151 research outputs found
Strong convergence of inertial extragradient algorithms for solving variational inequalities and fixed point problems
The paper investigates two inertial extragradient algorithms for seeking a
common solution to a variational inequality problem involving a monotone and
Lipschitz continuous mapping and a fixed point problem with a demicontractive
mapping in real Hilbert spaces. Our algorithms only need to calculate the
projection on the feasible set once in each iteration. Moreover, they can work
well without the prior information of the Lipschitz constant of the cost
operator and do not contain any line search process. The strong convergence of
the algorithms is established under suitable conditions. Some experiments are
presented to illustrate the numerical efficiency of the suggested algorithms
and compare them with some existing ones.Comment: 25 pages, 12 figure
Weak convergence for variational inequalities with inertial-type method
Weak convergence of inertial iterative method for solving variational inequalities is the focus of this paper. The cost function is assumed to be non-Lipschitz and monotone. We propose a projection-type method with inertial terms and give weak convergence analysis under appropriate conditions. Some test results are performed and compared with relevant methods in the literature to show the efficiency and advantages given by our proposed methods
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