TY - JOUR
T1 - Computing the Least-Core and Nucleolus for Threshold Cardinality Matching Games
AU - Fang, Qizhi
AU - Li, B.
AU - Sun, Xiaoming
AU - Zhang, Jia
AU - Zhang, Jialin
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - In this paper, we study the algorithmic issues on the leastcore and nucleolus of threshold cardinality matching games (TCMG). We first show that for a TCMG, the problems of computing least-core value, finding and verifying least-core payoff are all polynomial time solvable. We also provide a general characterization of the least core for a large class of TCMG. Next, based on Gallai-Edmonds Decomposition in matching theory, we give a concise formulation of the nucleolus for a typical case of TCMG which the threshold T equals 1. When the threshold T is relevant to the input size, we prove that the nucleolus can be obtained in polynomial time in bipartite graphs and graphs with a perfect matching.
AB - In this paper, we study the algorithmic issues on the leastcore and nucleolus of threshold cardinality matching games (TCMG). We first show that for a TCMG, the problems of computing least-core value, finding and verifying least-core payoff are all polynomial time solvable. We also provide a general characterization of the least core for a large class of TCMG. Next, based on Gallai-Edmonds Decomposition in matching theory, we give a concise formulation of the nucleolus for a typical case of TCMG which the threshold T equals 1. When the threshold T is relevant to the input size, we prove that the nucleolus can be obtained in polynomial time in bipartite graphs and graphs with a perfect matching.
UR - http://www.scopus.com/inward/record.url?scp=84914127396&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13129-0_42
DO - 10.1007/978-3-319-13129-0_42
M3 - Journal article
AN - SCOPUS:84914127396
SN - 0302-9743
VL - 8877
SP - 474
EP - 479
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -