odatse.algorithm.min_search module

class odatse.algorithm.min_search.Algorithm(info: Info, runner: Runner = None, domain=None, run_mode: str = 'initial')

Bases: AlgorithmBase

Algorithm class for performing minimization using scipy.optimize.minimize.

The optimization method is selected by the method parameter in the [algorithm.minimize] section (default: “Nelder-Mead”). All other entries of the section except ODAT-SE-specific keys are passed through to scipy.optimize.minimize as its options argument.

Setting basinhopping (a boolean, or a [algorithm.minimize.basinhopping] table whose entries are passed to scipy.optimize.basinhopping) switches to global optimization by basin hopping, with the configured method serving as the local minimizer.

Initialize the Algorithm class.

Parameters:

• info (Info) – Information object containing algorithm settings.

• runner (Runner) – Runner object for submitting jobs.

• domain – Domain object defining the search space.

• run_mode (str) – Mode of running the algorithm.

__init__(info: Info, runner: Runner = None, domain=None, run_mode: str = 'initial') → None

Initialize the Algorithm class.

Parameters:

• info (Info) – Information object containing algorithm settings.

• runner (Runner) – Runner object for submitting jobs.

• domain – Domain object defining the search space.

• run_mode (str) – Mode of running the algorithm.

_initialize() → None

Set up initial state for a fresh run.

Nelder-Mead does not use checkpointing, so this is a no-op. The simplex initialisation is done in _prepare().

_output_results()

Output the results of the minimization to files.

_post()

Post-process the results after minimization.

_prepare()

Prepare the initial simplex for the Nelder-Mead algorithm.

The simplex is only passed to scipy when method is Nelder-Mead; for other methods it is built but unused.

_run() → None

Run the minimization algorithm.

class odatse.algorithm.min_search._ClippedRandomDisplacement(rng, stepsize, min_list, max_list)

Bases: object

Random displacement for basinhopping, clipped to the search region.

The default take_step of scipy’s basinhopping may propose points outside [min_list, max_list], which would only waste solver evaluations on the inf-penalty. Clipping keeps every hop inside the region. The stepsize attribute is exposed so that basinhopping’s adaptive stepsize adjustment keeps working.