pythonoptimizationcplextraveling-salesman
CPLEX-python (3.6) keeps showing a "not list" ERROR
I am using the CPLEX solver with python 3.6 to solve mathematical programming models. I used to do this on my old computer and now reinstall cplex on the new one without problems, however when I try to run models that originally ran without errors, now I always get the same error, for example for the Traveling Salesman Problem:
TypeError Traceback (most recent call last)
~\Dropbox\CPLEX\TSP_MTZ\TSP.py in <module>
137
138
--> 139 TSP(4)
~\Dropbox\CPLEX\TSP_MTZ\TSP.py in TSP(N)
38 for j in range(N):
39 x_varobj.append(float(c[i,j]))
---> 40 Model.variables.add(obj = x_varobj, lb = x_varlb, ub = x_varub, types = x_vartypes, names = x_varnames)
41
42 u_vars=list(np.array(["u("+str(i)+")" for i in range(0,N)]))
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_subinterfaces.py in add(self, obj, lb, ub, types, names, columns)
454 columns)
455 return self._add_iter(self.get_num, self._add,
--> 456 obj, lb, ub, types, names, columns)
457
458 def delete(self, *args):
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_baseinterface.py in _add_iter(getnumfun, addfun, *args, **kwargs)
39 """non-public"""
40 old = getnumfun()
---> 41 addfun(*args, **kwargs)
42 return range(old, getnumfun())
43
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_subinterfaces.py in _add(self, obj, lb, ub, types, names, columns)
376 if columns == []:
377 CPX_PROC.newcols(self._env._e, self._cplex._lp, obj, lb, ub,
--> 378 types, names)
379 else:
380 with CPX_PROC.chbmatrix(columns, self._cplex._env_lp_ptr,
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_procedural.py in newcols(env, lp, obj, lb, ub, xctype, colname)
965 status = CR.CPXXnewcols(
966 env, lp, ccnt, c_obj, c_lb, c_ub,
--> 967 xctype, colname)
968 check_status(env, status)
969
c:\users\healh\.conda\envs\py36\lib\site-packages\cplex\_internal\_pycplex.py in CPXXnewcols(env, lp, ccnt, py_obj, py_lb, py_ub, xctype, colname)
1783
1784 def CPXXnewcols(env: 'CPXCENVptr', lp: 'CPXLPptr', ccnt: 'CPXDIM', py_obj: 'double const *', py_lb: 'double const *', py_ub: 'double const *', xctype: 'char const *', colname: 'char const *const *') -> "int":
-> 1785 return _pycplex_platform.CPXXnewcols(env, lp, ccnt, py_obj, py_lb, py_ub, xctype, colname)
1786
1787 def CPXXaddcols(env: 'CPXCENVptr', lp: 'CPXLPptr', ccnt: 'CPXDIM', nzcnt: 'CPXNNZ', py_obj: 'double const *', py_matbeg: 'CPXNNZ const *', py_lb: 'double const *', py_ub: 'double const *', colname: 'char const *const *') -> "int":
TypeError: not a list
My code is the following:
import time
import numpy as np
import cplex
from cplex import Cplex
from cplex.exceptions import CplexError
import sys
import networkx as nx
import matplotlib.pyplot as plt
from openpyxl import Workbook
import xlrd
def TSP(N):
wb = Workbook()
ws = wb.active
book = xlrd.open_workbook('C.xlsx') #LECTURA DE PARÁMETROS.
