I am trying to solve MILP in puLP (Python), and I keep getting the following error:
Traceback (most recent call last):
File "main_lp.py", line 63, in <module>
ans = solve_lp(C)
File "/home/ashwin/Documents/Williams/f2014/math317_or/project/solve_lp.py", line 36, in solve_lp
prob.solve()
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/pulp.py", line 1619, in solve
status = solver.actualSolve(self, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solvers.py", line 1283, in actualSolve
return self.solve_CBC(lp, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solvers.py", line 1346, in solve_CBC
raise PulpSolverError("Pulp: Error while executing "+self.path)
pulp.solvers.PulpSolverError: Pulp: Error while executing /usr/local/lib/python2.7/dist-packages/PuLP-1.5.6-py2.7.egg/pulp/solverdir/cbc-32
For my linear programming problem, I am attempting to take sums of different vectors as the constraint, and I think I must have done that wrong somehow, because a much simpler problem works with no hitches. I have attached the code (C
is an N by N numpy
array).
def solve_lp(C):
N = len(C)
prob=LpProblem('Scheduling',LpMinimize)
X = [[LpVariable('X' + str(i+1) + str(j+1), 0, C[i,j],LpBinary)
for j in range(N)] for i in range(N)]
X = np.array(X)
X_o = [LpVariable('X0' + str(i), 0, None, LpBinary) for i in range(N)]
X_t = [LpVariable('X' + str(i) + 't', 0, None, LpBinary) for i in range(N)]
# Objective Function
ones_vec = list(np.ones(len(X_o)))
prob += lpDot(ones_vec,X_o), 'Minimize Buses'
# Constraints
for i in range(N):
row = list(X[i,:]) + [X_t[i]]
ones_vec = list(np.ones(len(row)))
prob += lpDot(ones_vec, row) == 1, 'Only one destination for ' + str(i)
for j in range(N):
col = list(X[:,j]) + [X_o[j]]
ones_vec = list(np.ones(len(col)))
prob += lpDot(ones_vec,col) == 1, 'Only one source for ' + str(j)
prob.solve()
return X, value(prob.objective)
In PuLP, the default solver is CBC, but it can work with other solvers as well.
Infeasible problemsTake out constraints and see if the problem becomes feasible. Generate lp file writeLP() for the problem and open it with a text editor to see if the constraints are correctly built. Check the solver logs to additional message from the solver regarding the constraint or variable involved.
Make sure you don't have duplicate LpVariable names, and watch out for LpVariable names with the unsupported characters -+[] ->/
since all of those characters are silently converted to underscores _
Setting LpSolverDefault.msg = 1
before calling prob.solve()
may help by printing the solvers output to the console.
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