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Actually in this way one obtains a generalization of the min-cost flow problem, which remains a linear programming problem, but can not be solved as a classical min-cost flow problem.
Actually in this way one obtains a generalization of the min-cost flow problem (where: the costs are allowed to be negative, and the flow value is not fixed), which remains a linear programming problem, but can not be solved as a classical min-cost flow problem.
Then P3 can be solved as a Hitchcock problem, i.e., in polynomial time.
An instance of P can be solved as a min-cost flow problem.
Then P1 can be solved as a min-cost flow problem, i.e., in polynomial time.
This leads to a problem that can be solved as a non-linear optimization problem.
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"A lot of us that were trained and had skills in reproductive health moved to HIV and the world believed at the time that HIV … could be solved as an emergency.
The above optimization problem (5) to (9) can be solved as an LP.
The two-stage problem can be solved as an MILP by decomposition algorithms.
Fortunately, as shown in [24, 25], the above probabilistically constrained problem can be solved as an integer programming (IP) problem with deterministic constraints.
It is to be noted that (20a) is to be solved as an integer optimization problem, because N p N ∈ N must be satisfied.
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