scholarly journals A General Iterative Scheme Based on Regularization for Solving Equilibrium and Constrained Convex Minimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Ming Tian
Keyword(s):  
Equilibrium Problem ◽  
Split Feasibility Problem ◽  
Convex Minimization ◽  
Feasibility Problem ◽  
Constrained Convex Minimization ◽  
Common Solution ◽  
Minimization Problems ◽  
Convex Minimization Problems ◽  
Implicit And Explicit ◽  
Split Feasibility

The present paper is divided into two parts. First, we introduce implicit and explicit iterative schemes based on the regularization for solving equilibrium and constrained convex minimization problems. We establish results on the strong convergence of the sequences generated by the proposed schemes to a common solution of minimization and equilibrium problem. Such a point is also a solution of a variational inequality. In the second part, as applications, we apply the algorithm to solve split feasibility problem and equilibrium problem.

New iterative methods for equilibrium and constrained convex minimization problems

2019 ◽  
Vol 12 (03) ◽  
pp. 1950042 ◽  
Author(s):  
Maryam Yazdi
Keyword(s):  
Convex Minimization ◽  
Projection Algorithm ◽  
Averaged Mapping ◽  
Convex Minimization Problem ◽  
Constrained Convex Minimization ◽  
Common Solution ◽  
Minimization Problems ◽  
Convex Minimization Problems ◽  
Constrained Convex Minimization Problem ◽  
Implicit And Explicit

The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, we combine the GPA and averaged mapping approach to propose implicit and explicit composite iterative schemes for finding a common solution of an equilibrium problem and a constrained convex minimization problem. Then, we prove some strong convergence theorems which improve and extend some recent results.

scholarly journals A new iterative method for generalized equilibrium and constrained convex minimization problems

2020 ◽  
Vol 74 (2) ◽  
pp. 81
Author(s):  
M. Yazdi
Keyword(s):  
Convex Minimization ◽  
Projection Algorithm ◽  
Strong Convergence Theorem ◽  
Constrained Convex Minimization ◽  
Common Solution ◽  
Minimization Problems ◽  
Convex Minimization Problems ◽  
Constrained Convex Minimization Problem ◽  
Generalized Equilibrium ◽  
New Iterative Method

The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, we combine the GPA and averaged mapping approach to propose an explicit composite iterative scheme for finding a common solution of a generalized equilibrium problem and a constrained convex minimization problem. Then, we prove a strong convergence theorem which improves and extends some recent results.

scholarly journals Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Ming Tian ◽  
Jun-Ying Gong
Keyword(s):  
Minimization Problem ◽  
Iterative Algorithms ◽  
Split Feasibility Problem ◽  
Convex Minimization ◽  
Feasibility Problem ◽  
Convex Minimization Problem ◽  
Constrained Convex Minimization ◽  
Constrained Convex Minimization Problem ◽  
The Split Feasibility Problem ◽  
Implicit And Explicit

As is known, the regularization method plays an important role in solving constrained convex minimization problems. Based on the idea of regularization, implicit and explicit iterative algorithms are proposed in this paper and the sequences generated by the algorithms can converge strongly to a solution of the constrained convex minimization problem, which also solves a certain variational inequality. As an application, we also apply the algorithm to solve the split feasibility problem.

scholarly journals An intermixed iteration for constrained convex minimization problem and split feasibility problem

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Kanyanee Saechou ◽  
Atid Kangtunyakarn
Keyword(s):  
Strong Convergence ◽  
Minimization Problem ◽  
Convergence Theorem ◽  
Split Feasibility Problem ◽  
Convex Minimization ◽  
Feasibility Problem ◽  
Strong Convergence Theorem ◽  
Convex Minimization Problem ◽  
Constrained Convex Minimization Problem ◽  
Split Feasibility

Abstract In this paper, we first introduce the two-step intermixed iteration for finding the common solution of a constrained convex minimization problem, and also we prove a strong convergence theorem for the intermixed algorithm. By using our main theorem, we prove a strong convergence theorem for the split feasibility problem. Finally, we apply our main theorem for the numerical example.

Sign in / Sign up

Export Citation Format

Share Document