def __UpdateAllVariables(self, NId, Objective):
if NId in self.node_objectives:
# First, remove the Variables from the old Objective.
old_obj = self.node_objectives[NId]
self.all_variables = self.all_variables - set(old_obj.variables())
# Check that the Variables of the new Objective are not currently
# in other Objectives.
new_variables = set(Objective.variables())
if __builtin__.len(self.all_variables.intersection(new_variables)) != 0:
raise Exception('Objective at NId %d shares a variable.' % NId)
self.all_variables = self.all_variables | new_variables
# Helper method to get CVXPY Variables out of a CVXPY Objective
python类len()的实例源码
def __UpdateAllVariables(self, NId, Objective):
if NId in self.node_objectives:
# First, remove the Variables from the old Objective.
old_obj = self.node_objectives[NId]
self.all_variables = self.all_variables - set(old_obj.variables())
# Check that the Variables of the new Objective are not currently
# in other Objectives.
new_variables = set(Objective.variables())
if __builtin__.len(self.all_variables.intersection(new_variables)) != 0:
raise Exception('Objective at NId %d shares a variable.' % NId)
self.all_variables = self.all_variables | new_variables
# Helper method to get CVXPY Variables out of a CVXPY Objective
def __UpdateAllVariables(self, NId, Objective):
if NId in self.node_objectives:
# First, remove the Variables from the old Objective.
old_obj = self.node_objectives[NId]
self.all_variables = self.all_variables - set(old_obj.variables())
# Check that the Variables of the new Objective are not currently
# in other Objectives.
new_variables = set(Objective.variables())
if __builtin__.len(self.all_variables.intersection(new_variables)) != 0:
raise Exception('Objective at NId %d shares a variable.' % NId)
self.all_variables = self.all_variables | new_variables
# Helper method to get CVXPY Variables out of a CVXPY Objective
def __UpdateAllVariables(self, NId, Objective):
if NId in self.node_objectives:
# First, remove the Variables from the old Objective.
old_obj = self.node_objectives[NId]
self.all_variables = self.all_variables - set(old_obj.variables())
# Check that the Variables of the new Objective are not currently
# in other Objectives.
new_variables = set(Objective.variables())
if __builtin__.len(self.all_variables.intersection(new_variables)) != 0:
raise Exception('Objective at NId %d shares a variable.' % NId)
self.all_variables = self.all_variables | new_variables
# Helper method to get CVXPY Variables out of a CVXPY Objective
def _parse(self, txt, linenr=0, directcall=False):
pos = 0
for match in _ALL_DIRECTIVES_REGEXP.finditer(txt):
start, end = match.span()
if start > pos:
endlinenr = linenr + txt.count('\n', pos, start)
self._process_text(txt[pos:start], (linenr, endlinenr))
linenr = endlinenr
endlinenr = linenr + txt.count('\n', start, end)
span = (linenr, endlinenr)
ldirtype, ldir, idirtype, idir = match.groups()
if directcall and (idirtype is None or idirtype != '$'):
msg = 'only inline eval directives allowed in direct calls'
raise FyppFatalError(msg, self._curfile, span)
elif idirtype is not None:
if idir is None:
msg = 'missing inline directive content'
raise FyppFatalError(msg, self._curfile, span)
dirtype = idirtype
content = idir
elif ldirtype is not None:
if ldir is None:
msg = 'missing line directive content'
raise FyppFatalError(msg, self._curfile, span)
dirtype = ldirtype
content = _CONTLINE_REGEXP.sub('', ldir)
else:
# Comment directive
dirtype = None
if dirtype == '$':
self.handle_eval(span, content)
elif dirtype == '#':
self._process_control_dir(content, span)
elif dirtype == '@':
self._process_direct_call(content, span)
else:
self.handle_comment(span)
pos = end
linenr = endlinenr
if pos < len(txt):
endlinenr = linenr + txt.count('\n', pos)
self._process_text(txt[pos:], (linenr, endlinenr))
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve(solver=SCS, eps = 1e-7, max_iters = 25000, verbose=True)
except SolverError:
print "Using SCS"
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
print "NOT SOLVED"
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False,
UseClustering = False, ClusterSize = 1000 ):
global m_func
m_func = M
# Use ADMM if the appropriate parameter is specified and if there
# are edges in the graph.
#if __builtin__.len(SuperNodes) > 0:
if UseClustering and ClusterSize > 0:
SuperNodes = self.__ClusterGraph(ClusterSize)
self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
EpsAbs, EpsRel, Verbose)
return
if UseADMM and self.GetEdges() != 0:
self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
Verbose)
return
if Verbose:
print 'Serial ADMM'
objective = 0
constraints = []
# Add all node objectives and constraints
for ni in self.Nodes():
nid = ni.GetId()
objective += self.node_objectives[nid]
constraints += self.node_constraints[nid]
# Add all edge objectives and constraints
for ei in self.Edges():
etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
objective += self.edge_objectives[etup]
constraints += self.edge_constraints[etup]
# Solve CVXPY Problem
objective = m_func(objective)
problem = Problem(objective, constraints)
try:
problem.solve()
except SolverError:
problem.solve(solver=SCS)
if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
problem.solve(solver=SCS)
# Set TGraphVX status and value to match CVXPY
self.status = problem.status
self.value = problem.value
# Insert into hash to support ADMM structures and GetNodeValue()
for ni in self.Nodes():
nid = ni.GetId()
variables = self.node_variables[nid]
value = None
for (varID, varName, var, offset) in variables:
if var.size[0] == 1:
val = numpy.array([var.value])
else:
val = numpy.array(var.value).reshape(-1,)
if value is None:
value = val
else:
value = numpy.concatenate((value, val))
self.node_values[nid] = value
