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# 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:

        0. (default) There will be no printing

        1. 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

        2. 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.

        3. 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