TY - JOUR
T1 - An improved two-step method for solving generalized Nash equilibrium problems
AU - Han, Deren
AU - Zhang, Hongchao
AU - Qian, Gang
AU - Xu, Lingling
PY - 2012/2/1
Y1 - 2012/2/1
N2 - The generalized Nash equilibrium problem (GNEP) is a noncooperative game in which the strategy set of each player, as well as his payoff function, depend on the rival players strategies. As a generalization of the standard Nash equilibrium problem (NEP), the GNEP has recently drawn much attention due to its capability of modeling a number of interesting conflict situations in, for example, an electricity market and an international pollution control. In this paper, we propose an improved two-step (a prediction step and a correction step) method for solving the quasi-variational inequality (QVI) formulation of the GNEP. Per iteration, we first do a projection onto the feasible set defined by the current iterate (prediction) to get a trial point; then, we perform another projection step (correction) to obtain the new iterate. Under certain assumptions, we prove the global convergence of the new algorithm. We also present some numerical results to illustrate the ability of our method, which indicate that our method outperforms the most recent projection-like methods of Zhang et al. (2010).
AB - The generalized Nash equilibrium problem (GNEP) is a noncooperative game in which the strategy set of each player, as well as his payoff function, depend on the rival players strategies. As a generalization of the standard Nash equilibrium problem (NEP), the GNEP has recently drawn much attention due to its capability of modeling a number of interesting conflict situations in, for example, an electricity market and an international pollution control. In this paper, we propose an improved two-step (a prediction step and a correction step) method for solving the quasi-variational inequality (QVI) formulation of the GNEP. Per iteration, we first do a projection onto the feasible set defined by the current iterate (prediction) to get a trial point; then, we perform another projection step (correction) to obtain the new iterate. Under certain assumptions, we prove the global convergence of the new algorithm. We also present some numerical results to illustrate the ability of our method, which indicate that our method outperforms the most recent projection-like methods of Zhang et al. (2010).
KW - Convex programming
KW - Generalized Nash equilibrium
KW - Projection methods
KW - Quasi-variational inequality problems
KW - Two-step methods
UR - https://www.scopus.com/pages/publications/80053916798
U2 - 10.1016/j.ejor.2011.08.008
DO - 10.1016/j.ejor.2011.08.008
M3 - 文章
AN - SCOPUS:80053916798
SN - 0377-2217
VL - 216
SP - 613
EP - 623
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -