seemps.analysis.cross.cross_greedy

seemps.analysis.cross.cross_greedy(black_box: BlackBox, cross_strategy: CrossStrategyGreedy = CrossStrategyGreedy(), initial_points: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None) → CrossResults

Computes the MPS representation of a black-box function using the tensor cross-approximation (TCI) algorithm based on two-site optimizations following greedy updates of the pivot matrices. The black-box function can represent several different structures. See black_box for usage examples.

Parameters:

black_boxBlackBox

The black box to approximate as a MPS.

cross_strategyCrossStrategy, default=CrossStrategy()

A dataclass containing the parameters of the algorithm.

initial_pointsnp.ndarray, optional

A collection of initial points used to initialize the algorithm. If None, an initial point at the origin is used.

Returns:

CrossResults

A dataclass containing the MPS representation of the black-box function, among other useful information.