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Upper Bounds on the Number of Solutions of Binary Integer Programs

机译:二进制整数程序解数的上限

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摘要

We present a new method to compute upper bounds of the number of solutions of binary integer programming (BIP) problems. Given a BIP, we create a dynamic programming (DP) table for a redundant knapsack constraint which is obtained by surrogate relaxation. We then consider a Lagrangian relaxation of the original problem to obtain an initial weight bound on the knapsack. This bound is then refined through subgradient optimization. The latter provides a variety of Lagrange multipliers which allow us to filter infeasible edges in the DP table. The number of paths in the final table then provides an upper bound on the number of solutions. Numerical results show the effectiveness of our counting framework on automatic recording and market split problems.
机译:我们提出了一种新的方法来计算二进制整数规划(BIP)问题的解数的上限。给定一个BIP,我们为通过代理松弛获得的冗余背包约束创建了一个动态规划(DP)表。然后,我们考虑原始问题的拉格朗日松弛,以在背包上获得初始重量约束。然后通过次梯度优化来优化此界限。后者提供了各种拉格朗日乘数,使我们能够过滤DP表中不可行的边。然后,最终表中的路径数提供了解决方案数的上限。数值结果表明我们的计数框架在自动记录和市场拆分问题上的有效性。

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