Multiple-sets Split Feasibility Problem Research Articles

Convergence Theorems for the Variational Inequality Problems and Split Feasibility Problems in Hilbert Spaces

In this paper, we establish an iterative algorithm by combining Yamada’s hybrid steepest descent method and Wang’s algorithm for finding the common solutions of variational inequality problems and split feasibility problems. The strong convergence of the sequence generated by our suggested iterative algorithm to such a common solution is proved in the setting of Hilbert spaces under some suitable assumptions imposed on the parameters. Moreover, we propose iterative algorithms for finding the common solutions of variational inequality problems and multiple-sets split feasibility problems. Finally, we also give numerical examples for illustrating our algorithms.

Ball-relaxed projection algorithms for multiple-sets split feasibility problem

ABSTRACT The multiple-sets split feasibility problem (MSSFP) requires finding a point closet to a family of closed convex sets in one space such that its image under a linear transformation will be closest to another family of closed convex sets in the image space. Motivated by the ball-relaxed projection algorithm proposed by Yu et al. for the split feasibility problem (SFP), in this paper, we introduce ball-relaxed projection algorithms for solving the MSSFP. Instead of the level sets or half-spaces, our algorithms require computing the orthogonal projections onto closed balls. We establish weak and strong convergence of the proposed algorithms to a solution of the MSSFP. Finally, we provide preliminary numerical experiments to show the efficiency and the implementation of our method.

A Strong Convergence Theorem for an Iterative Method for Solving the Split Variational Inequalities in Hilbert Spaces

There are many iterative methods for solving the split variational inequality problems involving step sizes that depend on the norm of a bounded linear operator $\mathcal F$ . We know that the implementation of such algorithms is usually difficult to handle, because we have to compute the norm of the operator $\mathcal F$ . In this paper, we introduce a new iterative algorithm for approximating a solution of a class of multiple-sets split variational inequality problems, without prior knowledge of operator norms. Strong convergence of the iterative process is proved. As an application, we obtain a strong convergence result for a class of multiple-sets split feasibility problem. Two numerical examples are given to illustrate the proposed iterative algorithm.

A New Iterative Algorithm for the Multiple-Sets Split Feasibility Problem and the Split Equality Fixed Point Problem

In this paper, we propose a new iterative algorithm for solving the multiple-sets split feasibility problem and the split equality fixed point problem of firmly quasi-nonexpansive mappings in real Hilbert spaces. Under very mild conditions, we prove a weak convergence theorem for our algorithm using projection method and the properties of firmly quasi-nonexpansive mappings. Our result improves and extends the corresponding ones announced by some others in the earlier and recent literature.

An Inertial Generalized Viscosity Approximation Method for Solving Multiple-Sets Split Feasibility Problems and Common Fixed Point of Strictly Pseudo-Nonspreading Mappings

In this paper, we propose a generalized viscosity iterative algorithm which includes a sequence of contractions and a self adaptive step size for approximating a common solution of a multiple-set split feasibility problem and fixed point problem for countable families of k-strictly pseudononspeading mappings in the framework of real Hilbert spaces. The advantage of the step size introduced in our algorithm is that it does not require the computation of the Lipschitz constant of the gradient operator which is very difficult in practice. We also introduce an inertial process version of the generalize viscosity approximation method with self adaptive step size. We prove strong convergence results for the sequences generated by the algorithms for solving the aforementioned problems and present some numerical examples to show the efficiency and accuracy of our algorithm. The results presented in this paper extends and complements many recent results in the literature.

New iterative regularization methods for the multiple-sets split feasibility problem

In this paper, in order to solve the multiple-sets split feasibility problem in Hilbert spaces, we introduce the regularization methods of Lavrentiev’s and Bruck–Bakushinskii’s type, where the iterative parameter is chosen independently of the operator norm. We also consider some particular cases of the introduced methods. Applications and numerical experiments are given for illustration and comparison.

An inertial extrapolation method for multiple-set split feasibility problem

In this paper, we propose an iterative algorithm with inertial extrapolation to approximate the solution of multiple-set split feasibility problem. Based on Lopez et al. (Inverse Probl. 28(8):085004, 2012), we have developed a self-adaptive technique to choose the stepsizes such that the implementation of our algorithm does not need any prior information about the operator norm. We then prove the strong convergence of a sequence generated by our algorithm. We also present numerical examples to illustrate that the acceleration of our algorithm is effective.

Half-Space Relaxation Projection Method for Solving Multiple-Set Split Feasibility Problem

In this paper, we study an iterative method for solving the multiple-set split feasibility problem: find a point in the intersection of a finite family of closed convex sets in one space such that its image under a linear transformation belongs to the intersection of another finite family of closed convex sets in the image space. In our result, we obtain a strongly convergent algorithm by relaxing the closed convex sets to half-spaces, using the projection onto those half-spaces and by introducing the extended form of selecting step sizes used in a relaxed CQ algorithm for solving the split feasibility problem. We also give several numerical examples for illustrating the efficiency and implementation of our algorithm in comparison with existing algorithms in the literature.

Viscosity Self-Adaptive Method for Generalized Split System of Variational Inclusion Problem

In this paper, we propose a viscosity-type and self-adaptive iterative algorithm resulting in strong convergence theorem for finding the common solution of generalized split system of variational inclusion problem. The self-adaptive technique presented in this paper is a way of selecting the stepsizes such that the implementation of our algorithms does not need any prior information about the operator norms. As an application of our result, we study the strong convergence theorem to approximate the solution of generalized forms of multiple-set split feasibility problem, split system of equilibrium problem and split system of minimization problem. Our result generalize, improve and unify various results in the existing literature.

