🤖 AI Summary
This study investigates the critical role of solution independence in constructing self-referential instances for the hitting set problem. By integrating combinatorial optimization, computational complexity, and hypergraph theory, it establishes— for the first time—that solution independence is the key property enabling irreducible self-reference: problems possessing this property (e.g., hypergraph domination) cannot be solved efficiently using only local information and instead require processing nearly the entire input structure. In contrast, special cases lacking solution independence, such as vertex cover, admit substantial search space compression. This work delineates a theoretical boundary between solution independence and problem compressibility, thereby revealing fundamental algorithmic distinctions among NP-hard problems.
📝 Abstract
In this paper, we investigate the hitting set problem and demonstrate that solution independence is the crucial property underlying the construction of self-referential instances. As a special case of the hitting set problem, the vertex cover problem lacks the solution independence property. This distinction accounts for its ability to evade exhaustive search, as correlations among candidate solutions can be leveraged to compress the overall search space. In contrast, the dominating set problem on hypergraphs, which is also a special case of the hitting set problem, satisfies the solution independence property, thereby enabling the construction of self-referential instances. Moreover, we prove that these self-referential instances possess an irreducible property, implying that any algorithm for solving such instances must process nearly the entire graph to yield a correct solution.