seemps.optimization.power_method

seemps.optimization.power_method(H: MPO | MPOList | MPOSum, inverse: bool = False, shift: float = 0.0, guess: MPS | None = None, maxiter: int = 1000, maxiter_cgs: int = 50, tol: float = 1e-13, tol_variance: float = 1e-14, tol_cgs: float = 1e-8, tol_up: float | None = None, upward_moves: int = 5, strategy: Strategy = DESCENT_STRATEGY, callback: Callable[[MPS, OptimizeResults], Any] | None = None) → PowerMethodOptimizeResults

Ground state search of Hamiltonian H by power method.

Parameters:

HMPO | MPOList | MPOSum

Hamiltonian in MPO form.

guessMPS | None

Initial guess of the ground state. If None, defaults to a random MPS deduced from the operator’s dimensions.

maxiterint

Maximum number of iterations (defaults to 1000).

maxiter_cgsint

Maximum number of iterations of CGS (defaults to 50).

tolfloat

Energy variation that indicates termination (defaults to 1e-13).

tol_upfloat, default = tol

If energy fluctuates up below this tolerance, continue the optimization.

tol_variancefloat

Energy variance target (defaults to 1e-14).

strategyStrategy | None

Linear combination of MPS truncation strategy. Defaults to DESCENT_STRATEGY.

callbackCallable[[MPS, OptimizeResults], Any] | None

A callable called after each iteration (defaults to None).

Returns:

PowerMethodOptimizeResults

Results from the optimization. See OptimizeResults.