def optimize(
self, solver="ralg", plot=0, maxIter=1e5, maxCPUTime=3600, maxFunEvals=1e12
):
if self.mtype == "LP" or self.mtype == "MILP":
p = MILP(
self.objective,
self.init,
constraints=self.constraints,
maxIter=maxIter,
maxCPUTime=maxCPUTime,
maxFunEvals=maxFunEvals,
)
elif self.mtype == "NLP":
p = NLP(
self.objective,
self.init,
constraints=self.constraints,
maxIter=maxIter,
maxCPUTime=maxCPUTime,
maxFunEvals=maxFunEvals,
)
else:
print("Model Type Error")
raise TypeError
if self.sense == GRB.MAXIMIZE:
self.Results = p.maximize(solver, plot=plot)
else:
self.Results = p.minimize(solver, plot=plot)
# print(self.Results)
self.ObjVal = self.Results.ff
if self.Results.isFeasible:
self.Status = GRB.OPTIMAL
else:
self.Status = GRB.INFEASIBLE
for v in self.variables:
v.VarName = v.name
v.X = self.Results.xf[v]
return self.Status
# Define some constraints
cons = [
x + 5 * y < 15, x[0] < -5,
f1 < [25, 35], f1 > -100, 2 * f1 + 4 * z < [80, 800], 5 * f2 + 4 * z < 100,
[-5.5, -4.5] < x, x < 1, -17 < y, y < 20, -4000 < z, z < 4
]
# Create prob
# old-style:
#p = MILP(obj, startPoint, intVars = [y, z], constraints=cons)
# new (OpenOpt v 0.37+):
p = MILP(obj, startPoint, constraints=cons)
# Solve
r = p.minimize(
'lpSolve', iprint=-1
) # glpk is name of the solver involved, see OOF doc for more arguments
# Decode solution
s = r.xf
print('Solution: x = %s y = %f z = %f' % (str(s[x]), s[y], s[z]))
# Solution: x = [-5.25 -4.5 ] y = 3.000000 z = -33.000000
# OPTIONAL: you can export the problem into MPS format file
# (lpsolve and its Python binding should be properly installed,
# you may take a look at the instructions from openopt.org/LP)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('milp_1')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
obj = x.sum() + y + 50*z + sum(f3) + 2*f2.sum() + 4064.6
# Start point - currently matters only size of variables
startPoint = {x:[8, 15], y:25, z:80} # however, using numpy.arrays is more recommended than Python lists
# Define some constraints
cons = [x+5*y<15, x[0]<-5, f1<[25, 35], f1>-100, 2*f1+4*z<[80, 800], 5*f2+4*z<100, [-5.5, -4.5]<x, x<1, -17<y, y<20, -4000<z, z<4]
# Create prob
# old-style:
#p = MILP(obj, startPoint, intVars = [y, z], constraints=cons)
# new (OpenOpt v 0.37+):
p = MILP(obj, startPoint, constraints=cons)
# Solve
r = p.minimize('lpSolve', iprint=-1) # glpk is name of the solver involved, see OOF doc for more arguments
# Decode solution
s = r.xf
print('Solution: x = %s y = %f z = %f' % (str(s[x]), s[y], s[z]))
# Solution: x = [-5.25 -4.5 ] y = 3.000000 z = -33.000000
# OPTIONAL: you can export the problem into MPS format file
# (lpsolve and its Python binding should be properly installed,
# you may take a look at the instructions from openopt.org/LP)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('milp_1')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
# You can solve problems defined in MPS files