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Strong convergence inertial projection algorithm with self-adaptive step size rule for pseudomonotone variational inequalities in Hilbert spaces

Wairojjana Nopparat, Pakkaranang Nuttapol, Pholasa Nattawut

Demonstratio Mathematica

2021

Vol. 54, nr 1

110--128

In this paper, we introduce a new algorithm for solving pseudomonotone variational inequalities with a Lipschitz-type condition in a real Hilbert space. The algorithm is constructed around two algorithms: the subgradient extragradient algorithm and the inertial algorithm. The proposed algorithm uses a new step size rule based on local operator information rather than its Lipschitz constant or any other line search scheme and functions without any knowledge of the Lipschitz constant of an operator. The strong convergence of the algorithm is provided. To determine the computational performance of our algorithm, some numerical results are presented.

Strong convergence theorem of a hybrid projection algorithm for a family of quasi-φ-asymptotically nonexpansive mappings

Tang J. F., Chang S. S., Liu M., Liu J. A.

Opuscula Mathematica

2010

Vol. 30, no. 3

341-348

The main purpose of this paper is by using a new hybrid projection iterative algorithm to prove some strong convergence theorems for a family of quasi-φ-asymptotically nonexpansive mappings. The results presented in the paper improve and extend the corresponding results announced by some authors.