Core Determining Class and Inequality Selection
提出一种算法，通过二分图结构构建精确核心决定类（即冗余不等式集），并证明其不依赖于结果概率测度；针对更一般的线性不等式选择问题，提出类似Dantzig选择子的统计方法，通过蒙特卡洛实验验证性能。
The relations between unobserved events and observed outcomes can be characterized by a bipartite graph. We propose an algorithm that explores the structure of the graph to construct the “exact Core Determining Class,” i.e., the set of irredudant inequalities. We prove that in general the exact Core Determining Class does not depend on the probability measure of the outcomes but only on the structure of the graph. For more general linear inequalities selection problems, we propose a statistical procedure similar to the Dantzig Selector to select the truly informative constraints. We demonstrate performances of our procedures in Monte-Carlo experiments.