parameters: (self, options)
docstring: Initiates the options.
          
          Can also be useful to change parameters after class initiation.
  
          Parameters
          ----------
          options : dict
  
          Returns
          -------
          None
docstring_summary: Initiates the options.
          
          Can also be useful to change parameters after class initiation.
docstring_tokens: Initiates the options . Can also be useful to change parameters after class initiation .
function: def init_options(self, options):
          """
          Initiates the options.
          
          Can also be useful to change parameters after class initiation.
  
          Parameters
          ----------
          options : dict
  
          Returns
          -------
          None
  
          """
          self.minimizer_kwargs['options'].update(options)
          # Default settings:
          self.minimize_every_iter = options.get('minimize_every_iter', False)
  
          # Algorithm limits
          # Maximum number of iterations to perform.
          self.maxiter = options.get('maxiter', None)
          # Maximum number of function evaluations in the feasible domain
          self.maxfev = options.get('maxfev', None)
          # Maximum number of sampling evaluations (includes searching in
          # infeasible points
          self.maxev = options.get('maxev', None)
          # Maximum processing runtime allowed
          self.init = time.time()
          self.maxtime = options.get('maxtime', None)
          if 'f_min' in options:
              # Specify the minimum objective function value, if it is known.
              self.f_min_true = options['f_min']
              self.f_tol = options.get('f_tol', 1e-4)
          else:
              self.f_min_true = None
  
          self.minhgrd = options.get('minhgrd', None)
  
          # Objective function knowledge
          self.symmetry = 'symmetry' in options
  
          # Algorithm functionality
          # Only evaluate a few of the best candiates
          self.local_iter = options.get('local_iter', False)
  
          self.infty_cons_sampl = options.get('infty_constraints', True)
  
          # Feedback
          self.disp = options.get('disp', False)
function_tokens: def init_options ( self , options ) : self . minimizer_kwargs [ 'options' ] . update ( options ) # Default settings: self . minimize_every_iter = options . get ( 'minimize_every_iter' , False ) # Algorithm limits # Maximum number of iterations to perform. self . maxiter = options . get ( 'maxiter' , None ) # Maximum number of function evaluations in the feasible domain self . maxfev = options . get ( 'maxfev' , None ) # Maximum number of sampling evaluations (includes searching in # infeasible points self . maxev = options . get ( 'maxev' , None ) # Maximum processing runtime allowed self . init = time . time ( ) self . maxtime = options . get ( 'maxtime' , None ) if 'f_min' in options : # Specify the minimum objective function value, if it is known. self . f_min_true = options [ 'f_min' ] self . f_tol = options . get ( 'f_tol' , 1e-4 ) else : self . f_min_true = None self . minhgrd = options . get ( 'minhgrd' , None ) # Objective function knowledge self . symmetry = 'symmetry' in options # Algorithm functionality # Only evaluate a few of the best candiates self . local_iter = options . get ( 'local_iter' , False ) self . infty_cons_sampl = options . get ( 'infty_constraints' , True ) # Feedback self . disp = options . get ( 'disp' , False )