def minimize(self, **kwargs):
""" solve the linear problem with the solver given in self.solver
Returns:
obj_value, solution
obj_value -- value of the objective function at the discovered solution
solution -- the solution flux vector (indexed like matrix columns)
"""
if self.solver == _GLPK:
# Use pyMathProg interface to GLPK solver
self.glpkP.solve()
self.istop = self.glpkP.status()
# print "istop:", self.istop
self.status = glpkToSolverStatus(self.istop)
if self.status == SolverStatus.OPTIMAL:
self.obj_value = self.glpkP.vobj()
self.solution = [
self.glpkVar[i].primal for i in range(len(self.glpkVar))
]
else:
self.obj_value = 0.
self.solution = []
else:
# Use OpenOpt interface
lp = LP(self.obj,
A=self.Aineq,
Aeq=self.Aeq,
b=self.bineq,
beq=self.beq,
lb=self.lb,
ub=self.ub,
**kwargs)
lp.debug = 1
# lp.iprint=-1 # suppress solver output
r = lp.solve(self.solver if self.solver != _OOGLPK else 'glpk')
if r.istop <= 0. or r.ff != r.ff: # check halting condition
self.obj_value = 0.
self.solution = []
self.istop = r.istop
else:
self.obj_value = r.ff
self.solution = r.xf
self.istop = r.istop
# print self.istop
return self.obj_value, self.solution
