Has anyone encountered scalable solutions to a binary linear optimization problem of the form:
\begin{align*} &\min \sum_{i=1}^n x_i\\ &\ \text{s.t}\quad x_i \in \{0,1\}\\ &\qquad Ax=b \end{align*}
where $x=(x_1,x_2,...,x_n)^t$, $b=(b_1, b_2,...,b_m)^t$, $b_i$ are positive integers and $A$ is a very sparse matrix with entries $0$ or $1$.
By scalable I mean solution that handles large values of $n$ and multiple constraints ($m$).
Thank you!
