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!
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