论文标题
迭代地重新加权贪婪的套装
Iteratively reweighted greedy set cover
论文作者
论文摘要
我们通过经验分析了一个简单的启发式，以解决稀疏的固定覆盖问题。它使用加权贪婪算法作为基本构建块。通过对元素附加的权重的乘法更新，迭代的解决方案可以改善。该算法的实现是微不足道的，算法本质上没有需要调整的参数。更多的迭代只能改善解决方案。这套功能使该方法对实际问题有吸引力。
We empirically analyze a simple heuristic for large sparse set cover problems. It uses the weighted greedy algorithm as a basic building block. By multiplicative updates of the weights attached to the elements, the greedy solution is iteratively improved. The implementation of this algorithm is trivial and the algorithm is essentially free of parameters that would require tuning. More iterations can only improve the solution. This set of features makes the approach attractive for practical problems.
