Subgradient Extragradient Method with Double Inertial Steps for Variational Inequalities

In this paper, we obtain successively weak, strong and linear convergence analysis of the sequence of iterates generated by our proposed subgradient extragradient method with double inertial extrapolation steps and self-adaptive step sizes for solving variational inequalities for which the cost operator is pseudo-monotone and Lipschitz continuous in real Hilbert spaces. Our proposed method is a combination of double inertial extrapolation steps, relaxation step and subgradient extragradient method which is aimed to increase the speed of convergence of many available subgradient extragradient methods with inertia for solving variational inequalities. Several versions of subgradient extragradient methods with inertial extrapolation step serve as special cases of our proposed method and the inertia in our proposed method is more relaxed and chosen in [0, 1]. Numerical implementations of our method show that our method is efficient, implementable and the benefits gained when subgradient extragradient method with double inertial extrapolation steps are considered for variational inequalities instead of subgradient extragradient methods with one inertial extrapolation step available in the literature.