Inertial Subgradient Projection Algorithms Extended to Equilibrium Problems

In this paper, we introduce a new inertial subgradient projection algorithm for finding a solution of an equilibrium problem in a real Hilbert space. The algorithm combines subgradient projection methods with the self-adaptive and inertial techniques to generate an iteration sequence converging weakly to a solution of the equilibrium problem. Based on the proposed algorithm, we develop a modified algorithm for finding a common solution of the equilibrium problem and fixed point problems. Several of fundamental experiments are showed to illustrate our algorithms and compare with some others.