem.points {
|
||||
let pred = slope * px + intercept;
|
||||
println!(
|
||||
" x = {:.1} actual = {:.2} predicted = {:.4} residual = {:+.4}",
|
||||
px, py, pred, py - pred,
|
||||
);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The Sphere function is a single-objective continuous problem: minimize
|
||||
`f(x) = Σ xᵢ²`. The optimum is `x = 0`, `f = 0`.
|
||||
|
||||
## 3. Pick an algorithm and run it
|
||||
|
||||
For a smooth single-objective continuous problem, [`CmaEs`] is a
|
||||
strong default. Configure it, build it, run it.
|
||||
|
||||
```rust,no_run
|
||||
# use heuropt::prelude::*;
|
||||
# struct Sphere;
|
||||
# impl Problem for Sphere {
|
||||
# type Decision = Vec<f64>;
|
||||
# fn objectives(&self) -> ObjectiveSpace {
|
||||
# ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
# }
|
||||
# fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
# Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
# }
|
||||
# }
|
||||
let bounds = RealBounds::new(vec![(-5.0, 5.0); 5]); // 5-dim search box
|
||||
|
||||
let mut opt = CmaEs::new(
|
||||
CmaEsConfig {
|
||||
population_size: 12,
|
||||
generations: 80,
|
||||
initial_sigma: 1.0,
|
||||
eigen_decomposition_period: 1,
|
||||
initial_mean: None,
|
||||
seed: 42,
|
||||
},
|
||||
bounds,
|
||||
);
|
||||
|
||||
let result = opt.run(&Sphere);
|
||||
|
||||
let best = result.best.expect("at least one feasible candidate");
|
||||
println!("best f = {:.3e} at x = {:?}", best.evaluation.objectives[0], best.decision);
|
||||
```
|
||||
|
||||
Run with `cargo run --release` — heuristic optimization is allergic
|
||||
to debug builds. Expect output like:
|
||||
to debug builds. The actual output:
|
||||
|
||||
```text
|
||||
best f = 1.4e-29 at x = [-1.6e-15, 4.5e-16, ...]
|
||||
best fit: y = 2.0100 x + 1.0000 (sse = 1.0700e-1, evaluations = 960)
|
||||
|
||||
predictions vs actual:
|
||||
x = 0.0 actual = 1.10 predicted = 1.0000 residual = +0.1000
|
||||
x = 1.0 actual = 2.90 predicted = 3.0100 residual = -0.1100
|
||||
x = 2.0 actual = 5.10 predicted = 5.0200 residual = +0.0800
|
||||
x = 3.0 actual = 6.80 predicted = 7.0300 residual = -0.2300
|
||||
x = 4.0 actual = 9.20 predicted = 9.0400 residual = +0.1600
|
||||
```
|
||||
|
||||
CMA-ES drops to machine epsilon on the Sphere in well under 80
|
||||
generations.
|
||||
### Reading the result
|
||||
|
||||
CMA-ES recovered **slope ≈ 2.01, intercept ≈ 1.00** — within
|
||||
hundredths of the underlying line `y = 2x + 1` that the data was
|
||||
sampled from. The residuals are evenly distributed in sign (3
|
||||
positive, 2 negative) and small in magnitude (the largest is 0.23
|
||||
at `x = 3`), which means the fit is balancing the noise rather than
|
||||
chasing any single point.
|
||||
|
||||
The total **sum of squared errors is 0.107** — that is the value
|
||||
the optimizer was actually minimizing, and it matches the answer
|
||||
you'd get from running `numpy.polyfit` or solving the normal
|
||||
equations directly. CMA-ES is overkill for a two-parameter problem
|
||||
(closed-form least-squares does it in one step), but the **same
|
||||
code shape** scales straight up to nonlinear models, robust loss
|
||||
functions, or constrained variants where there is no closed form.
|
||||
|
||||
It used 960 evaluations to get there. That's `population_size × generations`
|
||||
= 12 × 80 = 960, and CMA-ES converges to machine epsilon on
|
||||
problems this clean in well under that budget.
|
||||
|
||||
## 4. What just happened
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ hyperopt, optuna, DEAP). heuropt's design priorities:
|
||||
|
||||
## What's in the box
|
||||
|
||||
heuropt v0.5 ships **35 algorithms** spanning:
|
||||
heuropt v0.8 ships **33 algorithms** spanning:
|
||||
|
||||
- Single-objective continuous: `RandomSearch`, `HillClimber`,
|
||||
`OnePlusOneEs`, `SimulatedAnnealing`, `GeneticAlgorithm`,
|
||||
@@ -65,6 +65,15 @@ ProjectToSimplex), the metrics (hypervolume, spacing), and the Pareto
|
||||
utilities (dominance, fronts, crowding distance, Das–Dennis reference
|
||||
points, the `ParetoArchive`) that you'd expect.
