Boole's probability bounding problem, linear programming aggregations, and nonnegative quadratic pseudo-Boolean functions

TL;DR

This paper develops a hierarchy of aggregation methods for Boole's probability bounding problem, improving bounds by analyzing polyhedral descriptions of subcones and enhancing computational efficiency.

Contribution

It introduces a hierarchy of aggregation techniques and a generic analysis approach, leading to tighter bounds for Hailperin's model and better understanding of the underlying polyhedral structure.

Findings

Hierarchical aggregation methods improve bounds for Boole's problem.

Complete polyhedral descriptions of subcones enable tighter bounds.

Significant improvements over previous aggregation-based bounds.

Abstract

Hailperin (1965) introduced a linear programming formulation to a difficult family of problems, originally proposed by Boole (1854,1868). Hailperin's model is computationally still difficult and involves an exponential number of variables (in terms of a typical input size for Boole's problem). Numerous papers provided efficiently computable bounds for the minimum and maximum values of Hailperin's model by using aggregation that is a monotone linear mapping to a lower dimensional space. In many cases the image of the positive orthant is a subcone of the positive orthant in the lower dimensional space, and thus including some of the defining inequalities of this subcone can tighten up such an aggregation model, and lead to better bounds. Improving on some recent results, we propose a hierarchy of aggregations for Hailperin's model and a generic approach for the analysis of these aggregations. We obtain complete polyhedral descriptions of the above mentioned subcones and obtain significant improvements in the quality of the bounds.