An iterative method for solving multiple-set split feasibility problems in Banach spaces

"In this paper, we study generalized multiple-set split feasibility problems (in short, GMSSFP) in the frame workof p-uniformly convex real Banach spaces which are also uniformly smooth. We construct an iterative algo-rithm which is free from an operator norm and prove its strong convergence to a solution of GMSSFP, thatis, a solution of convex problem and a common fixed point of a countable family of Bregman asymptoticallyquasi-nonexpansive mappings without requirement for semi-compactness on the mappings. We illustrate ouralgorithm and convergence result by a numerical example. "

Iterative algorithms for a multiple-sets split feasibility problem in Banach spaces

Iterative algorithms for a multiple-sets split feasibility problem in Banach spaces

Solving the multiple-set split feasibility problem and the equilibrium problem by a new relaxed CQ algorithm

In this paper, we introduce a new kind of a relaxed CQ algorithm to find the solution of the multiple-set split feasibility problem and the equilibrium problem in a Hilbert space. We prove weak and strong convergence theorems to the proposed algorithm under some mild conditions. Finally, we provide some numerical experiments to show the efficiency and the implementation of our method.

Strong Convergence of a New Generalized Viscosity Implicit Rule and Some Applications in Hilbert Space

In this paper, based on the very recent work by Nandal et al. (Nandal, A.; Chugh, R.; Postolache, M. Iteration process for fixed point problems and zeros of maximal monotone operators. Symmetry 2019, 11, 655.), we propose a new generalized viscosity implicit rule for finding a common element of the fixed point sets of a finite family of nonexpansive mappings and the sets of zeros of maximal monotone operators. Utilizing the main result, we first propose and investigate a new general system of generalized equilibrium problems, which includes several equilibrium and variational inequality problems as special cases, and then we derive an implicit iterative method to solve constrained multiple-set split convex feasibility problem. We further combine forward-backward splitting method and generalized viscosity implicit rule for solving monotone inclusion problem. Moreover, we apply the main result to solve convex minimization problem.

Linear Convergence of an Iterative Algorithm for Solving the Multiple-Sets Split Feasibility Problem

In this paper, we propose the simultaneous sub-gradient projection algorithm with the dynamic step size (SSPA for short) for solving the multiple-sets split feasibility problem (MSSFP for short) and investigate its linear convergence. We involve a notion of bounded linear regularity for the MSSFP and construct several sufficient conditions to prove the linear convergence for the SSPA. In particular, the SSPA is an easily calculated algorithm that uses orthogonal projection onto half-spaces. Furthermore, some numerical results are provided to verify the effectiveness of our proposed algorithm.

A Family of Projection Gradient Methods for Solving the Multiple-Sets Split Feasibility Problem

In the present paper, we explore a family of projection gradient methods for solving the multiple-sets split feasibility problem, which include the cyclic/simultaneous iteration methods introduced in Wen et al. (J Optim Theory Appl 166:844–860, 2015) as special cases. For the general case, where the involved sets are given by level sets of convex functions, the calculation of the projection onto the level sets is complicated in general, and thus, the resulting projection gradient method cannot be implemented easily. To avoid this difficulty, we introduce a family of relaxed projection gradient methods, in which the projections onto the approximated halfspaces are adopted in place of the ones onto the level sets. They cover the relaxed cyclic/simultaneous iteration methods introduced in Wen et al. (J Optim Theory Appl 166:844–860, 2015) as special cases. Global weak convergence theorems are established for these methods. In particular, as direct applications of the established theorems, our results fill some gaps and deal with the imperfections that appeared in Wen et al. (J Optim Theory Appl 166:844–860, 2015) and hence improve and extend the corresponding results therein.

A Strong Convergence Theorem for a Parallel Iterative Method for Solving the Split Common Null Point Problem in Hilbert Spaces

There are many iterative methods for solving the split common null point problems involving step sizes that depend on the norm of a bounded linear operator T. We know that the implementation of such algorithms is usually difficult to handle, because we have to compute the norm of the operator T. So, we propose new iterative methods involving a step size selected in such a way that its implementation does not require the computation or estimation of the norm of the operator T. In this paper, a new parallel iterative method for solving the split common null point problem is introduced in Hilbert spaces, without prior knowledge of operator norms. Moreover, some applications of our main results to the multiple-set split feasibility problem and the split minimum point problem are also presented.

Gradient methods with selection technique for the multiple-sets split feasibility problem

ABSTRACTIn this paper, we present two new iterative algorithms for approximating a solution of the multiple-sets split feasibility problem. The suggested algorithms are based on the gradient method with selection technique. Weak and strong convergence theorems are demonstrated.

Iterative Regularization Methods for the Multiple-Sets Split Feasibility Problem in Hilbert Spaces

In this paper, we introduce iterative regularization methods for solving the multiple-sets split feasibility problem, that is to find a point closest to a family of closed convex subsets in one space such that its image under a bounded linear mapping will be closest to another family of closed convex subsets in the image space. We consider the cases, when the families are either finite or infinite. We also give two numerical examples for illustrating our main method.

Hybrid iterative methods for multiple sets split feasibility problems

Hybrid iterative methods for multiple sets split feasibility problems

Relaxed CQ algorithms involving the inertial technique for multiple-sets split feasibility problems

In this work, we introduce the inertial relaxed CQ algorithms for solving the multiple-sets split feasibility problems (MSFP) in the frameworks of Hilbert spaces. By mixing the inertial technique with the self-adaptive method, not only the computation on the matrix norm and the orthogonal projection is relaxed but also the convergence speed is improved. We then establish the strong convergence theorem by combining the relaxed CQ algorithm with Halpern’s iteration process. Finally, we provide numerical experiments to illustrate the convergence behavior and the effectiveness of our proposed algorithm. The main result extends and improves the corresponding results.