|
||||
|
||||
**Async evaluation** (since v0.8, behind the `async` feature flag):
|
||||
when your `evaluate` function is IO-bound — calling an HTTP service,
|
||||
an RPC, or a subprocess — implement [`AsyncProblem`] and use
|
||||
`run_async(&problem, concurrency).await` on any algorithm in the
|
||||
catalog. heuropt is the only mainstream optimization library with
|
||||
first-class async support across its entire algorithm set.
|
||||
|
||||
[`AsyncProblem`]: https://docs.rs/heuropt/latest/heuropt/core/async_problem/trait.AsyncProblem.html
|
||||
|
||||
## How to use this guide
|
||||
|
||||
If you're new to heuropt, read it linearly:
|
||||
|
||||
@@ -3,6 +3,64 @@
|
||||
Per-release notes for upgrading between heuropt versions. Skip the
|
||||
sections that don't apply to your starting version.
|
||||
|
||||
## To 0.8
|
||||
|
||||
### From 0.5.x
|
||||
|
||||
**Additive feature only.** Bumping `heuropt = "0.8"` is enough for
|
||||
any code that doesn't need async evaluation. To opt into async,
|
||||
enable the new feature flag:
|
||||
|
||||
```toml
|
||||
heuropt = { version = "0.8", features = ["async"] }
|
||||
```
|
||||
|
||||
What changed:
|
||||
|
||||
- New `async` feature flag, gated on the
|
||||
[`futures`](https://crates.io/crates/futures) crate.
|
||||
- New `core::async_problem::AsyncProblem` trait — mirrors `Problem`
|
||||
but with `async fn evaluate_async`.
|
||||
- New `core::async_problem::AsyncPartialProblem` trait — mirrors
|
||||
`PartialProblem` for multi-fidelity (Hyperband) workloads.
|
||||
- `run_async(&problem, concurrency).await` on **every** algorithm in
|
||||
the catalog (33 of them) for IO-bound evaluations.
|
||||
- New cookbook recipe: [Async evaluation](./cookbook/async.md).
|
||||
|
||||
### From 0.7.x
|
||||
|
||||
`0.7.0` introduced an experimental observability layer (`Snapshot`,
|
||||
`Observer`, `run_with`, `MaxTime`, `TargetFitness`, `Stagnation`,
|
||||
`Periodic`, `AnyOf`, `AllOf`, `TracingObserver`) and three
|
||||
additional Pareto metrics (`igd`, `igd_plus`, `r2`). All of those
|
||||
were rolled back in `0.8.0` — the design didn't bake long enough
|
||||
and they shipped half-wired (`run_with` was overridden on only 3 of
|
||||
35 algorithms). The `tracing` feature flag is also gone.
|
||||
|
||||
If your code uses any of those APIs, the migration is:
|
||||
|
||||
- Remove all `run_with(&problem, &mut observer)` calls and replace
|
||||
with `run(&problem)`.
|
||||
- Remove all uses of `Observer`, `Snapshot`, `ControlFlow`,
|
||||
`MaxTime`, `MaxIterations`, `TargetFitness`, `Stagnation`,
|
||||
`Periodic`, `AnyOf`, `AllOf`, `TracingObserver`.
|
||||
- Remove all uses of `metrics::igd::igd`, `metrics::igd::igd_plus`,
|
||||
`metrics::r2::r2`.
|
||||
- Remove `Population::as_slice()` calls (the method is gone).
|
||||
- Drop the `tracing` feature from your `Cargo.toml` if you had it.
|
||||
|
||||
Stop conditions can still be implemented by wrapping `run` in a
|
||||
loop with a custom RNG-driven termination, or by wrapping
|
||||
the algorithm yourself; observers may return as a public API in a
|
||||
future release once the design has settled.
|
||||
|
||||
The async work introduced in 0.7.0 (`AsyncProblem` + `run_async`)
|
||||
**survived** and is broadened in 0.8: every algorithm in the catalog
|
||||
now has a `run_async` (0.7.0 only had it on three of them), and
|
||||
multi-fidelity problems get a parallel `AsyncPartialProblem` trait
|
||||
that Hyperband's `run_async` consumes. Existing call sites continue
|
||||
to work unchanged.
|
||||
|
||||
## To 0.5
|
||||
|
||||
### From 0.4.x
|
||||
|
||||
+12
-13
@@ -18,10 +18,10 @@ versions — use them at your own risk.
|
||||
|
||||
While we are pre-1.0:
|
||||
|
||||
- **Minor bumps (`0.5 → 0.6`) may break the public API.** The
|
||||
- **Minor bumps (`0.8 → 0.9`) may break the public API.** The
|
||||
CHANGELOG calls out everything that changed, and a **migration
|
||||
guide** in this book documents the move.
|
||||
- **Patch bumps (`0.5.0 → 0.5.1`) only contain bug fixes,
|
||||
- **Patch bumps (`0.8.0 → 0.8.1`) only contain bug fixes,
|
||||
performance improvements, and additive non-breaking features.**
|
||||
No deprecations, no removals.
