CONMIN - CONstrained function MINimization

This optimizer implements the method of feasible directions. CONMIN solves the nonlinear programming problem by moving from one feasible point to an improved one by choosing at each iteration a feasible direction and step size that improves the objective function. [Vanderplaats1973] [LICENSE]

class pyCONMIN.CONMIN(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

CONMIN Optimizer Class - Inherited from Optimizer Abstract Class

CONMIN Optimizer Class Initialization

Keyword arguments:

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

Documentation last updated: Feb. 16, 2010 - Peter W. Jansen

__solve__(opt_problem={}, sens_type='FD', store_sol=True, store_hst=False, hot_start=False, disp_opts=False, sens_mode='', sens_step={}, *args, **kwargs)

Run Optimizer (Optimize Routine)

Keyword arguments:

• opt_problem -> INST: Optimization instance
• sens_type -> STR/FUNC: Gradient type, Default = ‘FD’
• 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
• sens_mode -> STR: Flag for parallel gradient calculation, Default = ‘’
• sens_step -> FLOAT: Sensitivity setp size, Default = {} [corresponds to 1e-6 (FD), 1e-20(CS)]

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

Documentation last updated: February. 2, 2011 - Ruben E. Perez

Optimizer Options

Name	Type	Default Value	Notes
ITMAX	int	1e4	Maximum Number of Iterations
DELFUN	float	1e-6	Objective Relative Tolerance
DABFUN	float	1e-6	Objective Absolute Tolerance
ITRM	int	2
NFEASCT	int	20
IPRINT	int	2	Print Control (0 - None, 1 - Final, 2,3,4,5 - Debug)
IOUT	int	6	Output Unit Number
IFILE	str	‘CONMIN.out’	Output File Name