SLSQP - Sequential Least Squares Programming

SLSQP optimizer is a sequential least squares programming algorithm which uses the Han–Powell quasi–Newton method with a BFGS update of the B–matrix and an L1–test function in the step–length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver. [Kraft1988] [LICENSE]

class pySLSQP.SLSQP(pll_type=None, *args, **kwargs)

Bases: pyOpt.pyOpt_optimizer.Optimizer

SLSQP Optimizer Class - Inherited from Optimizer Abstract Class

SLSQP 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
ACC	float	1e-6	Convergence Accurancy
MAXIT	int	50	Maximum Iterations
IPRINT	int	1	Output Level (neg-None, 0-Screen, 1-File)
IOUT	int	6	Output Unit Number
IFILE	str	‘SLSQP.out’	Output File Name