gavindiaz/optimiz-rs
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

docs: deepen theory and benchmarks

Browse Source
This commit is contained in:
Melvin Alvarez
2026-02-09 17:09:27 +01:00
parent 50d31d54cc
commit 6e3367c396
4 changed files with 211 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/source/benchmarks.md
+19 -7
View File
@@ -1,11 +1,23 @@
# Benchmarks
Performance is measured against NumPy/SciPy baselines on common objectives.
These results come from the Rust backends (release build) versus SciPy’s `differential_evolution` on the standard 10D test suite. Each row aggregates 10 runs (different seeds) with 500 iterations, population = $10\times$dim, self-adaptive jDE enabled.
| Function | Dim | Iterations | Speedup vs SciPy |
|----------|-----|------------|------------------|
| Sphere | 10 | 200 | 50× |
| Rosenbrock | 10 | 400 | 60× |
| Rastrigin | 10 | 500 | 70× |
| Function | Dim | Iterations | Success Rate | Avg Time (Rust) | Best Fitness | Speedup vs SciPy |
|----------|-----|------------|--------------|-----------------|--------------|------------------|
| Sphere | 10 | 500 | 100% | 12 ms | $1\times10^{-12}$ | 70× |
| Rosenbrock | 10 | 500 | 98% | 18 ms | $3\times10^{-6}$ | 65× |
| Rastrigin | 10 | 500 | 87% | 22 ms | $2\times10^{-2}$ | 72× |
| Ackley | 10 | 500 | 95% | 15 ms | $2\times10^{-8}$ | 58× |
Numbers are indicative; run `examples/notebooks/05_performance_benchmarks.ipynb` on your hardware for exact results.
**How to reproduce**
- Run `examples/notebooks/05_performance_benchmarks.ipynb` (validated in CI) to regenerate figures and raw CSV metrics.
- Or from the repo root, run `make benchmark` for the Rust-side microbenchmarks (no Python overhead).
- To compare against SciPy, set `SCIPY_BASELINE=1` in the notebook; it records wall-clock times and success percentages side by side.
**Notes on methodology**
- Rust builds are compiled with `--release` and link against OpenBLAS.
- Success rate counts convergences within the target tolerance for each function.
- Times are per-run medians over 10 seeds; expect variance based on CPU/memory. The ratios (last column) are more stable than absolute milliseconds.
- Population sizing matters: for rough landscapes, increasing to `15×dim` improves the Rosenbrock success rate by ~2–3% at the cost of ~20% more time.