minimize(method=’COBYLA’)#
- scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)
- Minimize a scalar function of one or more variables using the Constrained Optimization BY Linear Approximation (COBYLA) algorithm. This method uses the pure-python implementation of the algorithm from PRIMA. - See also - For documentation for the rest of the parameters, see - scipy.optimize.minimize- Options:
- ——-
- rhobegfloat
- Reasonable initial changes to the variables. 
- tolfloat
- Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region. 
- dispint
- Controls the frequency of output:
- (default) There will be no printing 
- A message will be printed to the screen at the end of iteration, showing the best vector of variables found and its objective function value 
- in addition to 1, each new value of RHO is printed to the screen, with the best vector of variables so far and its objective function value. 
- in addition to 2, each function evaluation with its variables will be printed to the screen. 
 
 
- maxiterint
- Maximum number of function evaluations. 
- catolfloat
- Tolerance (absolute) for constraint violations 
- f_targetfloat
- Stop if the objective function is less than f_target. - Changed in version 1.16.0: The original Powell implementation was replaced by a pure Python version from the PRIMA package, with bug fixes and improvements being made. 
 
 - References - Zhang Z. (2023), “PRIMA: Reference Implementation for Powell’s Methods with Modernization and Amelioration”, https://www.libprima.net, DOI:10.5281/zenodo.8052654