radia-optuna

radia-optuna is the separately distributed MATLAB optimization component of the Radia monorepo. It installs the radia.optuna MATLAB namespace, the independent 20-command optuna_mex, and the checked Optuna 4.9.0 compatibility contracts. 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.

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. The first PyPI publication additionally requires the repository's trusted publisher to be registered for the new radia-optuna project; until that external registration and release tag exist, install the verified wheel artifact rather than assuming the PyPI name is already live.