The per-file Phase 1 test commits were written without running rustfmt
as I went; this pass formats the new test code (long assert_eq! lines
wrapped, etc.). Formatting-only — no behavioural change.
Companion to the feat(explorer) commit. Bumps the version and
brings every cross-referencing doc up to v0.9 currency.
- Cargo.toml: version 0.8.0 -> 0.9.0.
- CHANGELOG: 0.9.0 entry covering the explorer export, the
Problem-side metadata additions, the AlgorithmInfo trait, the
pick_a_car example, and the new cookbook recipe.
- README: closing paragraph of the PickACar example points users
at the explorer with a one-call snippet
(`ExplorerExport::from_result(...).with_algorithm_info(...)
.to_file(...)?`). Version snippets bumped 0.8 -> 0.9.
- New cookbook recipe at docs/book/src/cookbook/explorer.md
covering: enabling the serde feature, enriching Problem with
labels/units/decision-schema, the export call, the JSON schema,
and custom decision-type handling.
- SUMMARY.md and cookbook.md link the new recipe.
- migration.md: new "To 0.9" section documenting the additive
changes (purely backwards-compatible upgrade from 0.8.x).
- introduction.md, comparison.md, choosing-an-algorithm.md,
stability.md: version refs bumped 0.8 -> 0.9.
- cookbook/parallel.md, cookbook/async.md: version refs bumped
0.8 -> 0.9.
- getting-started.md: version refs bumped, serde feature
description expanded to mention the explorer module.
- SECURITY.md: supported-versions table moves to 0.9.x.
Adds a tiny additive surface that turns any OptimizationResult into
a self-describing JSON file the heuropt-explorer webapp can load.
Real Pareto fronts have 50–200+ candidates spanning 2–7+ objectives;
reading them as numbers in a terminal scales badly. This commit
ships the heuropt-side of the explorer — the schema and the export
API. The webapp itself lives in a separate repo on its own cadence.
Three trait/type extensions, all with working defaults so existing
impls compile untouched:
- Objective gains optional `label: Option<String>` and
`unit: Option<String>` fields, plus fluent builders
`.with_label("Price").with_unit(\"\$k\")`. Existing
`Objective::minimize(name)` / `Objective::maximize(name)` are
unchanged. Both fields are #[serde(default,
skip_serializing_if = \"Option::is_none\")] so existing JSON
round-trips cleanly.
- Problem trait gains an optional
`fn decision_schema(&self) -> Vec<DecisionVariable>` with default
empty impl. Override it to provide pretty names / labels / units /
bounds for the explorer; the default produces fallback x[0],
x[1], … names. New DecisionVariable type at
`heuropt::core::DecisionVariable` with builder methods.
- New `heuropt::traits::AlgorithmInfo` trait with `name()`
(required) and `seed()` (default None). Every built-in algorithm
— all 33 — implements it. Separate from Optimizer<P> so
multi-fidelity Hyperband (which uses PartialProblem) implements
it uniformly.
The new explorer module:
- `heuropt::explorer::ExplorerExport` envelope with versioned
schema (SCHEMA_VERSION = 1).
- ExplorerCandidate per row, with front_rank from
non_dominated_sort attached at export time so downstream tools
don't re-derive it.
- ToDecisionValues adapter trait with provided impls for Vec<f64>,
Vec<bool>, Vec<usize>, Vec<i64>; custom decision types implement
one method.
- Free functions to_json / to_writer / to_file plus a builder API
(with_algorithm_info, with_problem_name, with_wall_clock,
with_timestamp).
- Gated on the existing `serde` feature, which now also pulls in
`serde_json` as a dep.
The example:
- `examples/pick_a_car.rs` — promotes the README's PickACar to a
real example, fully enriched with Objective labels/units and a
decision_schema. Runs NSGA-III for 200 generations, prints a
sample slice, writes pick_a_car.json. Gated on `serde`.
10 new explorer unit tests cover round-trip serde, fallback
decision-variable names, enriched export, AlgorithmInfo flow,
front-rank correctness, and the ToDecisionValues impls. Lib test
count went from 229 to 242.
Async coverage was incomplete in 0.7 (only RandomSearch and
DifferentialEvolution had run_async). 0.8 closes the gap: every one
of the 33 algorithms now exposes
run_async(&problem, concurrency).await, gated on the async feature.
- Population-based algorithms fan out per-generation evaluations
through evaluate_batch_async with concurrency-bounded
FuturesOrdered chunks.
- Steady-state algorithms (HillClimber, SimulatedAnnealing,
OnePlusOneEs, Paes, NelderMead) await each step sequentially;
they accept the concurrency parameter for API uniformity.
- TabuSearch fans out the K-neighbor batch each step.
- Surrogate algorithms (BayesianOpt, Tpe) batch the initial design
and await per-iteration acquisitions sequentially so the surrogate
can update between picks.
- Hyperband uses a new AsyncPartialProblem trait (mirroring
PartialProblem for multi-fidelity workloads) and a parallel
evaluate_batch_at_budget_async helper; each Successive-Halving
rung fans out its budgeted evaluations.
All paths preserve seeded determinism: RNG draws happen on the main
task in the same order as the sync path, and only the evaluations
are concurrent.
Adds a dedicated cookbook recipe at docs/book/src/cookbook/async.md
with a worked example (DifferentialEvolution under tokio) and
guidance on picking concurrency. Cross-references in SUMMARY.md
and cookbook.md are updated to surface the new recipe.
The follow-up docs commit reconciles the rest of the user guide
and README to describe the new feature; this commit is the bare
async surface.
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.
Auger & Hansen 2005 IPOP-CMA-ES: wraps the existing CmaEs in a restart
loop that doubles the population size and re-randomizes the mean
whenever a restart trigger fires. Specifically addresses the failure
mode we observed on Rastrigin (vanilla CMA-ES = 2.3 vs DE = 0).
Restart triggers:
- The whole budget for one inner CmaEs run finishes without improvement
- (More sophisticated triggers — eigenvalue collapse, condition-number
blow-up, sigma stagnation — are left for future versions; the
per-run budget trigger captures the bulk of the practical benefit)
Each restart:
- Doubles the population_size (Auger & Hansen 2005)
- Re-randomizes the initial mean to a fresh point in the bounds box
- Resets sigma to the user's initial value
Same Vec<f64> + single-objective constraints as CmaEs. The total
budget is divided across restarts; restart budget grows with
population. Tests verify it beats vanilla CMA-ES on Rastrigin.
Nelder & Mead 1965: gradient-free local optimizer that maintains a
simplex of n+1 points in n-D and at each iteration replaces the worst
vertex by one of {reflect, expand, outside-contract, inside-contract,
shrink} relative to the centroid of the rest. The five standard
coefficients (reflection α=1, expansion γ=2, contraction ρ=0.5,
shrinkage σ=0.5) are exposed in the config but default to canonical
values so users can leave them alone.
Single-objective only, Vec<f64> only, bounds enforced by clamping
each new vertex. Termination is purely iteration-count for v0.2;
"vertices have collapsed" stopping is a future enhancement.
Filling a real gap: heuropt had population-based local search
(SimulatedAnnealing, HillClimber) but no classical direct-search
algorithm. Excellent for low-dim smooth-ish problems where a
population is overkill.
swaits