convex minimization
Extragradient method for convex minimization problem
... the convex minimization problem (CMP) with constraints of several problems: finitely many generalized mixed equilibrium problems (GMEPs), finitely many variational inclusions, and the fixed point problem of a ...
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General iterative scheme based on the regularization for solving a constrained convex minimization problem
... constrained convex minimization problem and prove that the methods converge strongly to a solution of the minimization problem, which is also a solution of the variational ...
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An iterative algorithm for fixed point problem and convex minimization problem with applications
... constrained convex minimization problem and prove that the sequences generated by their schemes converge strongly to a solution of the constrained convex minimization problem (see [] for ...
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Regularized gradient projection methods for equilibrium and constrained convex minimization problems
... constrained convex minimiza- tion problem have been extensively studied, respectively, in a Hilbert ...strained convex minimization ...constrained convex minimization problem, which ...
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An intermixed iteration for constrained convex minimization problem and split feasibility problem
... The purpose of this article is to combine the GPA and averaged mapping approach to design a two-step intermixed iteration for finding the common solution of a constrained convex minimization problem, and ...
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Iterative methods for constrained convex minimization problem in Hilbert spaces
... In this paper, based on Yamada’s hybrid steepest descent method, a general iterative method is proposed for solving constrained convex minimization problem. It is proved that the sequences generated by ...
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Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator
... In this section, we present the following concrete examples to judge the numerical per- formance of our algorithm. By using the algorithm in Theorem . and Theorem ., we illustrate its realization, effectiveness, and ...
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Regularized gradient projection methods for finding the minimum norm solution of the constrained convex minimization problem
... In a real Hilbert space, there are many methods to solve the constrained convex mini- mization problem. However, most of them cannot find the minimum-norm solution. In this article, we use the regularized ...
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On the global and linear convergence of direct extension of ADMM for 3 block separable convex minimization models
... separable convex minimization problems, it is convergent if one block in the objective possesses sub-strong monotonicity which is weaker than strong ...
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Iterative algorithms with regularization for hierarchical variational inequality problems and convex minimization problems
... In this paper, we consider a variational inequality problem which is defined over the set of intersections of the set of fixed points of a ζ -strictly pseudocontractive mapping, the set of fixed points of a nonexpansive ...
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A Relaxed Explicit Extragradient-Like Method for Solving Generalized Mixed Equilibria, Variational Inequalities and Constrained Convex Minimization
... the minimization problem for a convex and continuously Fr´ echet dif- ferentiable functional, the set of solutions of a finite family of generalized mixed equilibrium problems and the set of solutions of a ...
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The generalized viscosity implicit rules of nonexpansive mappings in Hilbert spaces
... The organization of this paper is as follows. In Section , we recall the notion of the metric projection, the demiclosedness principle of nonexpansive mappings and a conver- gence lemma. In Section , the strong ...
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A generalized descent method for global optimization
... For a minimizer of a convex minimization problem with a differentiable objective function we have the condition that the inner product between the local gradient and any direction contai[r] ...
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Acceleration of the Halpern algorithm to search for a fixed point of a nonexpansive mapping
... smooth convex minimization problem, which is an example of a fixed point problem for a nonexpansive mapping, and indicate that the Halpern algorithm is based on the steepest descent method for solving the ...
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Convergence analysis on a modified generalized alternating direction method of multipliers
... Compared with the traditional proximal approach (12a)–(12d), the semi-proximal terms in (13a)–(13d) are more natural in the sense that the most recently updated values of vari- ables are involved. Actually, the global ...
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An iterative method for a common solution of generalized mixed equilibrium problems, variational inequalities, and hierarchical fixed point problems
... the convex minimization problem, the set of solutions of a fi- nite family of generalized mixed equilibrium problems, and the set of solutions of a finite family of variational inequality problems for inverse ...
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Solving total variation image super resolution problems via proximal symmetric alternating direction methods
... Keywords: proximal symmetric alternating direction method of multipliers; linearized Peaceman-Rechford splitting method; convex minimization; strictly contractive; single image super-res[r] ...
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Convergence theorems for split equality generalized mixed equilibrium problems for demi contractive mappings
... In this paper, we introduce a new iterative algorithm for solving the split equality generalized mixed equilibrium problems. The weak and strong convergence theorems are proved for demi-contractive mappings in real ...
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Relaxed iterative algorithms for a system of generalized mixed equilibrium problems and a countable family of totally quasi-Phi-asymptotically nonexpansive multi-valued maps, with applications
... The class of generalized mixed equilibrium problems includes, as special cases, the class of mixed equilibrium problems (A ≡ ; see, e.g., Ceng and Yao [] and the references con- tained therein); the class of ...
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Convex separable minimization problems with a linear constraint and bounded variables
... a convex function but not necessarily a strictly convex function, then, as it is known, any local minimum point of c(x) is a global minimum point as well and the set of optimal solutions to a convex ...
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