MMFD - Modified Method of Feasible Directions

This optimizer is an extension of the method of feasible directions CONMIN - CONstrained function MINimization algorithm. MMFD utilizes the direction-finding sub-problem from the Method of Feasible Directions to find a search direction but does not require the addition of a large number of slack variables associated with inequality constraints. [Vanderplaats1983] [LICENSE]

class pyMMFD.MMFD(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

MMFD Optimizer Class - Inherited from Optimizer Abstract Class

MMFD 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
IOPT	int	0	Feasible Directions Approach (0 - MMFD, 1 - MFD)
IONED	int	0	One-Dimensional Search Method (0,1,2,3)
CT	float	-3e-2	Constraint Tolerance
DABOBJ	float	1e-3	Objective Absolute Tolerance (DABOBJ*abs(f(x)))
CTMIN	float	4e-3	Active Constraint Tolerance
DABOBJ	float	1e-3	Objective Absolute Tolerance (DABOBJ*abs(f(x)))
DELOBJ	float	1e-3	Objective Relative Tolerance
THETAZ	float	1e-1	Push-Off Factor
PMLT	float	1e1	Penalty multiplier for equality constraints
ITMAX	int	4e2	Maximum Number of Iterations
ITRMOP	int	3	Consecutive Iterations Iterations for Convergence
IPRINT	int	2	Print Control (0 - None, 1 - Final, 2 - Iters)
IFILE	str	‘MMFD.out’	Output File Name