🤖 AI Summary
This paper addresses the Stable Marriage problem with Ties and Incomplete lists (SMTI) and its extension, the Hospital/Resident problem with Ties (HRT), aiming to compute maximum-size weakly stable matchings. We propose TBLS, a novel local search algorithm based on dynamic tie-breaking—introducing the first iterative tie-breaking mechanism for these problems. Furthermore, we design TBLS-E, a fairness-enhanced variant that significantly reduces the gender-equality cost while maintaining near-optimal matching size—the first such improvement in fairness for SMTI/HRT. TBLS incorporates preference-rank-guided neighborhood exploration and explicit fairness constraints. On large-scale benchmark instances, TBLS achieves state-of-the-art (SOTA) matching size, while TBLS-E attains the best-known fairness performance and runs substantially faster than existing local search methods.
📝 Abstract
The stable marriage problem with incomplete lists and ties (SMTI) and the hospitals/residents problem with ties (HRT) are important in matching theory with broad practical applications. In this paper, we introduce a tie-breaking based local search algorithm (TBLS) designed to achieve a weakly stable matching of maximum size for both the SMTI and HRT problems. TBLS begins by arbitrarily resolving all ties and iteratively refines the tie-breaking strategy by adjusting the relative order within ties based on preference ranks and the current stable matching. Additionally, we introduce TBLS-E, an equity-focused variant of TBLS, specifically designed for the SMTI problem. This variant maintains the objective of maximizing matching size, while enhancing equity through two simple modifications. In comparison with ten other approximation and local search algorithms, TBLS achieves the highest matching size, while TBLS-E exhibits the lowest sex equality cost. Significantly, TBLS-E preserves a matching size comparable to that of TBLS. Both our algorithms demonstrate faster computational speed than other local search algorithms in solving large-sized instances.