Cite Us

SDPEN - Sequential Penalty Derivative-free method for Nonlinear constrained optimization

SDPEN is a derivative-free algorithm for local general constrained optimization problems. The algorithm solves the original nonlinear constrained optimization problem by a sequence of approximate minimizations of a merit function where penalization of constraint violation is progressively increased. At each sequence, a line-search based method is used with convergence to stationary points enforced using a suitable combination of the penalty parameter updating and different sampling strategies. [Liuzzi2010] [LICENSE]

class pySDPEN.SDPEN(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

SDPEN Optimizer Class - Inherited from Optimizer Abstract Class

SDPEN Optimizer Class Initialization

Keyword arguments:

  • pll_type -> STR: Parallel Implementation (None, ‘POA’-Parallel Objective Analysis), Default = None

Documentation last updated: August. 09, 2012 - Ruben E. Perez

__solve__(opt_problem={}, store_sol=True, disp_opts=False, store_hst=False, hot_start=False, *args, **kwargs)

Run Optimizer (Optimize Routine)

Keyword arguments:

  • opt_problem -> INST: Optimization instance
  • store_sol -> BOOL: Store solution in Optimization class flag, Default = True
  • disp_opts -> BOOL: Flag to display options in solution text, Default = False
  • store_hst -> BOOL/STR: Flag/filename to store optimization history, Default = False
  • hot_start -> BOOL/STR: Flag/filename to read optimization history, Default = False

Additional arguments and keyword arguments are passed to the objective function call.

Documentation last updated: August. 09, 2012 - Ruben E. Perez

Optimizer Options

Name Type Default Value Notes
alfa_stop float 1.0e-6 Convergence Accurancy
nf_max int 5000 Maximum Number of Function Evaluations
iprint int 0 Print Flag (<0-None, 0-Final, 1,2-Iteration)
iout int 6 Output Unit Number
ifile str ‘SDPEN.out’ Output File Name