radia-optuna

radia-optuna is an independent, separately distributed MATLAB optimization component from the Radia monorepo. It installs the radia.optuna MATLAB namespace, the 20-command optuna_mex, and checked compatibility contracts whose behavioral oracle is Optuna 4.9.0. It does not install or load the Radia solver, NGSolve, oneMKL, or Cubit.

Install it by itself:

python -m pip install radia-optuna
radia-optuna-path
radia-optuna-doctor --json

Or install the independently versioned release validated by Radia through:

python -m pip install "radia[optuna]"

radia[optuna] installs the native MATLAB/Simulink package without heavy Python numerical dependencies. Use radia[optuna-upstream] when GP, scrambled-QMC, or importance parity also needs the pinned upstream Python, SciPy, and PyTorch stack.

Add the printed directory to MATLAB, then use the upstream-shaped API:

addpath("<output of radia-optuna-path>")
study = radia.optuna.create_study( ...
    sampler=radia.optuna.TPESampler(Seed=42));
study.optimize(@(trial) (trial.suggest_float("x", -2, 2) - 0.25)^2, 100);

Generic Simulink optimization is part of the standalone contract. radia.optuna.SimulinkRunner configures Simulink.SimulationInput objects, runs the model, extracts objectives and constraints, classifies failed trials, and records reproducible execution metadata without loading Radia. Radia-owned electromagnetic models and application blocks remain in the main distribution.

The wheel also ships the generic radia.simulink.buildOptunaBlock Level-2 MATLAB S-Function block and radia.simulink.addOptunaMonitor. The block runs one trial per sample, persists the normalized study tables after every state transition, and exposes best value, trial counts, status, Pareto points, and failure telemetry as ordinary Simulink signals. The monitor uses Simulink Scope and XY Graph blocks; it does not require a browser or the Radia solver.

The distribution is Windows x64 because the current native artifact is optuna_mex.mexw64. Native Random/TPE/evolutionary/pruner workflows do not start Python. Install radia-optuna[upstream] for features intentionally executed through pinned upstream Python packages, including checked GP acquisition, scrambled QMC, and parameter importance.

LTspiceRunner, SheetMetalRunner, and internal.runLTspiceTrial are shipped as explicitly classified Radia integration adapters. The standalone core does not call them; choosing one requires the full radia installation.

Upstream attribution

This is an independent, unofficial project. It is not affiliated with, sponsored by, or endorsed by Preferred Networks, Inc. or the Optuna project. It does not use the Optuna logo or present itself as an official Optuna distribution.

Optuna, the Optuna logo and any related marks are trademarks of Preferred Networks, Inc.

Optuna and the official optuna/optuna-mcp server are separate MIT-licensed upstream projects and are not bundled in this wheel. See THIRD_PARTY_NOTICES.md for their copyright and license notices. Shared MCP Study/Trial/visualization operations remain owned by the official server; radia-mcp covers only MATLAB/Simulink differences.

Release boundary

CI builds and verifies a distinct radia-optuna-wheel artifact. A radia-optuna-v<version> tag may publish only that exact CI artifact, after rechecking its tag, version, API inventory, MEX inventory, and dependency boundary, including the bundled third-party notice.

Publication also uses the standalone four-machine release-quad lane. After the successful main CI run, execute python tools/release_quad.py optuna-candidate --ci-run-id <id> --target all, then pass the retained wheel to python tools/release_quad.py optuna-done --wheel <path>. Only after that gate passes may the matching commit be tagged. The manual release workflow requires the same CI run ID and the candidate SHA256, publishes that exact wheel to PyPI, and attaches it to the matching GitHub Release.