seemps.optimization.gradient_descent#
- seemps.optimization.gradient_descent(H: MPO | MPOList | MPOSum, guess: MPS | MPSSum, maxiter: int = 1000, tol: float = 1e-13, tol_up: float | None = None, tol_variance: float = 1e-14, strategy: Strategy = DESCENT_STRATEGY, callback: Callable[[MPS, OptimizeResults], Any] | None = None) OptimizeResults[source]#
Ground state search of Hamiltonian H by gradient descent.
- Parameters:
- H
MPO|MPOList|MPOSum Hamiltonian in MPO form.
- state
MPS|MPSSum Initial guess of the ground state.
- maxiter
int Maximum number of iterations (defaults to 1000).
- tol
float Energy variation that indicates termination (defaults to 1e-13).
- tol_up
float,default= tol If energy fluctuates up below this tolerance, continue the optimization.
- tol_variance
float Energy variance target (defaults to 1e-14).
- strategy
Strategy,default=DESCENT_STRATEGY Linear combination of MPS truncation strategy.
- callback
Callable[[MPS,OptimizeResults],Any] |None A callable called after each iteration (defaults to None).
- H
- Returns:
OptimizeResultsResults from the optimization. See
OptimizeResults.