CPLEX-python (3.6) 不断显示 "not list" 错误
CPLEX-python (3.6) keeps showing a "not list" ERROR
我正在使用带有 python 3.6 的 CPLEX 求解器来求解数学规划模型。我曾经在我的旧计算机上这样做，现在在新计算机上重新安装 cplex 没有问题，但是当我尝试 运行 最初 运行 没有错误的模型时，现在我总是得到同样的错误，因为旅行商问题示例：
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
我的代码如下：
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)
这是数据：
我真的不明白这个问题，我以前每天都这样做，现在我有这个问题，有什么提示吗？
问题在于
x_varnames = x_vars.flatten()
将 x_varnames 创建为一个 numpy 数组，而 names 的参数 variables.add() 应该是一个列表。
您可以通过将 x_varnames 定义为
来解决此问题
x_varnames = x_vars.flatten().tolist()
我不确定是CPLEX的变化还是numpy的变化导致了这个问题。
我正在使用带有 python 3.6 的 CPLEX 求解器来求解数学规划模型。我曾经在我的旧计算机上这样做，现在在新计算机上重新安装 cplex 没有问题，但是当我尝试 运行 最初 运行 没有错误的模型时，现在我总是得到同样的错误，因为旅行商问题示例：
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
我的代码如下：
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)
这是数据：
我真的不明白这个问题，我以前每天都这样做，现在我有这个问题，有什么提示吗？
问题在于
x_varnames = x_vars.flatten()
将 x_varnames 创建为一个 numpy 数组，而 names 的参数 variables.add() 应该是一个列表。
您可以通过将 x_varnames 定义为
x_varnames = x_vars.flatten().tolist()
我不确定是CPLEX的变化还是numpy的变化导致了这个问题。