sheet = book.sheet_by_name("C")
c=[[int(sheet.cell_value(r,c)) for c in range(sheet.ncols)] for r in range(sheet.nrows)]
c=np.matrix(c)
print("")
print("MATRIZ DE DISTANCIAS")
print("")
print(c)
print("")
print("")
print("")
Model=cplex.Cplex()
x_vars=np.array([["x("+str(i)+","+str(j)+")" for j in range(N)] for i in range(N)])
x_varnames = x_vars.flatten()
x_vartypes='B'*N*N
x_varlb = [0.0]*len(x_varnames)
x_varub = [1.0]*len(x_varnames)
x_varobj = []
for i in range(N):
for j in range(N):
x_varobj.append(float(c[i,j]))
Model.variables.add(obj = x_varobj, lb = x_varlb, ub = x_varub, types = x_vartypes, names = x_varnames)
u_vars=np.array(["u("+str(i)+")" for i in range(0,N)])
u_varnames=u_vars.flatten()
u_vartypes='I'*N
u_varlb=[1.0]*N
u_varub=[float(N)-1.0]*N
u_varobj=[0.0]*N
Model.variables.add(obj = u_varobj, lb = u_varlb, ub = u_varub, types = u_vartypes, names = u_varnames)
Model.objective.set_sense(Model.objective.sense.minimize)
# suma(J,x[i,j])==1.0, forall i in N
for i in range(N):
row1=[]
val1=[]
for j in range(N):
row1.append(x_vars[i,j])
val1.append(1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row1, val= val1)], senses = 'E', rhs = [1.0])
# suma(i,x[i,j])==1.0, forall j in N
for j in range(N):
row2=[]
val2=[]
for i in range(N):
row2.append(x_vars[i,j])
val2.append(1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row2, val= val2)], senses = 'E', rhs = [1.0])
#u[i]-u[j]-(N-1)x[i,ji]<=N-2 , forall i in N, forall j in N, con i!=j.
for i in range(1,N):
for j in range(1,N):
if i!=j:
row3=[]
val3=[]
row3.append(u_vars[i])
val3.append(1.0)
row3.append(u_vars[j])
val3.append(-1.0)
row3.append(x_vars[i,j])
val3.append(float(N)-1.0)
Model.linear_constraints.add(lin_expr = [cplex.SparsePair(ind = row3, val= val3)], senses = 'L', rhs = [float(N)-2.0])
solution=Model.solve()
Model.write('modelo.lp')
#Model.parameters.mip.pool.relgap.set(0.6)
pool_solution=Model.populate_solution_pool()
#print(pool_solution)
def show_solution():
print("\nVARLOS FUNCION OBJETIVO - DISTANCIA MINIMIA = {}".format(Model.solution.get_objective_value()))
V=[i for i in range(N)]
E=[]
E1=[(i,j) for i in range(N) for j in range(N) if i!=j]
for i in range(0,N):
for j in range(0,N):
if(Model.solution.get_values("x("+str(i)+","+str(j)+")")!=0.0):
print("x("+str(i)+","+str(j)+")"+" = "+str(Model.solution.get_values("x("+str(i)+","+str(j)+")")))
E.append((i,j))
print("")
for i in range(0,N):
if(Model.solution.get_values("u("+str(i)+")")!=0.0):
print("u("+str(i)+")"+" = "+str(Model.solution.get_values("u("+str(i)+")")))
print("")
G=nx.DiGraph()
G.add_edges_from(E)
G.add_nodes_from(V)
pos=nx.spring_layout(G,k=0.3)
print(Model.solution.get_values("x("+str(1)+","+str(0)+")")) #OBTENER VALOR DE UNA VARIABLE.
print("ESTATUS_DE_LA_SOLUCION_ENCONTRADA:", Model.solution.get_status_string())
print("SOLUCION_PRIMAL_OPTIMA?:", Model.solution.is_primal_feasible())
#print(Model.variables.get_cols())
nx.draw_networkx_nodes(G, pos)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edges(G, pos, edgelist=E1, edge_color='blue', width=0.3 ,arrows=True) # highlight elist
nx.draw_networkx_edges(G, pos, edge_color='black', width=1.8,arrows=True) # show all edges, thin lines
# turn off axis markings
plt.axis('off')
plt.savefig('grafo_tsp.png',dpi=20)
plt.show()
show_solution()
TSP(4)
And here is the data:
I really do not understand the problem, I use to do this daily and now I have this problem, any hints?
Solution
The problem is that
x_varnames = x_vars.flatten()
creates x_varnames as a numpy array while the argument to names for variables.add() is expected to be a list.
You can fix this by defining x_varnames as
x_varnames = x_vars.flatten().tolist()
I am not sure whether it is a change in CPLEX or a change in numpy that causes this trouble.