NLPQL - Non-Linear Programming by Quadratic Lagrangian

NLPQL is a sequential quadratic programming (SQP) method which solves problems with smooth continuously differentiable objective function and constraints. The algorithm uses a quadratic approximation of the Lagrangian function and a linearization of the constraints. To generate a search direction a quadratic subproblem is formulated and solved. The line search can be performed with respect to two alternative merit functions, and the Hessian approximation is updated by a modified BFGS formula. [Schitt1986] [LICENSE]

class pyNLPQL.NLPQL(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

NLPQL Optimizer Class - Inherited from Optimizer Abstract Class

NLPQL 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, disp_opts=False, store_hst=False, hot_start=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 - Peter W. Jansen

Optimizer Options

Name	Type	Default Value	Notes
Accurancy	float	1e-6	Convergence Accurancy
ScaleBound	float	1e30
maxFun	int	20	Maximum Number of Function Calls During Line Search
maxIt	int	500	Maximum Number of Iterations
iPrint	int	2	Output Level (0-None, 1-Final, 2-Major, 3-Major/Minor, 4-Full)
mode	int	0	NLPQL Mode (0 - Normal Execution, 1 to 18 - See Manual)
iout	int	6	Output Unit Number|
lmerit	bool	True	Merit Function (True: L2 Augmented Penalty, False: L1 Penalty)
lql	bool	False	QP Solver (True - Quasi-Newton, False - Cholesky)
iFile	str	‘NLPQL.out’	Output File Name