picos.sum does not work on variable vectors

Calling picos.sum(v), when v is an instance of picos.expressions.Variable with multiple entries (i.e. a vector), raises a TypeError.

MWE:

import picos as pic
p=picos.Problem()
v=p.add_variable('v',15)
pic.sum(v)

Actual Result: TypeError: dimension too small (see traceback at end)

Expected result: <1×1 Affine Expression: ∑(v[i] : i ∈ [0…14])> The expected result is produced if instead of pic.sum(v) I take pic.sum(v+0), which is why I report this as a bug rather than a feature request.

The root cause of this issue appears to be that a variable vector is not properly an iterable, so you cant do for item in v.

But the code to do sum assumes that this is possible.

See Traceback:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-fd05a1d5fdae> in <module>
----> 1 picos.sum(v)

~/miniconda3/envs/uq/lib/python3.6/site-packages/picos/tools.py in sum(lst, it, indices)
     74             DeprecationWarning)
     75
---> 76     if not all(isinstance(expression, Expression) for expression in lst):
     77         return builtin_sum(lst)
     78

~/miniconda3/envs/uq/lib/python3.6/site-packages/picos/tools.py in <genexpr>(.0)
     74             DeprecationWarning)
     75
---> 76     if not all(isinstance(expression, Expression) for expression in lst):
     77         return builtin_sum(lst)
     78

~/miniconda3/envs/uq/lib/python3.6/site-packages/picos/expressions.py in __getitem__(self, index)
   3973         JJ = rangeT
   3974         VV = [1.] * nsz
-> 3975         newfacs = {self: spmatrix(VV, II, JJ, (nsz, sz))}
   3976         if not self.constant is None:
   3977             newcons = self.constant[rangeT]

~/miniconda3/envs/uq/lib/python3.6/site-packages/picos/tools.py in spmatrix(*args, **kwargs)
   2174     """
   2175     try:
-> 2176         return cvx.spmatrix(*args, **kwargs)
   2177     except TypeError as error:
   2178         # CVXOPT does not like NumPy's int64 scalar type for indices, so attempt

TypeError: dimension too small