parameters: (fun, x0, args=(), jac=None, hess=None,
                       **trust_region_options)
return_statement: return _minimize_trust_region(fun, x0, args=args, jac=jac, hess=hess,
                                    subproblem=DoglegSubproblem,
                                    **trust_region_options)
docstring: Minimization of scalar function of one or more variables using
      the dog-leg trust-region algorithm.
  
      Options
      -------
      initial_trust_radius : float
          Initial trust-region radius.
      max_trust_radius : float
          Maximum value of the trust-region radius. No steps that are longer
          than this value will be proposed.
      eta : float
          Trust region related acceptance stringency for proposed steps.
      gtol : float
          Gradient norm must be less than `gtol` before successful
          termination.
docstring_summary: Minimization of scalar function of one or more variables using
      the dog-leg trust-region algorithm.
docstring_tokens: Minimization of scalar function of one or more variables using the dog - leg trust - region algorithm .
function: def _minimize_dogleg(fun, x0, args=(), jac=None, hess=None,
                       **trust_region_options):
      """
      Minimization of scalar function of one or more variables using
      the dog-leg trust-region algorithm.
  
      Options
      -------
      initial_trust_radius : float
          Initial trust-region radius.
      max_trust_radius : float
          Maximum value of the trust-region radius. No steps that are longer
          than this value will be proposed.
      eta : float
          Trust region related acceptance stringency for proposed steps.
      gtol : float
          Gradient norm must be less than `gtol` before successful
          termination.
  
      """
      if jac is None:
          raise ValueError('Jacobian is required for dogleg minimization')
      if hess is None:
          raise ValueError('Hessian is required for dogleg minimization')
      return _minimize_trust_region(fun, x0, args=args, jac=jac, hess=hess,
                                    subproblem=DoglegSubproblem,
                                    **trust_region_options)
function_tokens: def _minimize_dogleg ( fun , x0 , args = ( ) , jac = None , hess = None , * * trust_region_options ) : if jac is None : raise ValueError ( 'Jacobian is required for dogleg minimization' ) if hess is None : raise ValueError ( 'Hessian is required for dogleg minimization' ) return _minimize_trust_region ( fun , x0 , args = args , jac = jac , hess = hess , subproblem = DoglegSubproblem , * * trust_region_options )