seemps.analysis.cross.cross_greedy

seemps.analysis.cross.cross_greedy(black_box, cross_strategy=CrossStrategyGreedy(maxiter=100, maxbond=1000, tol_sampling=1e-10, norm_sampling=inf, num_samples=1000, tol_norm_2=None, rng=Generator(PCG64) at 0x7F72A29C6C00, tol_pivot=1e-10, partial=True, maxiter_partial=5, points_partial=10), initial_points=None, callback=None)

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 random point is used.

callbackCallable, optional

A callable called on the MPS after each iteration. The output of the callback is included in a list ‘callback_output’ in CrossResults.

Returns:

CrossResults

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