optimization.optimizer.scipy.base#

Optiland Scipy Optimization Module

This module contains classes for various optimization algorithms that can be used to solve optimization problems defined in the OptimizationProblem class. This module provides a generic optimizer class and several specific optimizers that utilize different algorithms from the SciPy library.

Kramer Harrison, 2024

Classes

OptimizerGeneric(problem)

Generic optimizer class for solving optimization problems.
class OptimizerGeneric(problem: OptimizationProblem)[source]#

Generic optimizer class for solving optimization problems.

Parameters:

problem (OptimizationProblem) – The optimization problem to be solved.

Variables:

  • problem (OptimizationProblem) – The optimization problem to be solved.

  • _x (list) – List to store the values of the variables during optimization.

Methods:

  • optimize (maxiter=1000, disp=True, tol=1e-3) – Optimize the problem using the specified parameters.

  • undo() – Undo the last optimization step.

  • _fun (x) – Internal function to evaluate the objective function.

optimize(method: str | None = None, maxiter: int = 1000, disp: bool = True, tol: float = 0.001, callback: Callable | None = None, plot: bool = False) → optimize.OptimizeResult[source]#

Optimize the problem using the specified parameters.

Parameters:

  • method (str, optional) – The optimization method to use. Default is chosen to be one of BFGS, L-BFGS-B, SLSQP, depending on whether contraints or bounds given. Follows scipy.optimize.minimize method.

  • maxiter (int, optional) – Maximum number of iterations. Default is 1000.

  • disp (bool, optional) – Whether to display optimization information. Default is True.

  • tol (float, optional) – Tolerance for convergence. Default is 1e-3.

  • callback (callable) – A callable called after each iteration.

  • plot – If True, update live plots during optimization.

Returns:

The optimization result.

Return type:

result (OptimizeResult)

undo()[source]#

Undo the last optimization step.