A general regularized gradient-projection method for solving equilibrium and constrained convex minimization problems

In this article, we provide a general iterative method for solving an equilibrium and a constrained convex minimization problem. By using the idea of regularized gradient-projection algorithm (RGPA), we find a common element, which is also a solution of a variational inequality problem. Then the str...

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Vydáno v:Optimization Ročník 65; číslo 11; s. 2007 - 2024
Hlavní autoři: Tian, Ming, Jiao, Si-Wen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 01.11.2016
Taylor & Francis LLC
Témata:
Algorithms
constrained convex minimization
Constraints
Convergence
Equilibrium
fixed point
general iterative algorithms
Inequalities
Iterative methods
Minimization
Optimization
Portfolio management
Regularization methods
Theorems
Variational inequality
ISSN:0233-1934, 1029-4945
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Shrnutí:In this article, we provide a general iterative method for solving an equilibrium and a constrained convex minimization problem. By using the idea of regularized gradient-projection algorithm (RGPA), we find a common element, which is also a solution of a variational inequality problem. Then the strong convergence theorems are obtained under suitable conditions.
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2016.1193738