[PDF] Top 20 Extragradient method for convex minimization problem
Has 10000 "Extragradient method for convex minimization problem" found on our website. Below are the top 20 most common "Extragradient method for convex minimization problem".
Extragradient method for convex minimization problem
... In , Huang [] studied problem (.) in the case where R is maximal monotone and B is strongly monotone and Lipschitz-continuous with D(R) = C = H. Subsequently, Zeng et al. [] further studied ... See full document
40
Weak convergence theorem for variational inequality problems with monotone mapping in Hilbert space
... the extragradient method, we introduce an iterative method for finding an element of the set of solutions of a variational inequality problem for a monotone and Lipschitz continuous mapping in ... See full document
12
An iterative method for a common solution of generalized mixed equilibrium problems, variational inequalities, and hierarchical fixed point problems
... of problem (.) and the fixed point problem of finitely many nonexpansive ...Korpelevič’s extragradient method [], the hybrid steepest-descent method in [], the viscosity approximation ... See full document
25
An intermixed iteration for constrained convex minimization problem and split feasibility problem
... iterative method for finding the common element of the set of solutions to an equilibrium problem and the solution set to a constrained convex minimization problem, as well as proved a ... See full document
22
Convex separable minimization problems with a linear constraint and bounded variables
... arbitrary convex or concave objective functions is studied in [2, 16, 20, 25, 31], and so ...solving convex quadratic minimization problems with a linear equality/inequality constraint and box ... See full document
25
An iterative algorithm for fixed point problem and convex minimization problem with applications
... gradient method is at best ...Newton method and the inte- rior point method, which uses Newton’s ...gradient method is that it can be very slow when the gradient directions are almost ... See full document
17
Iterative algorithms based on the viscosity approximation method for equilibrium and constrained convex minimization problem
... be a bifunction from C × C into R satisfying (A), (A), (A), and (A). Let g : C → R be a real-valued convex function, and assume that ∇g is an L-Lipschitzian mapping with L ≥ and f : C → C is a contraction ... See full document
17
Regularized gradient projection methods for finding the minimum norm solution of the constrained convex minimization problem
... Let H be a real Hilbert space with inner product · , · and norm · . Let C be a nonempty closed convex subset of H. Let N and R denote the sets of positive integers and real num- bers. Suppose that f is a ... See full document
12
Iterative methods for constrained convex minimization problem in Hilbert spaces
... descent method, a general iterative method is proposed for solving constrained convex minimization ...constrained convex minimization problem, which also solves a certain ... See full document
18
A New Approximation Scheme Combining the Viscosity Method with Extragradient Method for Mixed Equilibrium Problems
... Theorem 4.4. Let C be a nonempty closed convex subset of a real Hilbert space H. Let F be a bifunction from C × C to R satisfying A1–A5 and let ϕ : C → R ∪ {∞} be a proper lower semicontinuous and convex ... See full document
25
Convergence theorems of subgradient extragradient algorithm for solving variational inequalities and a convex feasibility problem
... this extragradient method is that one needs to calculate two projections onto C in each iteration ...and convex set, this iteration process might require a huge amount of computation ...subgradient ... See full document
14
Convergence analysis on a modified generalized alternating direction method of multipliers
... The alternating direction method of multipliers (ADMM) is one of the most powerful and successful methods for solving convex composite minimization problem. The generalized ADMM relaxes both ... See full document
14
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 ... See full document
11
Simultaneous extragradient iterative method to a split equality variational inequality problem and a multiple-sets split equality fixed point problem for multi-valued demicontractive mappings
... Corollary 4.1. Let H 1 , H 2 and H 3 be real Hilbert spaces and C ⊂ H 1 , Q ⊂ H 2 be nonempty, closed and convex sets. Let A : H 1 → H 3 , B : H 2 → H 3 be bounded linear operators with their adjoint operators A ∗ ... See full document
24
Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator
... On the other hand, based on Theorem . and Theorem ., we will give another two applications of it. In , Censor and Elfving [] introduced the split feasibility problem (SFP). Then various algorithms were ... See full document
23
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] ... See full document
143
General iterative scheme based on the regularization for solving a constrained convex minimization problem
... regularization method plays an important role in solving a constrained convex minimization ...constrained convex minimization ... See full document
15
Accelerated Mann and CQ algorithms for finding a fixed point of a nonexpansive mapping
... smooth convex minimization problem and point out that the Picard algorithm is the steepest descent method for solving the minimization ... See full document
12
A Relaxed Explicit Extragradient-Like Method for Solving Generalized Mixed Equilibria, Variational Inequalities and Constrained Convex Minimization
... plicit extragradient-like scheme for finding a common element of the set of solutions of the minimization problem for a convex and continuously Fr´ echet dif- ferentiable functional, the set ... See full document
5
Strong convergence of a modified iterative algorithm for hierarchical fixed point problems and variational inequalities
... It is well known that the iterative methods for finding hierarchical fixed points of non- expansive mappings can also be used to solve a convex minimization problem; see, for example, [, ] and the ... See full document
9
Related subjects