def __init__(self, *args, **kw):
self.goal = 'min'
self.bins = {}
#self.objective = ''
MatrixProblem.__init__(self, *args, **kw)
self.__init_kwargs = kw
self._init = True
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
self.n = self.C.shape[1]
# if 'damp' not in kwargs.keys(): kwargs['damp'] = None
# if 'X' not in kwargs.keys(): kwargs['X'] = nan*ones(self.n)
if self.x0 is None: self.x0 = zeros(self.n)
def __init__(self, *args, **kwargs):
self.goal = 'minimum'
MatrixProblem.__init__(self, *args, **kwargs)
if len(args) > 1 and not hasattr(args[0], 'is_oovar'):
self.err(
'No more than 1 argument is allowed for classic style LP constructor'
)
def __init__(self, *args, **kwargs):
if len(args) > 2: self.err('incorrect args number for LLAVP constructor, must be 0..2 + (optionaly) some kwargs')
if len(args) > 0: kwargs['C'] = args[0]
if len(args) > 1: kwargs['d'] = args[1]
MatrixProblem.__init__(self)
llavp_init(self, kwargs)
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
self.n = self.C.shape[1]
# if 'damp' not in kwargs.keys(): kwargs['damp'] = None
# if 'X' not in kwargs.keys(): kwargs['X'] = nan*ones(self.n)
if self.x0 is None: self.x0 = zeros(self.n)
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if self._isFDmodel():
self.x0 = self.C
return
self.f = asfarray(self.f)
self.n = self.f.size # for p.n to be available immediately after assigning prob
if self.x0 is None: self.x0 = zeros(self.n)
def __init__(self, *args, **kwargs):
self.probType = 'SDP'
self.S = {}
self.d = {}
MatrixProblem.__init__(self, *args, **kwargs)
self.f = asfarray(self.f)
self.n = self.f.size
if self.x0 is None: self.x0 = zeros(self.n)
def __init__(self, *args, **kwargs):
if len(args) > 2:
self.err(
'incorrect args number for LLAVP constructor, must be 0..2 + (optionaly) some kwargs'
)
if len(args) > 0: kwargs['C'] = args[0]
if len(args) > 1: kwargs['d'] = args[1]
MatrixProblem.__init__(self)
llavp_init(self, kwargs)
def __init__(self, *args, **kwargs):
self.QC = []
MatrixProblem.__init__(self, *args, **kwargs)
if self._isFDmodel():
if len(args) > 1:
self.x0 = args[1]
self.f = args[0]
else:
if len(args) > 1 or 'f' in kwargs.keys():
self.f = ravel(self.f)
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if len(args) > 1 or 'f' in kwargs.keys():
self.f = ravel(self.f)
self.n = self.f.size
if len(args) > 0 or 'H' in kwargs.keys():
# TODO: handle sparse cvxopt matrix H unchanges
# if not ('cvxopt' in str(type(H)) and 'cvxopt' in p.solver):
if not isspmatrix(self.H):
self.H = asfarray(self.H, float) # TODO: handle the case in runProbSolver()
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if len(args) > 1 or 'f' in kwargs.keys():
self.f = ravel(self.f)
self.n = self.f.size
if len(args) > 0 or 'H' in kwargs.keys():
# TODO: handle sparse cvxopt matrix H unchanges
# if not ('cvxopt' in str(type(H)) and 'cvxopt' in p.solver):
if not isspmatrix(self.H):
self.H = asfarray(
self.H, float) # TODO: handle the case in runProbSolver()
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if self.goal == 'all':
Name, name = 'all eigenvectors and eigenvalues', 'all'
if not isinstance(self.C[0], dict):
self.N = self.C.shape[0]
else:
assert type(self.goal) in (dict, tuple, list) and len(self.goal) == 1, \
'EIG goal argument should be "all" or Python dict {goal_name: number_of_required_eigenvalues}'
if type(self.goal) == dict:
goal_name, N = list(self.goal.items())[0]
else:
goal_name, N = self.goal
self.N = N
name = ''.join(goal_name.lower().split())
if name in ('lm', 'largestmagnitude'):
Name, name = 'largest magnitude', 'le'
elif name in ('sm', 'smallestmagnitude'):
Name, name = 'smallest magnitude', 'sm'
elif name in ('lr', 'largestrealpart'):
Name, name = 'largest real part', 'lr'
elif name in ('sr', 'smallestrealpart'):
Name, name = 'smallest real part', 'sr'
elif name in ('li', 'largestimaginarypart'):
Name, name = 'largest imaginary part', 'li'
elif name in ('si', 'smallestimaginarypart'):
Name, name = 'smallest imaginary part', 'si'
elif name in ('la', 'largestamplitude'):
Name, name = 'largestamplitude', 'la'
elif name in ('sa', 'smallestamplitude'):
Name, name = 'smallest amplitude', 'sa'
