Theme: production lifecycle. heuropt becomes deployable for long-
running, real-world workloads. No breaking changes — Optimizer trait
gains a default-impl run_with method that falls back to run.
Adds:
- src/observer/ module: Snapshot, Observer trait, ControlFlow, plus
built-in MaxTime / MaxIterations / TargetFitness / Stagnation /
Periodic / AnyOf / AllOf and a closure impl.
- Optimizer::run_with(problem, observer): default-impl on the trait,
overridden for full per-gen visibility on Nsga2, RandomSearch, and
DifferentialEvolution. Other algorithms inherit a final-only
notification — full per-gen support follows incrementally.
- New 'tracing' optional feature plus TracingObserver that emits
structured debug! events per generation.
- src/metrics/igd.rs: IGD + IGD+ performance indicators against a
reference set.
- src/metrics/r2.rs: R2 indicator using the weighted Tchebycheff
utility; pair with das_dennis for the canonical weight set.
- examples/constrained.rs: BNH constrained 2-objective problem
solved with NSGA-II + observer composition (MaxTime.or(Periodic)).
Bumps Cargo.toml to 0.6.0; CHANGELOG entry consolidates the above.
Existing 247 unit + 38 doctest + 32 algorithm-property + property /
metric / numerical-stability tests all pass; bit-identical compare
output verified post-DE refactor.
The M≥3 branch of `hso_recursive` cloned every input point into
`sorted: Vec<Vec<f64>>` solely so it could sort. Each clone is M
f64s allocated; with N points per call and ~30 HV calls per SMS-EMOA
generation × 30 k generations, that's millions of small Vec<f64>
allocations.
Sort indices into a `Vec<usize>` instead, then iterate the original
points by index. The pre-projection step still produces a
Vec<Vec<f64>> (which the active-prefix slicing requires), but we
save the outer N inner-Vec clones per call.
gungraun (instructions):
- hypervolume_nd_3d n=30: 87 969 → 70 334 (-20 %, 1.25×)
- hypervolume_nd_3d n=100: 422 767 → 367 767 (-13 %, 1.15×)
Cumulative vs the v0.3.0 baseline:
- hypervolume_nd_3d n=30: 676 902 → 70 334 (9.6×)
- hypervolume_nd_3d n=100: 13 523 760 → 367 767 (37×)
Wall-clock impact is in the noise on the compare harness because the
SMS-EMOA worst-front HV calls operate on small fronts (5–10 points
once converged). The win is most visible in synthetic dense-front
HV benchmarks.
The HSO recursion in `hypervolume_nd` had three overheads that
dominated SMS-EMOA's per-generation cost on DTLZ2 (5.6 s baseline,
~30 k generations × ~40 HV calls per generation = ~1.2 M HV calls
per run):
1. `active = sorted.clone()` plus `active.iter().position(...)`
linear scan to remove the just-processed point each band — O(N)
per band, total O(N²) per HV call.
2. Per-band re-projection
`active.iter().map(|q| q[..last].to_vec())` — full
Vec<Vec<f64>> rebuild for every band, O(N·M) allocations per HV
call.
3. `non_dominated_projection` called even when recursing into the
M=2 base case, whose sweep already filters dominated points
internally.
Replace (1) with prefix-slicing `projected_all[..=k]` (sort points
ascending by last axis once; the active set at each band is just a
prefix). Pre-project once outside the loop (2). Skip the explicit
non-dominance filter when the inner recursion is M=2 (3).
Bit-identical output verified by re-running the compare harness and
diffing against the v0.3.0 snapshot — every quality metric matches
to the last decimal.
gungraun (instructions):
- hypervolume_nd_3d n=30: 676 902 → 87 969 (-87 %, 7.7×)
- hypervolume_nd_3d n=100: 13 523 760 → 422 767 (-97 %, 32×)
Wall-clock (compare harness, 10-seed mean):
- SMS-EMOA / DTLZ2: 5643 ms → 1413 ms (-4230 ms, -75 %)
Corne, Jerram, Knowles & Oates 2001: divides objective space into a
hyperbox grid and uses per-box population counts to drive selection
toward sparsely-populated regions.
Each generation:
- Maintain an external archive of non-dominated members
- Build a hyperbox grid (`grid_divisions` per axis on the archive's
current axis ranges); count members per box
- Selection picks two parents by region-based tournament: choose two
random non-empty boxes and take a uniform-random member from the
one with fewer occupants
- Variation produces an offspring; insert into archive, dropping
dominated members and (if archive overflows) the most-crowded
occupant of the most-occupied box
Tests cover non-empty front on Schaffer N.1, deterministic reruns,
and panic on `archive_size == 0`.
Generalizes the existing 2-D hypervolume to arbitrary M ≥ 1 dimensions
using the standard recursive Hypervolume-by-Slicing-Objectives (HSO)
algorithm from While et al. 2006:
- For M = 1: return reference[0] - min(points[0])
- For M = 2: sort by axis 0, sweep accumulating rectangles (matches
hypervolume_2d's existing exact behavior)
- For M ≥ 3: sort by the last axis, peel off slices of increasing
thickness and recursively compute the (M−1)-dimensional HV of each
slice's projected non-dominated subset
Direction-aware: minimization-oriented input is the entry point, so
maximize objectives are negated by the caller via
`ObjectiveSpace::as_minimization` before the recursion runs.
Tested against:
- the existing 2-D analytical case (3 points → area 6)
- a known 3-D unit-cube case (1 point at origin, ref [1,1,1] → 1)
- empty front → 0
- agreement with hypervolume_2d on random 2-D fronts
Exact 2D dominated hypervolume against a fixed reference point. Sorts
points by the first minimization-oriented objective ascending, then
sweeps and accumulates the dominated rectangle area against the
reference. Points that don't strictly dominate the reference are
ignored. Panics with a clear message if the objective space does not
have exactly two objectives (spec §14.2).
Tests cover a known-area front, the no-coverage case, and the panic on
non-2D problems.
Standard Schott spacing: for each front point compute the Manhattan
distance to its nearest neighbor on minimization-oriented objective
values; the spacing metric is the population standard deviation of
those nearest-neighbor distances.
Returns 0.0 for empty or single-point fronts (spec §14.1).
swaits