论文标题
混合凸优化边界以进行最大渗透取样
Mixing convex-optimization bounds for maximum-entropy sampling
论文作者
论文摘要
最大透镜抽样问题是与空间统计中应用的基本且具有挑战性的组合优化问题。它要求找到一个最大确定订单-US $ s $ princadal submal of dorder-$ n $ n $协方差矩阵。此NP硬化问题的精确解决方案方法基于分支结合的框架。最佳值的许多已知上限基于凸优化。我们提出了一种“混合”这些界限以实现更好界限的方法。
The maximum-entropy sampling problem is a fundamental and challenging combinatorial-optimization problem, with application in spatial statistics. It asks to find a maximum-determinant order-$s$ principal submatrix of an order-$n$ covariance matrix. Exact solution methods for this NP-hard problem are based on a branch-and-bound framework. Many of the known upper bounds for the optimal value are based on convex optimization. We present a methodology for "mixing" these bounds to achieve better bounds.
