July  2016, 12(3): 879-890. doi: 10.3934/jimo.2016.12.879

An augmented Lagrangian-based parallel splitting method for a one-leader-two-follower game

1. 

Department of Mathematics, Taiyuan Normal University, Taiyuan 030012, Shanxi Province, China

Received  February 2014 Revised  November 2014 Published  September 2015

In this paper, we exploit a new parallel splitting method for the typical structured variational inequality problems which can be interpreted as a game with a leader and two followers. In the framework of this method, two followers decide their strategies simultaneously based on the instruction of the leader. Then, the leader improves his instruction by revising his own variable value according to the feedback information from the followers. The convergence of the method is established under some suitable conditions. Finally, we apply the proposed method to solve some application problems. Computational studies show that the method is reliable and efficient.
Citation: Xihong Yan. An augmented Lagrangian-based parallel splitting method for a one-leader-two-follower game. Journal of Industrial & Management Optimization, 2016, 12 (3) : 879-890. doi: 10.3934/jimo.2016.12.879
References:
[1]

G. Chen and M. Teboulle, A proximal-based decomposition method for convex minimization problems, Mathematical Programming, 64 (1994), 81-101. doi: 10.1007/BF01582566.  Google Scholar

[2]

J. Eckstein and D. P. Bertsekas, On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators, Mathematical Programming, 55 (1992), 293-318. doi: 10.1007/BF01581204.  Google Scholar

[3]

M. Fukushima, Application of the alternating direction method of multipliers to separable convex programming problems, Computational Optimization and Applications, 1 (1992), 93-111. doi: 10.1007/BF00247655.  Google Scholar

[4]

D. Gabay and B. Mercier, A dual algorithm for the solution of nonlinear variational problems via finite element approximations, Computers and Mathematics with Applications, 2 (1976), 17-40. doi: 10.1016/0898-1221(76)90003-1.  Google Scholar

[5]

R. Glowinski and P. Le Tallec, Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics, SIAM Studies in Applied Mathematics, SIAM, Philadelphia, 1989. doi: 10.1137/1.9781611970838.  Google Scholar

[6]

D. Han, H. He, H. Yang and X. Yuan, A customized Douglas-Rachford splitting algorithm for separable convex minimization with linear constraints, Numerische Mathematik, 127 (2014), 167-200. doi: 10.1007/s00211-013-0580-2.  Google Scholar

[7]

B. S. He, Inexact implicit methods for monotone general variational inequalities, Mathematical Programming, 86 (1999), 199-217. doi: 10.1007/s101070050086.  Google Scholar

[8]

B. S. He, Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities, Computational Optimization and Applications, 42 (2009), 195-212. doi: 10.1007/s10589-007-9109-x.  Google Scholar

[9]

B. S. He, L. Z. Liao, D. Han and H. Yang, A new inexact alternating directions method for monotone variational inequalities, Mathematical Programming 92 (2002), 103-118. doi: 10.1007/s101070100280.  Google Scholar

[10]

B. S. He, Y. Xu and X. M. Yuan, A logarithmic-quadratic proximal prediction-correction method for structured monotone variational inequalities, Computational Optimization and Applications, 35 (2006), 19-46. doi: 10.1007/s10589-006-6442-4.  Google Scholar

[11]

S. Kontogiorgis and R. Meyer, A variable-penalty alternating directions method for convex optimization, Mathematical Programming, 83 (1998), 29-53. doi: 10.1007/BF02680549.  Google Scholar

[12]

A. Nagurney and D. Zhang, Projected Dynamical Systems and Variational Inequalities with Applications, Kluwer, Boston, 1996. doi: 10.1007/978-1-4615-2301-7.  Google Scholar

[13]

M. Tao and X. Yuan, An inexact parallel splitting augmented Lagrangian method for monotone variational inequalities with separable structures, Computational Optimization and Applications, 52 (2012), 439-461. doi: 10.1007/s10589-011-9417-z.  Google Scholar

