I have a largish matrix M that defines a polytope P, via a bunch of constraints of the form u.v > b. Here u is the vector of variables and v ranges over the columns of the matrix M. I want to optimize a simple linear functional over P. I can use a MixedIntegerLinearProgram to solve this problem. But I have to roll my own dot_prod function, because sage will not let me treat u as a vector. Is there something I've missed?

Here is the code -

```
def dot_prod(u, v):
dim = len(v)
return sum(u[i]*v[i] for i in range(dim))
def farkas(M):
'''Look for edge vector u so that all entries of u*M are positive. If
one exists, return True.'''
N = Matrix(M)
q = MixedIntegerLinearProgram( maximization=False, solver='GLPK' )
u = q.new_variable( real=True, nonnegative=False )
for v in N.columns():
q.add_constraint( dot_prod(u, v), min = 1 ) # unpleasant
q.set_objective( sum( dot_prod(u, v) for v in N.columns() ) ) # likewise
try:
q.solve()
return True
except MIPSolverException:
return False
```