Python Optimizer.__init__方法代码示例

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
    def __init__(self, pll_type=None, *args, **kwargs):

        """
		ALHSO 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
		"""

        #
        if pll_type == None:
            self.poa = False
        elif pll_type.upper() == "POA":
            self.poa = True
        else:
            raise ValueError("pll_type must be either None or 'POA'")
            # end

            #
        name = "ALHSO"
        category = "Global Optimizer"
        def_opts = {
            "hms": [int, 5],  # Memory Size [1,50]
            "hmcr": [float, 0.95],  # Probability rate of choosing from memory [0.7,0.99]
            "par": [float, 0.65],  # Pitch adjustment rate [0.1,0.99]
            "dbw": [int, 2000],  # Variable Bandwidth Quantization
            "maxoutiter": [int, 2e3],  # Maximum Number of Outer Loop Iterations (Major Iterations)
            "maxinniter": [int, 2e2],  # Maximum Number of Inner Loop Iterations (Minor Iterations)
            "stopcriteria": [int, 1],  # Stopping Criteria Flag
            "stopiters": [
                int,
                10,
            ],  # Consecutively Number of Outer Iterations for which the Stopping Criteria must be Satisfied
            "etol": [float, 1e-6],  # Absolute Tolerance for Equality constraints
            "itol": [float, 1e-6],  # Absolute Tolerance for Inequality constraints
            "atol": [float, 1e-6],  # Absolute Tolerance for Objective Function
            "rtol": [float, 1e-6],  # Relative Tolerance for Objective Function
            "prtoutiter": [int, 0],  # Number of Iterations Before Print Outer Loop Information
            "prtinniter": [int, 0],  # Number of Iterations Before Print Inner Loop Information
            "xinit": [int, 0],  # Initial Position Flag (0 - no position, 1 - position given)
            "rinit": [float, 1.0],  # Initial Penalty Factor
            "fileout": [int, 1],  # Flag to Turn On Output to filename
            "filename": [
                str,
                "ALHSO.out",
            ],  # We could probably remove fileout flag if filename or fileinstance is given
            "seed": [float, 0],  # Random Number Seed (0 - Auto-Seed based on time clock)
            "scaling": [int, 1],  # Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1,1])
        }
        informs = {}
        Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		FILTERSD 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'FILTERSD'
		category = 'Local Optimizer'
		def_opts = {
		'rho':[float,100.0],			# initial trust region radius
		'htol':[float,1e-6],			# tolerance allowed in sum h of constraint feasibilities
		'rgtol':[float,1e-5],			# tolerance allowed in reduced gradient l2 norm
		'maxit':[int,1000],				# maximum number of major iterations allowed
		'maxgr':[int,1e5],				# upper limit on the number of gradient calls
		'ubd':[float,1e5],				# upper bound on the allowed constraint violation
		'dchk':[int,0],					# derivative check flag (0 - no check, 1 - check)
		'dtol':[float,1e-8],			# derivative check tolerance
		'iprint':[int,1],				# verbosity of printing (0 - none, 1 - Iter, 2 - Debug)
		'iout':[int,6],     			# Output Unit Number
		'ifile':[str,'FILTERSD.out'],	# Output File Name
		}
		informs = {
		-1 : 'ws not large enough',
		-2 : 'lws not large enough',
		-3 : 'inconsistency during derivative check',
		0 : 'successful run',
		1 : 'unbounded NLP (f <= fmin at an htol-feasible point)',
		2 : 'bounds on x are inconsistent',
		3 : 'local minimum of feasibility problem and h > htol, (nonlinear constraints are locally inconsistent)',
		4 : 'initial point x has h > ubd (reset ubd or x and re-enter)',
		5 : 'maxit major iterations have been carried out',
		6 : 'termination with rho <= htol',
		7 : 'not enough workspace in ws or lws (see message)',
		8 : 'insufficient space for filter (increase mxf and re-enter)',
		9 : 'unexpected fail in LCP solver',
		10 : 'unexpected fail in LCP solver',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		PSQP 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'PSQP'
		category = 'Local Optimizer'
		def_opts = {
		'XMAX':[float,1e16],  		# Maximum Stepsize
		'TOLX':[float,1e-16],  		# Variable Change Tolerance
		'TOLC':[float,1e-6],  		# Constraint Violation Tolerance
		'TOLG':[float,1e-6],  		# Lagrangian Gradient Tolerance
		'RPF':[float,1e-4],  		# Penalty Coefficient
		'MIT':[int,1000],  			# Maximum Number of Iterations
		'MFV':[int,2000],  			# Maximum Number of Function Evaluations
		'MET':[int,2],  			# Variable Metric Update (1 - BFGS, 2 - Hoshino)
		'MEC':[int,2],  			# Negative Curvature Correction (1 - None, 2 - Powell's Correction)	
		'IPRINT':[int,2],			# Output Level (0 - None, 1 - Final, 2 - Iter)
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'PSQP.out'],	# Output File Name
		}
		informs = {
		1 : 'Change in design variable was less than or equal to tolerance',
		2 : 'Change in objective function was less than or equal to tolerance',
		3 : 'Objective function less than or equal to tolerance',
		4 : 'Maximum constraint value is less than or equal to tolerance',
		11 : 'Maximum number of iterations exceeded',
		12 : 'Maximum number of function evaluations exceeded',
		13 : 'Maximum number of gradient evaluations exceeded',
		-6 : 'Termination criterion not satisfied, but obtained point is acceptable',
		#<0 : 'Method failed',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		SOLVOPT 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		# 
		name = 'SOLVOPT'
		category = 'Local Optimizer'
		def_opts = {
		'xtol':[float,1e-4],			# Variables Tolerance
		'ftol':[float,1e-6],			# Objective Tolerance
		'maxit':[int,15000],			# Maximum Number of Iterations
		'iprint':[int,1],     			# Output Level (-1 -> None, 0 -> Final, N - each Nth iter)
		'gtol':[float,1e-8],  			# Constraints Tolerance
		'spcdil':[float,2.5], 			# Space Dilation 
		'iout':[int,6],     			# Output Unit Number
		'ifile':[str,'SOLVOPT.out'],	# Output File Name
		}
		informs = {
		1 : 'Normal termination.', 
		-2 : 'Improper space dimension.',
		-3 : 'Objective equals infinity.',
		-4 : 'Gradient equals zero or infinity.',
		-5 : 'Objective equals infinity.',
		-6 : 'Gradient equals zero or infinity.',
