The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
Stable matchings, optimal assignments, and linear programming
Mathematics of Operations Research
Stable marriage and indifference
CO89 Selected papers of the conference on Combinatorial Optimization
The Geometry of Fractional Stable Matchings and its Applications
Mathematics of Operations Research
Hard variants of stable marriage
Theoretical Computer Science
Gale-Shapley Stable Marriage Problem Revisited: Strategic Issues and Applications
Proceedings of the 7th International IPCO Conference on Integer Programming and Combinatorial Optimization
Stable Marriage with Incomplete Lists and Ties
ICAL '99 Proceedings of the 26th International Colloquium on Automata, Languages and Programming
Randomized approximation of the stable marriage problem
Theoretical Computer Science - Special papers from: COCOON 2003
IEICE - Transactions on Information and Systems
Improved approximation results for the stable marriage problem
ACM Transactions on Algorithms (TALG)
A 1.875: approximation algorithm for the stable marriage problem
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Vertex cover might be hard to approximate to within 2-ε
Journal of Computer and System Sciences
Stable marriage with ties and bounded length preference lists
Journal of Discrete Algorithms
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Journal of Discrete Algorithms
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The problem of finding a largest stable matching where preference lists may include ties and unacceptable partners (MAX SMTI) is known to be NP-hard. It cannot be approximated within 33/29 ( 1.1379) unless P=NP, and the current best approximation algorithm achieves the ratio of 1.5. MAX SMTI remains NP-hard even when preference lists of one side do not contain ties, and it cannot be approximated within 21/19 ( 1.1052) unless P=NP. However, even under this restriction, the best known approximation ratio is still 1.5. In this paper, we improve it to 25/17 (