iOpt Interface Classes

problem

solution

solver

solver_parametrs

class iOpt.solver_parametrs.SolverParameters(eps: numpy.float64 = 0.01, r: numpy.float64 = 2.0, iters_limit: int = 20000, evolvent_density: int = 10, eps_r: numpy.float64 = 0.01, refine_solution: bool = False, start_point: iOpt.trial.Point = [], number_of_parallel_points: int = 1, async_scheme: bool = False, timeout: int = - 1, proportion_of_global_iterations: float = 0.95, start_lambdas: list = [], number_of_lambdas: int = 10, is_scaling: bool = False)[исходный код]

Базовые классы: object

The SolverParameters class allows you to define the parameters for searching the optimal solution

__init__(eps: numpy.float64 = 0.01, r: numpy.float64 = 2.0, iters_limit: int = 20000, evolvent_density: int = 10, eps_r: numpy.float64 = 0.01, refine_solution: bool = False, start_point: iOpt.trial.Point = [], number_of_parallel_points: int = 1, async_scheme: bool = False, timeout: int = - 1, proportion_of_global_iterations: float = 0.95, start_lambdas: list = [], number_of_lambdas: int = 10, is_scaling: bool = False)[исходный код]

Constructor of SolverParameters class

Параметры

  • eps – The accuracy of the solution to the task at hand. Smaller values – higher search accuracy, less likely to stop prematurely.

  • r – Reliability parameter. Higher value of r – slower convergence, higher probability of finding a global minimum.

  • iters_limit – maximum number of search trials.

  • evolvent_density – density of evolvent construction. The default density is \(2^{-10}\) on the hypercube \([0,1]^N\), which means that the maximum search accuracy is \(2^{-10}\).

  • eps_r – parameter affecting the speed of solving the problem with constraints. eps_r = 0 - slow convergence to the exact solution, eps_r>0 - fast convergence to the neighbourhood of the solution.

  • refine_solution – if true, the solution will be refined using the local method.

  • start_point – point of initial approximation to the solution.

  • number_of_parallel_points – number of parallel computed trials.

  • timeout – calculation time limit in minutes.

  • proportion_of_global_iterations – share of global iterations in the search when using the local method.

to_string() → str[исходный код]

Creates a string containing the values of the main parameters

Результат

string containing the parameters values

trial

class iOpt.trial.FunctionType(value)[исходный код]

Базовые классы: enum.Enum

An enumeration.

OBJECTIV = 1
CONSTRAINT = 2
class iOpt.trial.Point[исходный код]

Базовые классы: object

class iOpt.trial.FunctionValue(type: iOpt.trial.FunctionType = FunctionType.OBJECTIV, functionID: int = 0)[исходный код]

Базовые классы: object

class iOpt.trial.Trial(point: iOpt.trial.Point, function_values: array([None], dtype=object))[исходный код]

Базовые классы: object

iOpt Implementation