Replace the internal Vec<CompletedTrial<V>> with a pluggable Storage<V>
trait. MemoryStorage is the default (no behavior change for existing
users). Behind the `journal` feature flag, JournalStorage persists
trials to a JSONL file with fs2 file locking for multi-process safety.
- Storage<V> trait with push(), trials_arc(), refresh() methods
- MemoryStorage<V> wraps Arc<RwLock<Vec<CompletedTrial<V>>>>
- JournalStorage<V> appends JSON lines with exclusive file locks
- Study::with_sampler_and_storage() general constructor
- Study::with_journal() convenience constructor (journal feature)
- Refresh from storage on create_trial() for multi-process discovery
- MSRV bumped to 1.89
fastrand is smaller, faster, and has no dependencies. Add rng_util
helper for f64 range generation since fastrand lacks a built-in
equivalent. Migrate all samplers, KDE modules, and fANOVA to use
fastrand's concrete Rng type instead of rand's trait-based generics.
Implement a classical Bayesian optimization sampler using a GP surrogate
with Matérn 5/2 kernel and Expected Improvement acquisition function.
Feature-gated behind `gp = ["dep:nalgebra"]`.
Implement functional ANOVA decomposition behind the `fanova` feature
flag. A self-contained random forest is trained on trial data, then
marginal predictions are used to compute per-parameter main effects
and pairwise interaction effects, normalized to sum to 1.0.
Adds Study::fanova() / fanova_with_config(), FanovaResult, and
FanovaConfig. Includes unit and integration tests covering dominant
parameters, interaction detection, consistency with correlation-based
importance, and error handling.
Add a `visualization` feature flag that generates self-contained HTML
reports with interactive Plotly.js charts for offline visualization of
optimization results. Charts include optimization history, slice plots,
parallel coordinates, parameter importance, trial timeline, and
intermediate values (learning curves).
Add micro-benchmarks for TPE, Random, and Grid samplers with varying
history sizes, end-to-end optimization benchmarks on Sphere and
Rosenbrock, and a convergence tracking example that outputs CSV data.
Includes 6 standard test functions (Sphere, Rosenbrock, Rastrigin,
Ackley, Branin, Hartmann6) with correctness tests at known optima.
Implement CMA-ES (Covariance Matrix Adaptation Evolution Strategy) as a
new sampler for continuous optimization. Uses nalgebra for matrix
operations and implements the full Hansen 2016 algorithm: mean update,
evolution paths, covariance matrix adaptation, and cumulative step-size
adaptation.
Key features:
- Builder API with configurable sigma0, population_size, and seed
- Three-phase state machine: discovery, active sampling, generation update
- Rejection sampling with bounds clipping fallback
- Log-scale and step parameter support via internal-space mapping
- Categorical parameters handled via random sampling fallback
- Numerical robustness: eigenvalue clamping, symmetry enforcement
Add SobolSampler using Owen-scrambled Sobol sequences (sobol_burley
crate) for better uniform coverage of the parameter space compared to
random sampling. Supports all distribution types including log-scale
and stepped parameters.
Add optional structured logging via the tracing crate:
- info-level spans on all optimize* methods (direction, n_trials/duration)
- info events for trial completed, new best value, trial pruned
- debug events for trial failed and parameter sampled
- Zero overhead when the feature is disabled
Add Serialize/Deserialize derives to all public data types (ParamValue,
Distribution, Direction, TrialState, ParamId, AttrValue, CompletedTrial)
gated behind a `serde` feature flag. Introduce StudySnapshot struct and
Study::save()/Study::load() for persisting and restoring study state as
human-readable JSON.
Manuel Raimann