# scalable binary linear optimization

Has anyone encountered scalable solutions to a binary linear optimization problem of the form:

\min \sum_{i=1}^n x_i s.t x_i \in {0,1} Ax=b

where, x=(x_1,x_2,...,x_n)^t, b=(b_1, b_2,...,b_m)^t, b_i is positive integer and A is a very sparse matrix with entries 0 or 1.

By scalable I mean solution that handle large values of n and multiple constraints (m).

Thank you!