Some iterative methods for finding fixed points and for solving constrained convex minimization problems

2011 ◽  
Vol 74 (16) ◽  
pp. 5286-5302 ◽  
Author(s):  
L.-C. Ceng ◽  
Q.H. Ansari ◽  
J.-C. Yao
Keyword(s):  
Fixed Points ◽  
Iterative Methods ◽  
Convex Minimization ◽  
Constrained Convex Minimization ◽  
Minimization Problems ◽  
Convex Minimization Problems

scholarly journals A Hybrid Gradient-Projection Algorithm for Averaged Mappings in Hilbert Spaces

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Ming Tian ◽  
Min-Min Li
Keyword(s):  
Hilbert Spaces ◽  
Gradient Projection ◽  
Convex Minimization ◽  
Projection Algorithm ◽  
Constrained Convex Minimization ◽  
Minimization Problems ◽  
Gradient Projection Algorithm ◽  
Convex Minimization Problems ◽  
Averaged Mappings ◽  
General Iterative Method

It is well known that the gradient-projection algorithm (GPA) is very useful in solving constrained convex minimization problems. In this paper, we combine a general iterative method with the gradient-projection algorithm to propose a hybrid gradient-projection algorithm and prove that the sequence generated by the hybrid gradient-projection algorithm converges in norm to a minimizer of constrained convex minimization problems which solves a variational inequality.

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.

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