fcmaes-rust

One of 18 related repositories. The repository overview shows what each one does and how they connect.

Choosing an optimization tool? Start with the shared solver-selection and agent guide or the optimization ecosystem overview. This repository supplies native Rust black-box, multi-objective and quality-diversity optimizers, Python bindings, and a source-built job worker. The guide also covers Python objectives, LP/MIP and smooth constrained NLPs.

Slides (PDF) · Book (PDF) · YouTube video

fcmaes-rust is a native Rust implementation of fast, parallel, gradient-free optimization algorithms and selected fcmaes application examples. The optimizer implementation in fcmaes-core is 100% Rust: it does not compile, link, load, or call the original fast-cma-es C++ implementation. Optimizer numerics, retry coordination, random-number generation, fitness evaluation, and parallel execution all run in Rust.

fcmaes-rust reimplements the algorithm set of fast-cma-es natively in Rust rather than wrapping it, and is an alternative for both Rust and Python users via its PyO3 API. For objectives written in Python, prefer the Python library fast-cma-es: it uses multiprocessing, so Python objectives evaluate in parallel. The PyO3 API here is the better fit when the objective itself is in Rust.

In this project, “port” means that algorithms were translated, reimplemented, and tested in Rust. It does not mean an FFI wrapper around the old C++ code; the public API documentation explains the algorithms through their primary literature and records implementation-specific behavior explicitly.

The repository is a standalone Cargo workspace. GTOP models and example objective functions also execute in Rust. This includes the Mazda mass and constraint response surfaces: their compact model data is embedded in the example crate and evaluated by native Rust code. The Buckingham–Pi example also implements dimension-matrix enumeration, numerical nullspace analysis, regression, and continuous-exponent optimization directly in Rust; it has no BuckinghamPy dependency.

Prompt-driven application tutorials reuse the native models through optional tutorial workers.

The fluid dynamics prompt workshop uses the existing native room-ventilation CLI to compare designs, test unseen release locations and examine grid/setup sensitivity.

The prompt-driven surrogate tutorial adds a checked agent workflow over the existing fcmaes-job-worker.

experiment-craft runs reproducible campaigns of these jobs through optimization-run: parameter sweeps, dependent steps, budgets, deadlines and reports that keep each job's claim and evidence. Its baseline rebuilds and tests fcmaes-job-worker at a pinned revision together with the runner and the other workers. It is in early development.

Experimental surrogate optimization

The new source-only fcmaes-surrogate crate and fcmaes.surrogate/1 job profile implement small-budget exact-GP optimization with synchronous batches and a cancellable Rust simulation API. Current limits are 32 coordinates, 256 new plus warm observations, and eight inner threads per task. See the API guide (it links the executable tutorial), measured results and reviewed roadmap. Thompson-sampling TuRBO, asynchronous scheduling and large-budget handoff are future milestones. In an equal-budget comparison with BoTorch, egobox, scikit-optimize and Optuna it reached the lowest median final regret of the GP methods on four of five small problems at 1–3 orders of magnitude less proposal time; for separable or many-modal problems with tens of variables, BiteOpt retry (fcmaes.retry/1) remained better.

Core design principle: improve results for the same wait

Independent retry turns otherwise idle CPU cores into additional chances to find a better solution before the user's deadline. Many useful stochastic, local, and third-party optimizers execute one search on one thread. The generic fcmaes_core::retry coordinator runs independent instances concurrently and returns the best retained result; the wrapped optimizer does not have to be an fcmaes implementation.

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