I think the function you're looking for is cvx.multiply

For example:

In [1]: import cvxpy as cvx

In [2]: n = 10

In [3]: X = cvx.Variable((n, 1))

In [4]: V = cvx.Variable((n, n))

In [5]: cvx.multiply(X, V*X)
Out[5]: Expression(UNKNOWN, UNKNOWN, (10, 1))

In the 1.0 update notes, they mention that this function used to be called mul_elemwise (<1.0), which may have been the source of your confusion.

David Ketcheson has already indicated the problem in his comment. I will flesh it out here.

Note the form of the argument of the log in the logistic regression objective:

$log[1+exp(−b_iA^T_ix)]$

where $b_i$ and $x$ are vectors and $A_i$ is a matrix. Only in the above order of matrix-vector multiplications will you get a scalar as an exponent.

The Matlab code should be (thanks for the correction OP):

log(1 + exp(-1*b.*(A * x)))      

This will be a vector which will work with the following sum:

sum(log(1 + exp(-1*b.*(A * x))))

The Python code could be (updated after OP's comment):

log_vec = [log(1+ exp(b[i]*A[i,:].T*x)) for i in range(n)] 
f = (1/m) + sum(log_vec) + lamb_default*mul(x,x)

This list comprehension will work for numpy matrix type. Make sure it works for matrix type of cvxopt.