elif name in ('be', 'bothendsofthespectrum'):
Name, name = 'both ends of the spectrum', 'be'
self.goal = Name
self._goal = name
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if self.goal == 'all':
Name, name = 'all eigenvectors and eigenvalues', 'all'
if not isinstance(self.C[0], dict):
self.N = self.C.shape[0]
else:
assert type(self.goal) in (dict, tuple, list) and len(self.goal) == 1, \
'EIG goal argument should be "all" or Python dict {goal_name: number_of_required_eigenvalues}'
if type(self.goal) == dict:
goal_name, N = list(self.goal.items())[0]
else:
goal_name, N = self.goal
self.N = N
name = ''.join(goal_name.lower().split())
if name in ('lm', 'largestmagnitude'):
Name, name = 'largest magnitude', 'le'
elif name in ('sm', 'smallestmagnitude'):
Name, name = 'smallest magnitude', 'sm'
elif name in ('lr', 'largestrealpart'):
Name, name = 'largest real part', 'lr'
elif name in ('sr', 'smallestrealpart'):
Name, name = 'smallest real part', 'sr'
elif name in ('li', 'largestimaginarypart'):
Name, name = 'largest imaginary part', 'li'
elif name in ('si', 'smallestimaginarypart'):
Name, name = 'smallest imaginary part', 'si'
elif name in ('la', 'largestamplitude'):
Name, name = 'largestamplitude', 'la'
elif name in ('sa', 'smallestamplitude'):
Name, name = 'smallest amplitude', 'sa'
elif name in ('be', 'bothendsofthespectrum'):
Name, name = 'both ends of the spectrum', 'be'
self.goal = Name
self._goal = name
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if 'damp' not in kwargs.keys(): self.damp = None
if 'f' not in kwargs.keys(): self.f = None
if not self._isFDmodel():
if len(args)>0:
self.n = args[0].shape[1]
else:
self.n = kwargs['C'].shape[1]
#self.lb = -inf * ones(self.n)
#self.ub = inf * ones(self.n)
if not hasattr(self, 'lb'): self.lb = -inf * ones(self.n)
if not hasattr(self, 'ub'): self.ub = inf * ones(self.n)
if self.x0 is None: self.x0 = zeros(self.n)
else: # is FD model
if type(self.C) not in (set, tuple, list):
if 'is_oovar' not in dir(self.C):
s = '''
Icorrect data type for LLSP constructor,
first argument should be numpy ndarray,
scipy sparse matrix, FuncDesigner oofun or list of oofuns'''
self.err(s)
self.C = [self.C]
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
if 'damp' not in kwargs.keys(): self.damp = None
if 'f' not in kwargs.keys(): self.f = None
if not self._isFDmodel():
if len(args) > 0:
self.n = args[0].shape[1]
else:
self.n = kwargs['C'].shape[1]
#self.lb = -inf * ones(self.n)
#self.ub = inf * ones(self.n)
if not hasattr(self, 'lb'): self.lb = -inf * ones(self.n)
if not hasattr(self, 'ub'): self.ub = inf * ones(self.n)
if self.x0 is None: self.x0 = zeros(self.n)
else: # is FD model
if type(self.C) not in (set, tuple, list):
if 'is_oovar' not in dir(self.C):
s = '''
Icorrect data type for LLSP constructor,
first argument should be numpy ndarray,
scipy sparse matrix, FuncDesigner oofun or list of oofuns'''
self.err(s)
self.C = [self.C]
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
def __init__(self, *args, **kw):
self.goal = 'max'
self.objective = 'weight'
MatrixProblem.__init__(self, *args, **kw)
self.__init_kwargs = kw
self._init = True
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
self.f = asfarray(self.f)
self.n = self.f.size # for p.n to be available immediately after assigning prob
if self.x0 is None: self.x0 = zeros(self.n)
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
self.x0 = zeros(2*len(self.q))
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
self.x0 = zeros(2 * len(self.q))
def __init__(self, *args, **kw):
MatrixProblem.__init__(self, *args, **kw)
self.__init_kwargs = kw
self._init = True
def __init__(self, *args, **kwargs):
self.goal = 'minimum'
MatrixProblem.__init__(self, *args, **kwargs)
if len(args) > 1 and not hasattr(args[0], 'is_oovar'):
self.err('No more than 1 argument is allowed for classic style LP constructor')
def __init__(self, *args, **kw):
self.goal = 'max'
self.objective = 'weight'
MatrixProblem.__init__(self, *args, **kw)
self.__init_kwargs = kw
self._init = True
def __init__(self, *args, **kw):
MatrixProblem.__init__(self, *args, **kw)
self.__init_kwargs = kw
self._init = True
