def construct(self,model,controlNames,newObjective):
'''
Top Level Function
Build Complete Framework
'''
self.newObjective = newObjective
result = LinearModel()
result.addModel(model)
sObjective = self._scaleMap(model.getObjective(),self.objectiveCoeffecent)
sObjective[self.newObjective] = .01
result.setObjective(sObjective)
for (libName,libPrefix) in self.libdata:
result = self._constructRegulationObjecive(result, controlNames, libName, libPrefix, -1.0)
pass
result = self._primalDual(result, result.getObjective())
for (libName,libPrefix) in self.libdata:
result = self.constructControl(result,controlNames,libPrefix,libName)
if self.rGeneMap != None:
self.geneControl(result,libPrefix + "y__",controlNames,libName)
if self.simoLimit !=0:
result = self._controlValueLimit(result,controlNames,postfix="y__")
return result
def _primalDual(self,model,objective,prime=True,dual=True,equal=True):
'''
class PathwayTarget:
@type model: LinearModel
@rtype: LinearModel
Construct Core Linear Optimization Matrix with primal dual equality
row i and column j
Primal:
min Z = sum(j): [c(j)*v(j)]
ST.
sum(j) [S(ij)*v(j)] = 0 E i
l(j) < v(j) < u(j) E j
Hameltonian:
Max(lambda,mu,nu): Min(v): sum(j)c(j)*v(j) + sum(i)lambda(i)*S(ij)v(j) + mu(j)(l(j)-v(j)) + nu(j)(v(j)-u(j))
=> c'v + lambda*(Sv) + mu(l-v) + nu(v-u)
=> Max: mu*l - nu*u
ST
c + lambda*S + mu + nu = 0
Dual:
max Z_dual = l*mu - u*nu
ST.
S'*lambda - mu + nu = -c'
mu(j) > 0 E j
nu(j) > 0 E j
Equality condition:
Z = Z_dual
==> sum(j) [c(j)'*v(j)] = sum(j) [l(j)*mu(j) - u(j)*nu]
==> -c*v + mu*l - u*nu = 0
'''
pName = self.pName
dName = self.dName
result = LinearModel()
#Primal:
if prime:
#S*v
for (row, column, value) in model.getSparseMatrix():
result.addData(self.primeRName + row, pName + column, value)
#S*v <=> b
for row in model.getRowLimits().keys():
result.addRowLimit(self.primeRName + row, model.getRowLimit(row))
#u<=v<=l
for column in model.getColumnNames():
result.addColumnLimit(pName + column, model.getColumnLimit(column))
#Dual:
if dual:
#S'*lambda
for (row, column, value) in model.getSparseMatrix():
result.addData(self.dualRName + column, dName + row, value)
#lambda <=> +- inf
for row in model.getRowLimits().keys():
result.addColumnLimit(dName + row, (None,None))
#S'*lambda = -c'
for column in model.getColumnNames():
if column in objective.keys():
value = objective[column]
result.addRowLimit(self.dualRName + column, (-value, -value))
else:
result.addRowLimit(self.dualRName + column, (0.0, 0.0))
#S'*lambda - mu + nu = -c'
for column in model.getColumnLimits().keys():
(lower, upper) = model.getColumnLimit(column)
if lower != None:
result.addData(self.dualRName + column, '__mu__' + column, -1)
result.addColumnLimit('__mu__' + column, (0.0, None))
if upper != None:
result.addData(self.dualRName + column, '__nu__' + column, 1)
result.addColumnLimit('__nu__' + column, (0.0, None))
if equal:
#objective relation
#Z^prime = Z^dual
# cv = l*mu - u*nu = 0
# -cv + l*mu - u*nu = 0
for column in model.getColumnNames():
if column in objective.keys():
value = objective[column]
result.addData(self.equalityRName, pName + column, -value)
else:
result.addData(self.equalityRName, pName + column, 0)
for column in model.getColumnLimits().keys():
(lower, upper) = model.getColumnLimit(column)
if lower != None and lower != 0.0:
result.addData(self.equalityRName, '__mu__' + column, lower)
if upper != None and upper != 0.0:
result.addData(self.equalityRName, '__nu__' + column, -upper)
result.addRowLimit(self.equalityRName,(0,0))
objective = {}
objective[self.pName + self.newObjective] = -1
result.setObjective(objective)
return result
