An inertial projection and contraction method with a line search technique for variational inequality and fixed point problems

Optimization ◽  
2021 ◽  
pp. 1-30
Author(s):  
Lateef Olakunle Jolaoso
Keyword(s):  
Fixed Point ◽  
Variational Inequality ◽  
Line Search ◽  
Fixed Point Problems ◽  
Search Technique ◽  
Contraction Method ◽  
Projection And Contraction Method ◽  
Line Search Technique

scholarly journals Hybrid Inertial Accelerated Algorithms for Solving Split Equilibrium and Fixed Point Problems

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2680
Author(s):  
Yanlai Song
Keyword(s):  
Fixed Point ◽  
Hilbert Spaces ◽  
Line Search ◽  
Equilibrium Problems ◽  
Recent Literature ◽  
Extragradient Method ◽  
Fixed Point Problems ◽  
Strong Convergence Theorem ◽  
Search Technique ◽  
Line Search Technique

In this paper, we introduce a new hybrid inertial accelerated algorithm with a line search technique for solving fixed point problems for demimetric mapping and split equilibrium problems in Hilbert spaces. The algorithm is inspired by Tseng’s extragradient method and the viscosity method. Then, we establish and prove the strong convergence theorem under proper conditions. Furthermore, we also give a numerical example to support the main results. The main results are new and the proofs are relatively simple and different from those in early and recent literature.

scholarly journals A Generalized Viscosity Inertial Projection and Contraction Method for Pseudomonotone Variational Inequality and Fixed Point Problems

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2039 ◽  
Author(s):  
Lateef Olakunle Jolaoso ◽  
Maggie Aphane
Keyword(s):  
Fixed Point ◽  
Variational Inequalities ◽  
Adaptive Method ◽  
Convergence Result ◽  
Null Point ◽  
Fixed Point Problems ◽  
Bounded Linear ◽  
Contraction Method ◽  
Projection And Contraction Method ◽  
Self Adaptive

We introduce a new projection and contraction method with inertial and self-adaptive techniques for solving variational inequalities and split common fixed point problems in real Hilbert spaces. The stepsize of the algorithm is selected via a self-adaptive method and does not require prior estimate of norm of the bounded linear operator. More so, the cost operator of the variational inequalities does not necessarily needs to satisfies Lipschitz condition. We prove a strong convergence result under some mild conditions and provide an application of our result to split common null point problems. Some numerical experiments are reported to illustrate the performance of the algorithm and compare with some existing methods.

scholarly journals A Nonmonotone Weighting Self-Adaptive Trust Region Algorithm for Unconstrained Nonconvex Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yunlong Lu ◽  
Weiwei Yang ◽  
Wenyu Li ◽  
Xiaowei Jiang ◽  
Yueting Yang
Keyword(s):  
Nonconvex Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Search Technique ◽  
Trust Region Algorithm ◽  
Unconstrained Optimization Problems ◽  
Line Search Technique ◽  
Self Adaptive

A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction. Under reasonable assumptions, the global convergence of the method is established for unconstrained nonconvex optimization. Numerical results show that the new method is efficient and robust for solving unconstrained optimization problems.

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