`scipy.optimize._cobyqa_py:_minimize_cobyqa`

source: /scipy/optimize/_cobyqa_py.py :10

Signature

   def     _minimize_cobyqa (    fun    ,    x0    ,    args    =  ()   ,    bounds    =  None   ,    constraints    =  ()   ,    callback    =  None   ,    disp    =  False   ,    maxfev    =  None   ,    maxiter    =  None   ,    f_target    =  -inf   ,    feasibility_tol    =  1e-08   ,    initial_tr_radius    =  1.0   ,    final_tr_radius    =  1e-06   ,    scale    =  False   ,   ** unknown_options      )

Summary

Minimize a scalar function of one or more variables using the Constrained Optimization BY Quadratic Approximations (COBYQA) algorithm ^[1].

Extended Summary

Parameters

disp : bool: Set to True to print information about the optimization procedure. Default is False.
maxfev : int: Maximum number of function evaluations. Default is 500 * n, where n is the number of variables.
maxiter : int: Maximum number of iterations. Default is 1000 * n, where n is the number of variables.
f_target : float: Target value for the objective function. The optimization procedure is terminated when the objective function value of a feasible point (see feasibility_tol below) is less than or equal to this target. Default is -numpy.inf.
feasibility_tol : float: Absolute tolerance for the constraint violation. Default is 1e-8.
initial_tr_radius : float: Initial trust-region radius. Typically, this value should be in the order of one tenth of the greatest expected change to the variables. Default is 1.0.
final_tr_radius : float: Final trust-region radius. It should indicate the accuracy required in the final values of the variables. If provided, this option overrides the value of tol in the minimize function. Default is 1e-6.
scale : bool: Set to True to scale the variables according to the bounds. If True and if all the lower and upper bounds are finite, the variables are scaled to be within the range $[- 1, 1]$ . If any of the lower or upper bounds is infinite, the variables are not scaled. Default is False.

Aliases

scipy.optimize._cobyqa_py._minimize_cobyqa