optimiz-rs/docs/source/benchmarks.md

Benchmarks

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	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×

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.

What the notebook plots

• Convergence trajectories (best fitness vs iterations) for each function
• Histograms of self-adapted (F, CR) values mid-run
• Speedup bars and success-rate bars vs SciPy on the same seeds
• Residuals heatmap for a sweep over population sizes (optional cell)

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.

Additional workloads (see notebook cells):

• High-dimension stress test: Rastrigin 50D, population 800, 700 iterations (shows scaling trend)
• HMM forward-backward throughput: synthetic 3-state Gaussian emissions (Rust vs pure Python)
• MFG solver timing: 100×100 grid vs 150×150 grid (observed ~1.8× runtime increase, stable memory)