|
||||
|
||||
@@ -29,24 +29,20 @@ While we are pre-1.0:
|
||||
|
||||
In rough order of likelihood:
|
||||
|
||||
1. **`Optimizer<P>` may grow new optional methods** for callbacks,
|
||||
stop conditions, and save/resume support. These will land as
|
||||
methods with default implementations so existing trait impls
|
||||
keep compiling, but the trait shape will be different.
|
||||
2. **Algorithm config structs may gain fields.** All current configs
|
||||
1. **Algorithm config structs may gain fields.** All current configs
|
||||
are public-field structs; adding a non-`Default` field is a
|
||||
breaking change. We may switch to builder patterns to avoid this
|
||||
class of break, or we may add `#[non_exhaustive]`.
|
||||
3. **The `Snapshot`, `Observer`, and `Checkpoint` types** (planned
|
||||
for a future release) will land as new public surfaces.
|
||||
4. **Some operators may move between `operators` and `pareto`** as
|
||||
2. **Some operators may move between `operators` and `pareto`** as
|
||||
the boundary between "things that produce candidates" and "Pareto
|
||||
utilities" gets clearer.
|
||||
|
||||
What is **not** likely to change:
|
||||
|
||||
- The `Problem` trait shape.
|
||||
- The `AsyncProblem` / `AsyncPartialProblem` trait shapes.
|
||||
- The `Variation` / `Initializer` / `Repair` traits.
|
||||
- The `Optimizer<P>` trait — single `run` method, no callbacks.
|
||||
- The `Evaluation` / `Candidate` / `Population` / `OptimizationResult`
|
||||
data types.
|
||||
- The seeded determinism property.
|
||||
@@ -60,12 +56,12 @@ Across minor versions, output may change if an algorithm's
|
||||
implementation changes (e.g. a perf rewrite that reorders
|
||||
floating-point operations, or a new feature that changes the
|
||||
RNG-consumption pattern). The CHANGELOG calls this out explicitly
|
||||
when it happens. As of v0.5, the entire history of perf optimizations
|
||||
has been bit-identical against the v0.3.0 reference.
|
||||
when it happens. As of v0.8, the entire history of perf
|
||||
optimizations has been bit-identical against the v0.3.0 reference.
|
||||
|
||||
## MSRV (minimum supported Rust version)
|
||||
|
||||
heuropt's MSRV is **1.85** as of v0.5. This is tested in CI against
|
||||
heuropt's MSRV is **1.85** as of v0.8. This is tested in CI against
|
||||
every PR.
|
||||
|
||||
MSRV bumps are treated as patch-bump-eligible (they don't break the
|
||||
@@ -79,6 +75,9 @@ The current optional features:
|
||||
- `serde` — adds `Serialize` / `Deserialize` derives on the core data
|
||||
types.
|
||||
- `parallel` — rayon-backed parallel population evaluation.
|
||||
- `async` — `AsyncProblem` + `AsyncPartialProblem` traits, plus a
|
||||
`run_async` method on every algorithm in the catalog, for
|
||||
IO-bound evaluations.
|
||||
|
||||
Features added in 0.x can be renamed or removed in any minor bump
|
||||
that documents the change. Removing a feature is treated like a
|
||||
|
||||
+7
-1
@@ -4,7 +4,7 @@
|
||||
//! The crate aims to make three things obvious:
|
||||
//!
|
||||
//! 1. **Define a problem** by implementing [`Problem`](crate::core::Problem).
|
||||
//! 2. **Run a built-in optimizer** — pick from 35 algorithms in
|
||||
//! 2. **Run a built-in optimizer** — pick from 33 algorithms in
|
||||
//! [`algorithms`] covering single-objective continuous (CMA-ES,
|
||||
//! Differential Evolution, Nelder-Mead, …), multi-objective
|
||||
//! (NSGA-II, MOPSO, IBEA, MOEA/D, …), many-objective (NSGA-III,
|
||||
@@ -32,6 +32,12 @@
|
||||
//! - `parallel` — rayon-backed parallel population evaluation in
|
||||
//! every population-based algorithm. Seeded runs stay bit-
|
||||
//! identical to serial mode.
|
||||
//! - `async` — adds the
|
||||
//! [`AsyncProblem`](crate::core::async_problem::AsyncProblem) and
|
||||
//! [`AsyncPartialProblem`](crate::core::async_problem::AsyncPartialProblem)
|
||||
//! traits and a `run_async(&problem, concurrency).await` method on
|
||||
//! every algorithm. Use this when your `evaluate` does IO (HTTP,
|
||||
//! RPC, subprocess) — see the [Async evaluation cookbook recipe](https://swaits.github.io/heuropt/cookbook/async.html).
|
||||
//!
|
||||
//! # Quick example
|
||||
//!
|
||||
|
||||
Reference in New Issue
Block a user