quara.minimization_algorithm.projected_fast_iterative_shrinkage_thresholding_algorithm module

class ProjectedFastIterativeShrinkageThresholdingAlgorithm(func_proj=None)[source]

Bases: quara.minimization_algorithm.projected_gradient_descent.ProjectedGradientDescent

This algorithm is based on the following:

Eliot Bolduc, George C. Knee, Erik M. Gauger & Jonathan Leach, “Projected gradient descent algorithms for quantum state tomography”,

Constructor

Parameters

func_proj (Callable[[np.ndarray], np.ndarray], optional) – function of projection, by default None

is_loss_sufficient()[source]

returns whether the loss is sufficient.

Returns

whether the loss is sufficient.

Return type

bool

is_option_sufficient()[source]

returns whether the option is sufficient.

Returns

whether the option is sufficient.

Return type

bool

optimize(loss_function, loss_function_option, algorithm_option, on_iteration_history=False)[source]

optimizes using specified parameters.

Parameters
Returns

the result of the optimization.

Return type

ProjectedFastIterativeShrinkageThresholdingAlgorithmResult

Raises

  • ValueError – when on_value of loss_function is False.

  • ValueError – when on_gradient of loss_function is False.

class ProjectedFastIterativeShrinkageThresholdingAlgorithmOption(on_algo_eq_constraint=True, on_algo_ineq_constraint=True, var_start=None, max_iteration_optimization=1000, max_iteration_proj_physical=100000, delta=None, mode_stopping_criterion_gradient_descent='single_difference_loss', num_history_stopping_criterion_gradient_descent=1, mode_proj_order='eq_ineq', eps=None)[source]

Bases: quara.minimization_algorithm.projected_gradient_descent.ProjectedGradientDescentOption

Constructor

Parameters

  • on_algo_eq_constraint (bool, optional) – whether this algorithm needs on algorithm equality constraint, by default True

  • on_algo_ineq_constraint (bool, optional) – whether this algorithm needs on algorithm inequality constraint, by default True

  • var_start (np.ndarray, optional) – initial variable for the algorithm, by default None

  • max_iteration_optimization (int, optional) – maximun number of iterations of optimization, by default 1000.

  • max_iteration_proj_physical (int, optional) – maximun number of iterations of projection to physical, by default 100000.

  • delta (float, optional) – algorithm option r, by default None

  • mode_stopping_criterion_gradient_descent (str, optional) – mode of stopping criterion for gradient descent, by default “single_difference_loss”

  • num_history_stopping_criterion_gradient_descent (int, optional) – number of history to be used stopping criterion for gradient descent, by default 1 this must be a integer and greater than or equal to 1.

  • mode_proj_order (str, optional) – the order in which the projections are performed, by default “eq_ineq”.

  • eps (float, optional) – algorithm option epsilon, by default None

property delta: float

returns algorithm option delta.

Returns

algorithm option delta.

Return type

float

class ProjectedFastIterativeShrinkageThresholdingAlgorithmResult(value, computation_time=None, k=None, fx=None, x=None, error_values=None)[source]

Bases: quara.minimization_algorithm.projected_gradient_descent.ProjectedGradientDescentResult

Constructor

Parameters

  • value (np.ndarray) – the result of the minimization.

  • computation_time (float, optional) – computation time for the minimization, by default None

  • k (int) –

  • fx (List[numpy.ndarray]) –

  • x (List[numpy.ndarray]) –

  • error_values (List[float]) –