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Approximation algorithms via discrepancy with hard constraints
In this paper, we present a discrepancy-based approach to cost-preserving rounding under hard constraints. Starting from a feasible fractional solution, our rounding procedure preserves the designated hard constraints, never increases the linear objective, and bounds the error in the remaining const
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