		-7 : 'Objective function is unbounded.',
		-8 : 'Gradient zero at the point, but stopping criteria are not fulfilled.',
		-9 : 'Iterations limit exceeded.',
		-11 : 'Premature stop is possible. Try to re-run the routine from the obtained point.',
		-12 : 'Result may not provide the optimum. The function apparently has many extremum points.',
		-13 : 'Result may be inaccurate in the coordinates. The function is flat at the optimum.',
		-14 : 'Result may be inaccurate in a function value. The function is extremely steep at the optimum.',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		FSQP 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'FSQP'
		category = 'Local Optimizer'
		def_opts = {
		'mode':[int,100],			# FSQP Mode (See Manual)
		'iprint':[int,2],			# Output Level (0 - None, 1 - Final, 2 - Major, 3 - Major Details)
		'miter':[int,500],			# Maximum Number of Iterations
		'bigbnd':[float,1e10],		# Plus Infinity Value
		'epstol':[float,1e-8],		# Convergence Tolerance
		'epseqn':[float,0],			# Equality Constraints Tolerance
		'iout':[int,6],     		# Output Unit Number
		'ifile':[str,'FSQP.out'],	# Output File Name
		}
		informs = {
		0 : 'Normal termination of execution',
		1 : 'User-provided initial guess is infeasible for linear constraints, unable to generate a point satisfying all these constraints',
		2 : 'User-provided initial guess is infeasible for nonlinear inequality constraints and linear constraints, unable to generate a point satisfying all these constraints',
		3 : 'The maximum number of iterations has been reached before a solution is obtained',
		4 : 'The line search fails to find a new iterate',
		5 : 'Failure of the QP solver in attempting to construct d0, a more robust QP solver may succeed',
		6 : 'Failure of the QP solver in attempting to construct d1, a more robust QP solver may succeed',
		7 : 'Input data are not consistent, check print out error messages',
		8 : 'Two consecutive iterates are numerically equivalent before a stopping criterion is satisfied',
		9 : 'One of the penalty parameters exceeded bigbnd, the algorithm is having trouble satisfying a nonlinear equality constraint',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'SLSQP'
		category = 'Local Optimizer'
		def_opts = {
		# SLSQP Options
		'ACC':[float,1e-6],			# Convergence Accurancy
		'MAXIT':[int,50], 			# Maximum Iterations
		'IPRINT':[int,1],			# Output Level (<0 - None, 0 - Screen, 1 - File)
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'SLSQP.out'],	# Output File Name
		}
		informs = {
		-1 : "Gradient evaluation required (g & a)",
		0 : "Optimization terminated successfully.",
		1 : "Function evaluation required (f & c)",
		2 : "More equality constraints than independent variables",
		3 : "More than 3*n iterations in LSQ subproblem",
		4 : "Inequality constraints incompatible",
		5 : "Singular matrix E in LSQ subproblem",
		6 : "Singular matrix C in LSQ subproblem",
		7 : "Rank-deficient equality constraint subproblem HFTI",
		8 : "Positive directional derivative for linesearch",
		9 : "Iteration limit exceeded",
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		ALGENCAN 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'ALGENCAN'
		category = 'Local Optimizer'
		def_opts = {
		# ALGENCAN Options
		'epsfeas':[float,1.0e-8],		# Feasibility Convergence Accurancy
		'epsopt':[float,1.0e-8],		# Optimality Convergence Accurancy
		'efacc':[float,1.0e-4],			# Feasibility Level for Newton-KKT Acceleration
		'eoacc':[float,1.0e-4],			# Optimality Level for Newton-KKT Acceleration
		'checkder':[bool,False],		# Check Derivatives Flag
		'iprint':[int,10],				# Print Flag (0 - None, )
		'ifile':[str,'ALGENCAN.out'],	# Output File Name
		'ncomp':[int,6],				# Print Precision
		}
		informs = {
		0 : "Solution was found.",
		1 : "Stationary or infeasible point was found.",
		2 : "penalty parameter is too large infeasibile or badly scaled problem",
		3 : "Maximum of iterations reached.",
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'MMFD'
		category = 'Local Optimizer'
		def_opts = {
		'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
		'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
		}
		informs = {
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		MMA 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		
		#
		name = 'MMA'
		category = 'Local Optimizer'
		def_opts = {
		# MMA Options
		'MAXIT':[int,1000],     	# Maximum Iterations
		'GEPS':[float,1e-6],    	# Dual Objective Gradient Tolerance
		'DABOBJ':[float,1e-6],  	# 
		'DELOBJ':[float,1e-6],  	# 
		'ITRM':[int,2],         	# 
		'IPRINT':[int,1],       	# Output Level (<0 - None, 0 - Screen, 1 - File)
		'IOUT':[int,6],         	# Output Unit Number
		'IFILE':[str,'MMA.out'],	# Output File Name
		}
		informs = {
		0 : 'The optimality conditions are satisfied.', 
		1 : 'The algorithm has been stopped after MAXIT iterations.',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		NSGA2 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'NSGA-II'
		category = 'Global Optimizer'
		def_opts = {
		'PopSize':[int,100],			# 
		'maxGen':[int,150],				# 
		'pCross_real':[float,0.6],		# 
		'pMut_real':[float,0.2],		# 
		'eta_c':[float,10],				# 
		'eta_m':[float,20],				# 
		'pCross_bin':[float,0],			# 
		'pMut_bin':[float,0],			# 
		'PrintOut':[int,1],				# Flag to Turn On Output to filename (0 - , 1 - , 2 - )
		'seed':[float,0],				# Random Number Seed (0 - Auto-Seed based on time clock)
		'xinit':[int,0],				# Use Initial Solution Flag (0 - random population, 1 - use given solution)
		}
		informs = {}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		COBYLA 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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'COBYLA'
		category = 'Local Optimizer'
		def_opts = {
		'RHOBEG':[float,0.5],		# Initial Variables Change
		'RHOEND':[float,1.0e-6],	# Convergence Accurancy
		'IPRINT':[int,2],			# Print Flag (0 - None, 1 - Final, 2,3 - Iteration)
		'MAXFUN':[int,3500],     	# Maximum Iterations
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'COBYLA.out'],	# Output File Name
		}
		informs = {
		0: 'Normal return',
		1: 'Max. number of function evaluations reach',
		2: 'Rounding errors are becoming damaging',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, *args, **kwargs):
		
