Reduced Cost Attributes
Hello,
I have a restricted master problem that runs just fine. But I'm not able to access the reduced cost or the shadow price attributes for the problem. All of the variables are continuous. I tried accessing the reduced cost by individual variable too (ex. r[0,1] ) but that does not work either. Any suggestions? Also, is there a method yet to access the reduced cost for integer variables? Thank you in advance!
m = Model("Master Problem")
m.Params.OutputFlag=1
m.read("m_param.prm")
m.update()
r = {}
s = {}
z = {}
r = m.addVars(airports,uc,lb=0,ub=1,vtype=GRB.CONTINUOUS,name='r')
s = m.addVars(secmea,airports,uc,lb=0,vtype=GRB.CONTINUOUS,name='s')
z = m.addVars(secmea,airports,uc,lb=0,vtype=GRB.CONTINUOUS,name='z')
objn1 = quicksum(r[k,j]*Rx[int(uniquecombon[j])] for k in range(airports) for j in range(uc))
objn2 = 1*quicksum(
(1SA[SAP,1])*fr[0]*r[k,j]*qnew1[int(uniquecombon[j])] + (1SA[SAP,2])*fr[1]*r[k,j]*qnew2[int(uniquecombon[j])]
for k in range(airports) for j in range(uc))
objn3 = quicksum(SL[k,int(uniquecombon[j])]*z[d,k,j] for d in range(secmea) for k in range(airports) for j in range(uc))
m.setObjectiveN(objn1,index=0,priority=0,weight=1)
m.setObjectiveN(objn2,index=1,priority=0,weight=1)
m.setObjectiveN(objn3,index=2,priority=0,weight=1)
c0 = {}
c1 = {}
c2 = {}
c3 = {}
c4 = {}
c5 = {}
for d in range(secmea):
c0[d] = m.addConstr(quicksum(z[d,k,j] for k in range(airports) for j in range(0,uc)) >= E[d],"Existing_Avail_"+str(d))
c1[d] = m.addConstr(quicksum(z[d,k,j] for k in range(airports) for j in range(0,uc)) <= U[d],"Resource_Avail_"+str(d))
for k in range(airports):
c2[k] = m.addConstr(quicksum(r[k,j] for j in range(uc)) == 1, 'Airport_'+str(k))
for d in range(secmea):
c3[d,k] = m.addConstr(quicksum(z[d,k,j] for j in range(uc)) <= quicksum(K[d,k] for j in range(uc)),"linearity1_"+str(d)+str(k))
c4[d,k] = m.addConstr(quicksum(s[d,k,j]  (1r[k,j])*K[d,k] for j in range(uc)) <= quicksum(z[d,k,j] for j in range(uc)),"linearity2_"+str(d)+str(k))
c5[d,k] = m.addConstr(quicksum(z[d,k,j] for j in range(uc)) <= quicksum(s[d,k,j] for j in range(uc)),"linearity3_"+str(d)+str(k))
m.update
m.optimize()
shadow_price = m.getAttr(GRB.Attr.Pi)
print("Master Complete")
mval = m.ObjVal
r.RC
s.RC
z.RC
Thanks,
Taylor

Concerning your question about integer variables: There are no (trivial) duals / reduced costs in MIPs. What you might want to do is to fix the integer solution and solve the remaining LP.
Concerning your other (continous) question: The code seems incomplete, so I cannot test it. What do you mean by "does not work"? Do you get an exception? Do you get zeros (which might be perfectly ok)? For a very simple LP I just tested, the code
print m.getAttr(GRB.Attr.RC)
print m.getAttr(GRB.Attr.Pi)works.
0 
Hi Thomas, thanks for the reply. The error I get when optimizing a standard linear program with continuous variables is
AttributeError: 'tupledict' object has no attribute 'RC'
I understand that zeros are fine, but I did not expect to receive no attribute because I know that the solution is not the optimal one.
Thank you,
Taylor
0 
Ah, I see.
You are calling:
r.RC
s.RC
z.RCI guess, this should be something like:
r.getAttr(GRB.Attr.RC)
s.getAttr(GRB.Attr.RC)
z.getAttr(GRB.Attr.RC)0
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