declivity 0.1.0

Unified benchmarking framework for evolutionary and quasi-Newton optimization algorithms (DES, CMA-ES, MF-CMA-ES, L-BFGS-B)

declivity

A unified benchmarking framework and modern Python port for evolutionary and quasi-Newton optimization algorithms. Thesis project by Jedrzej Grabski.

What this is

Two things, glued together:

1. A unified framework for fairly comparing optimizers under identical conditions (shared budgets, shared seeds, shared problem definitions).
2. Modern Python implementations of algorithms that previously only existed as inconsistent academic ports — CMA-ES variants from R, L-BFGS-B from Fortran 77 — rewritten as clean, type-checked Python that plugs into the framework.

The current algorithm roster:

Algorithm	Family	Notes
DES	Evolutionary	Adaptive Ft via evolution-path history (ring buffer)
CMA-ES	Evolutionary	Full covariance matrix, rank-1 + rank-μ updates
MF-CMA-ES	Evolutionary	Matrix-free CMA-ES (Arabas), optional PPMF
L-BFGS-B	Quasi-Newton	Pure-Python port of Fortran v3.0, configurable B₀

L-BFGS-B is the newest addition and ships with the more involved studies (rotated Ellipsoid, CMA-ES → L-BFGS-B covariance handoff).

Quick start

pdm install
pdm run run-example      # Sphere demo (DES on 10D Sphere)
pdm run run-r            # CEC2017 F10, 10 seeds, writes CSVs for R cross-check

Or directly:

PYTHONPATH=. pdm run python experiments/basic/declarative_plotting.py
PYTHONPATH=. pdm run python experiments/handoff/multimodal.py --num-seeds 25

Minimal example

Run one optimizer:

import numpy as np
from declivity import AlgorithmFactory
from declivity.algorithms.choices import AlgorithmChoice
from declivity.plotting import plot_metrics
from declivity.utils.benchmark_functions import Sphere

func = Sphere(dimensions=10)
initial_point = np.random.uniform(-50, 50, 10)

optimizer = AlgorithmFactory.create_optimizer(
    algorithm=AlgorithmChoice.CMAES,
    func=func,
    initial_point=initial_point,
    lower_bounds=-100,
    upper_bounds=100,
)
result = optimizer.optimize()
print(f"f* = {result.best_fitness:.4e} after {result.evaluations} evals")

plot_metrics(result, save_path="cmaes.png")  # every default panel for CMA-ES

Compare two algorithms:

from declivity.plotting import plot_comparison

plot_comparison(
    {"CMA-ES": cmaes_result, "L-BFGS-B": lbfgsb_result},
    save_path="comparison.png",
)
# Auto-picks the panels both algorithms expose (convergence + step_size by
# default). CMA-ES sigma and L-BFGS-B step_length end up on the same axes
# under one "Step Size" panel — semantic comparability is the point.

Multi-seed benchmark:

from declivity.benchmarking import Benchmark, Problem, SingleAlgorithm, CMAESLBFGSBHandoff
from declivity.plotting import plot_benchmark_convergence, plot_benchmark_boxplot

bench = Benchmark(problems=[...], algorithms=[...], seeds=range(25), output_dir=...)
bench.run()
plot_benchmark_convergence(bench.traces, problems, algorithms, save_path="conv.png")
plot_benchmark_boxplot   (bench.traces, problems, algorithms, save_path="box.png")

See DOCUMENTATION.md for the full API.

Project layout

declivity/
├── declivity/                    Library code (algorithms, framework)
│   ├── core/                     BaseOptimizer, AlgorithmFactory, BaseConfig
│   ├── algorithms/               DES, CMA-ES, MF-CMA-ES, L-BFGS-B
│   ├── benchmarking/             Problem, Benchmark, RunTrace
│   │                             + BenchmarkAlgorithm / HandoffAlgorithm ABCs
│   ├── utils/                    Benchmark functions, constraint handlers,
│   │                             repair strategies, initial-point generators,
│   │                             population initializers, helpers
│   ├── logging/                  BaseLogData / PopulationLogData + per-algo loggers
│   └── plotting/                 Declarative panel system (Panel + 8 entry points)
│
├── experiments/                  Runnable studies (one script per study)
│   ├── basic/                    Tutorial demos, sanity checks, declarative API demos
│   ├── cross_validation/         Cross-checks against R reference impls
│   ├── lbfgsb/                   L-BFGS-B feature studies
│   ├── handoff/                  CMA-ES → L-BFGS-B handoff studies
│   └── report/                   Plot regeneration utilities
│
├── plots/                        Experiment outputs (gitignored, mirrors experiments/)
├── docs/                         Algorithm lectures + design notes + reports
├── reference/                    Reference R implementations and their outputs
└── notes/                        Local scratch notes (gitignored)

Where to look next

• API reference: DOCUMENTATION.md
• Framework design principles: docs/framework_design.md
• Experiment index: experiments/README.md
• L-BFGS-B internals: docs/lbfgsb_lecture.md
• Initial-Hessian design: docs/lbfgsb_initial_hessian_design.md
• CMA-ES → L-BFGS-B handoff study: docs/covariance_handoff_when_it_matters.md
• CMA-ES diagnostic plot legend: docs/cmaes_diagnostic_plots.md

Tech stack

• Python 3.12 (strict), managed with PDM
• NumPy 2.x, SciPy 1.15+, Matplotlib 3.10+
• opfunu for CEC2017 benchmark functions
• joblib for optional parallel benchmark execution

Status

Thesis-in-progress. There is no formal test suite — correctness is validated by cross-comparison against the R reference implementations in reference/ and by the supervisor-report experiments under plots/report/.

License

MIT — see LICENSE.