quri_parts.algo.optimizer.interface module

Params: TypeAlias = 'npt.NDArray[np.float_]'

Represents a parameter vector subject to optimization. (A gradient vector is also represented as Params.)

CostFunction

Cost function for optimization.

alias of Callable[[npt.NDArray[np.float_]], float]

GradientFunction

Gradient function for optimization.

alias of Callable[[npt.NDArray[np.float_]], npt.NDArray[np.float_]]

class OptimizerStatus(value)

Bases: Enum

Status of optimization.

SUCCESS = 1

No error, not converged yet.

FAILED = 2

The optimization failed and cannot be continued.

CONVERGED = 3

The optimization converged.

class OptimizerState(params, cost=0.0, status=OptimizerStatus.SUCCESS, niter=0, funcalls=0, gradcalls=0)

Bases: object

An immutable (frozen) dataclass representing an optimizer state.

Parameters:

• params (algo.optimizer.interface.Params) –

• cost (float) –

• status (OptimizerStatus) –

• niter (int) –

• funcalls (int) –

• gradcalls (int) –

params: Params

Current parameter values.

cost: float = 0.0

Current value of the cost function.

status: OptimizerStatus = 1

Optimization status.

niter: int = 0

Number of iterations.

funcalls: int = 0

Number of cost function calls.

gradcalls: int = 0

Number of gradient function calls.

property n_params: int

Number of parameters.

class Optimizer(*args, **kwargs)

Bases: Protocol

A protocol class for optimizers.

abstract get_init_state(init_params)

Returns an initial state for optimization.

Parameters:

init_params (algo.optimizer.interface.Params) –

Return type:

OptimizerState

abstract step(state, cost_function, grad_function=None)

Run a single optimization step and returns a new state.

Parameters:

• state (OptimizerState) –

• cost_function (algo.optimizer.interface.CostFunction) –

• grad_function (algo.optimizer.interface.GradientFunction | None) –

Return type:

OptimizerState