A kind of inexact parallel splitting method for solving the fairest core in cooperative game

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  • 1. Department of Mathematics and System Science, College of Science, National University of Defense Technology, Changsha 410073, China

Received date: 2015-11-02

  Online published: 2016-06-15

Abstract

In this paper, considering the characteristics of the core and the Shapley value in cooperative game, we transform the fairest core problem into a separable convex optimization problem with two variable. A kind of inexact parallel splitting method is proposed for solving the fairest core by introducing the operator splitting method framework of structured variational inequalities. Furthermore, the proposed method makes full use of the simple closed convexity of the feasible region in the solved problem, and all sub-problems are easy to be solved inexactly. Finally, some numerical results of a simple example indicate the convergence and validity of this method.

Cite this article

WANG Siqi, XIE Zheng, DAI Li . A kind of inexact parallel splitting method for solving the fairest core in cooperative game[J]. Operations Research Transactions, 2016 , 20(2) : 105 -112 . DOI: 10.15960/j.cnki.issn.1007-6093.2016.02.010

References

[1] 谢政. 对策论导论 [M]. 北京: 科学出版社, 2010.
[2] Peleg B, Sudh\"{o}lter P. Introduction to the Theory of Cooperative Games [M]. Berlin: Springer, 2007.
[3] Nguyen T D. The fairest core in cooperative games with transferable utilities [J]. Operations Research Letters, 2015, 43(1): 34-39.
[4] Lemke C E. Bimatrix equilibrium points and mathematical programming [J]. Management Science, 1965, 11: 681-689.
[5] Monteiro D C, Adler I. Interior path following primal-dual algorithms. Part II: Convex quadratic programming [J]. Mathematical Programming, 1989, 44(1): 43-66.
[6] Fletcher R. A general quadratic programming algorithm [J]. Journal of the Institute of Mathematics and its Applications, 1971, 7: 76-91.
[7] He B S. A projection and contraction method for a class of linear complementarity problem and its application in convex quadratic programming [J]. Applied Mathematics and Optimization, 1992, 25(3): 247-262.
[8] He B S, Yuan X M, Zhang W X. A customized proximal point algorithm for convex minimization with linear constraints [J]. Computation Optimization and Applications, 2013, 56(3): 559-572.
[9] Ye C H, Yuan X M. A descent method for structured monotone variational inequalities [J]. Optimization Methods and Software, 2007, 22(2): 329-338.
[10] He B S. Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities [J]. Computation Optimization and Applications, 2009, 42(2): 195-212.
[11] Tao M, Yuan X M. An inexact parallel splitting augmented Lagrangian method for monotone variational inequalities with separable structures [J]. Computation Optimization and Applications, 2012, 52(2): 439-461.
[12] 杨赟, 彭拯. 可分离凸优化问题的非精确平行分裂算法 [J]. 运筹学学报, 2014, 18(3): 33-46.
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