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Exact solution method to solve large scale integer quadratic multidimensional knapsack problems

机译:解决大规模整数二次多维背包问题的精确解法

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摘要

In this paper we develop a branch-and-bound algorithm for solving a particular integer quadratic multi-knapsack problem. The problem we study is defined as the maximization of a concave separable quadratic objective function over a convex set of linear constraints and bounded integer variables. Our exact solution method is based on the computation of an upper bound and also includes pre-procedure techniques in order to reduce the problem size before starting the branch-and-bound process. We lead a numerical comparison between our method and three other existing algorithms. The approach we propose outperforms other procedures for large-scaled instances (up to 2000 variables and constraints).
机译:在本文中,我们开发了一种分支定界算法来解决特定的整数二次多背包问题。我们研究的问题定义为在线性约束和有界整数变量的凸集上的凹可分离二次目标函数的最大化。我们的精确解决方案方法基于上限的计算,并且还包括预过程技术,以便在开始分支定界过程之前减小问题的大小。我们在我们的方法和其他三个现有算法之间进行了数值比较。对于大型实例(最多2000个变量和约束),我们提出的方法优于其他过程。

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