[14]

P. Tseng, Alternating projection-proximal methods for convex programming and variational inequalities, SIAM Journal on Optimization, 7 (1997), 951-965. doi: 10.1137/S1052623495279797.  Google Scholar

[15]

K. Wang, L. Xu and D. Han, A new parallel splitting descent method for structured variational inequalities, Journal of Industrial and Management Optimization, 10 (2014), 461-476. doi: 10.3934/jimo.2014.10.461.  Google Scholar

show all references

References:
[1]

G. Chen and M. Teboulle, A proximal-based decomposition method for convex minimization problems, Mathematical Programming, 64 (1994), 81-101. doi: 10.1007/BF01582566.  Google Scholar

[2]

J. Eckstein and D. P. Bertsekas, On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators, Mathematical Programming, 55 (1992), 293-318. doi: 10.1007/BF01581204.  Google Scholar

[3]

M. Fukushima, Application of the alternating direction method of multipliers to separable convex programming problems, Computational Optimization and Applications, 1 (1992), 93-111. doi: 10.1007/BF00247655.  Google Scholar

[4]

D. Gabay and B. Mercier, A dual algorithm for the solution of nonlinear variational problems via finite element approximations, Computers and Mathematics with Applications, 2 (1976), 17-40. doi: 10.1016/0898-1221(76)90003-1.  Google Scholar

[5]

R. Glowinski and P. Le Tallec, Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics, SIAM Studies in Applied Mathematics, SIAM, Philadelphia, 1989. doi: 10.1137/1.9781611970838.  Google Scholar

[6]

D. Han, H. He, H. Yang and X. Yuan, A customized Douglas-Rachford splitting algorithm for separable convex minimization with linear constraints, Numerische Mathematik, 127 (2014), 167-200. doi: 10.1007/s00211-013-0580-2.  Google Scholar

[7]

B. S. He, Inexact implicit methods for monotone general variational inequalities, Mathematical Programming, 86 (1999), 199-217. doi: 10.1007/s101070050086.  Google Scholar

[8]

B. S. He, Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities, Computational Optimization and Applications, 42 (2009), 195-212. doi: 10.1007/s10589-007-9109-x.  Google Scholar

[9]

B. S. He, L. Z. Liao, D. Han and H. Yang, A new inexact alternating directions method for monotone variational inequalities, Mathematical Programming 92 (2002), 103-118. doi: 10.1007/s101070100280.  Google Scholar

[10]

B. S. He, Y. Xu and X. M. Yuan, A logarithmic-quadratic proximal prediction-correction method for structured monotone variational inequalities, Computational Optimization and Applications, 35 (2006), 19-46. doi: 10.1007/s10589-006-6442-4.  Google Scholar

[11]

S. Kontogiorgis and R. Meyer, A variable-penalty alternating directions method for convex optimization, Mathematical Programming, 83 (1998), 29-53. doi: 10.1007/BF02680549.  Google Scholar

[12]

A. Nagurney and D. Zhang, Projected Dynamical Systems and Variational Inequalities with Applications, Kluwer, Boston, 1996. doi: 10.1007/978-1-4615-2301-7.  Google Scholar

[13]

M. Tao and X. Yuan, An inexact parallel splitting augmented Lagrangian method for monotone variational inequalities with separable structures, Computational Optimization and Applications, 52 (2012), 439-461. doi: 10.1007/s10589-011-9417-z.  Google Scholar

[14]

P. Tseng, Alternating projection-proximal methods for convex programming and variational inequalities, SIAM Journal on Optimization, 7 (1997), 951-965. doi: 10.1137/S1052623495279797.  Google Scholar

[15]

K. Wang, L. Xu and D. Han, A new parallel splitting descent method for structured variational inequalities, Journal of Industrial and Management Optimization, 10 (2014), 461-476. doi: 10.3934/jimo.2014.10.461.  Google Scholar

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