seemps.analysis.cross.cross_maxvol

seemps.analysis.cross.cross_maxvol(black_box, cross_strategy=CrossStrategyMaxvol(maxiter=100, maxbond=1000, tol_sampling=1e-10, norm_sampling=inf, num_samples=1000, tol_norm_2=None, rng=Generator(PCG64) at 0x7F72A29C6A40, rank_kick=(0, 1), maxiter_maxvol_square=10, tol_maxvol_square=1.05, tol_maxvol_rect=1.05, fortran_order=True), initial_points=None, callback=None)

Computes the MPS representation of a black-box function using the tensor cross-approximation (TCI) algorithm based on one-site optimizations using the rectangular maxvol decomposition. 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.