I,d = multidict({1:80, 2:270, 3:250 , 4:160, 5:180})
J,M,N = multidict({1:[500,600], 2:[800,500], 3:[500,100]})
print(I,d)
print(J,M,N)
cost = {(1,1):4, (1,2):6, (1,3):9,
(2,1):5, (2,2):4, (2,3):7,
(3,1):6, (3,2):3, (3,3):4,
(4,1):8, (4,2):5, (4,3):3,
(5,1):10, (5,2):8, (5,3):4
}
arcs = tuplelist([(i,j) for (i,j) in cost])
print( arcs.select(2,"*") )
CBC options
Coin Branch and Cutを用いて最適化する際に，以下のオプションを付加することができる．
class pulp.apis.PULP_CBC_CMD(mip=True, msg=True, timeLimit=None, fracGap=None, maxSeconds=None, gapRel=None, gapAbs=None, presolve=None, cuts=None, strong=None, options=None, warmStart=False, keepFiles=False, path=None, threads=None, logPath=None, mip_start=False)
Bases: pulp.apis.coin_api.COIN_CMDThis solver uses a precompiled version of cbc provided with the package
Parameters
mip (bool) – if False, assume LP even if integer variables
msg (bool) – if False, no log is shown
timeLimit (float) – maximum time for solver (in seconds)
gapRel (float) – relative gap tolerance for the solver to stop (in fraction)
gapAbs (float) – absolute gap tolerance for the solver to stop
threads (int) – sets the maximum number of threads
options (list) – list of additional options to pass to solver
warmStart (bool) – if True, the solver will use the current value of variables as a start
keepFiles (bool) – if True, files are saved in the current directory and not deleted after solving
path (str) – path to the solver binary
logPath (str) – path to the log file
presolve (bool) – if True, adds presolve on
cuts (bool) – if True, adds gomory on knapsack on probing on
strong (bool) – if True, adds strong
モデルクラスの最適化メソッド optimize の引数 solverで， PULP_CBC_CMDのインスタンスを渡す．
optimize(self, solver=PULP_CBC_CMD(timeLimit=max_cpu, presolve=True))
TEST=True
if TEST:
model = Model("lo1")
J, v = multidict({1: 16, 2: 19, 3: 23, 4: 28})
# x1 = model.addVar(vtype="C", name="x1")
# x2 = model.addVar(vtype="I", name="x2")
# x3 = model.addVar(lb=0, ub=30, vtype="B", name="x3")
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="x1")
# x1 = model.addVar(vtype="N", lb=10.5, ub=50,name="x1")
x2 = model.addVar(vtype="C", name="x2")
x3 = model.addVar(lb=0, ub=30, vtype="C", name="x3")
model.update()
#model.addSOS(2, [x1, x2, x3])
#L1 = LinExpr([2, 1, 1], [x1, x2, x3])
# L1=LinExpr()
# L1.addTerms(2,x1)
# L1.addTerms(1,x2)
# L1.addTerms(1,x3)
#model.addConstr(lhs=L1,sense="<=",rhs=60,name="C0")
model.addConstr(x1 + 2*x2 + x3 <= 60, name="C1")
#c1 = model.addConstr(lhs=L1, sense="<=", rhs=60)
model.addConstr(x1 + 2 * x2 + x3 <= 60)
model.setObjective(15 * x1 + 18 * x2 + 30 * x3, GRB.MAXIMIZE)
#model.write("mypulp1.mps")
#model.write("mypulp1.lp")
#SCIPを使用する場合
solver = pulp.SCIP(timeLimit=1)
model.optimize(solver=solver)
#model.optimize()
if model.Status == GRB.Status.OPTIMAL:
model.write("answer.sol")
print("Opt. Value=", model.ObjVal)
# for v in model.getVars():
# print(v.VarName,v.X,v.RC)
for v in model.getVars():
print(v.VarName, v.X)
for c in model.getConstrs():
print(c.ConstrName, c.Pi)
# m=Model()
# x=m.addVar( name="x", vtype="I" , lb=1)
# y=m.addVar( name="y", vtype="I" , lb=1)
# z=m.addVar( name="z", vtype="I" , lb=1)
# m.update()
# m.addConstr(2*x+ y ==11)
# m.addConstr(y+ 3*z ==14)
# # m.setObjective(0,GRB.MINIMIZE)
# m.optimize()
# print(x.X,y.X,z.X)
if TEST:
m=Model()
x = [0,2,3,1]
y = [1,0,3,3]
n=len(x)
X=m.addVar(ub=3.0, name="X")
Y=m.addVar(ub=3.0, name ="Y")
Z=m.addVar(name ="Z")
a,b={},{}
for i in range(n):
a[i] =m.addVar(name="a({0})".format(i))
b[i] =m.addVar(name="b({0})".format(i))
m.update()
for i in range(n):
m.addConstr( a[i] >= x[i] -X )
m.addConstr( a[i] >= X-x[i] )
m.addConstr( b[i] >= y[i] -Y )
m.addConstr( b[i] >= Y-y[i] )
m.addConstr(Z>=a[i]+b[i])
m.setObjective(Z, GRB.MINIMIZE)
# m.setObjective( quicksum( 1*a[i] for i in range(n))
# + quicksum( 1*b[i] for i in range(n)), GRB.MINIMIZE )
m.optimize()
print(X.X,Y.X)
print(m.ObjVal)
for i in range(n):
print(a[i].X,b[i].X)
if TEST:
model = Model("lo1")
x = model.addVar(name='tonkoke')
y = model.addVar(name='kokenton')
z = model.addVar(name='mix')
model.setObjective(15*x + 18*y + 30*z, GRB.MAXIMIZE)
model.addConstr(2*x + y + z <= 60, name='pork')
model.addConstr( x + 2*y + z <= 60, name='chicken')
model.addConstr( z <= 30, name='beef')
model.optimize()
print( model.ObjVal )
for i in model.getVars():
print(i.VarName, i.X)