		'''
		HSO Optimizer Class Initialization
		
		Documentation last updated:  October. 22, 2008 - Ruben E. Perez
		'''
		
		# 
		name = 'HSO'
		category = 'Global Optimizer'
		def_opts = {
		'hms':[int,10],				# Memory Size [4,10]
		'dbw':[float,0.01],			# 
		'hmcr':[float,0.96],		# 
		'par':[float,0.6],			# 
		'maxiter':[int,1e4],		# Maximum Number Iterations
		'printout':[int,0],			# Flag to Turn On Information Output
		'xinit':[int,0],			# Initial Position Flag (0 - no position, 1 - position given)
		'seed':[float,0],			# Random Number Seed (0 - Auto-Seed based on time clock)
		}
		informs = {}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'CONMIN'
		category = 'Local Optimizer'
		def_opts = {
		'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
		}
		informs = {
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		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
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'SDPEN'
		category = 'Local Optimizer'
		def_opts = {
		# SDPEN Options
		'alfa_stop':[float,1e-6],	# Convergence Tolerance
		'nf_max':[int,5000],		# Maximum Number of Function Evaluations
		'iprint':[int,0],			# Output Level (<0 - None, 0 - Final, 1 - Iters, 2 - Full)
		'iout':[int,6],     		# Output Unit Number
		'ifile':[str,'SDPEN.out'],	# Output File Name
		}
		informs = {
		1 : 'finished successfully',
		2 : 'maximum number of evaluations reached',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

# 需要导入模块: from pyOpt import Optimizer [as 别名]
# 或者: from pyOpt.Optimizer import __init__ [as 别名]
    def __init__(self, pll_type=None, *args, **kwargs):

        """
		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
		"""

        #
        if pll_type == None:
            self.poa = False
        elif pll_type.upper() == "POA":
            self.poa = True
        else:
            raise ValueError("pll_type must be either None or 'POA'")
            # end

            #
        name = "CONMIN"
        category = "Local Optimizer"
        def_opts = {
            "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
        }
        informs = {}
        Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)