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Journal ArticleDOI: 10.1080/02331934.2020.1723586

Modified inertial subgradient extragradient method with self adaptive stepsize for solving monotone variational inequality and fixed point problems

04 Mar 2021-Optimization (Taylor and Francis Ltd.)-Vol. 70, Iss: 3, pp 545-574
Abstract: In this paper, we study a classical monotone and Lipschitz continuous variational inequality and fixed point problems defined on a level set of a convex function in the setting of Hilbert space. We...

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Topics: Subgradient method (63%), Variational inequality (62%), Fixed point (61%) ... show more
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44 results found


Journal ArticleDOI: 10.1007/S11075-020-00937-2
Abstract: In this paper, we study the split common fixed point and monotone variational inclusion problem in uniformly convex and 2-uniformly smooth Banach spaces. We propose a Halpern-type algorithm with two self-adaptive stepsizes for obtaining solution of the problem and prove strong convergence theorem for the algorithm. Many existing results in literature are derived as corollary to our main result. In addition, we apply our main result to split common minimization problem and fixed point problem and illustrate the efficiency and performance of our algorithm with a numerical example. The main result in this paper extends and generalizes many recent related results in the literature in this direction.

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Topics: Banach space (56%), Monotone polygon (55%)

38 Citations


Journal ArticleDOI: 10.1080/02331934.2020.1808648
06 Sep 2020-Optimization
Abstract: In this paper, we propose a new inertial extrapolation method for solving the generalized split feasibility problems over the solution set of monotone variational inclusion problems in real Hilbert...

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Topics: Solution set (58%), Variational inequality (56%), Monotone polygon (55%)

28 Citations


Journal ArticleDOI: 10.1007/S12215-020-00505-6
01 Apr 2021-
Abstract: In this paper, we introduce a new algorithm with self adaptive step-size for finding a common solution of a split feasibility problem and a fixed point problem in real Hilbert spaces. Motivated by the self adaptive step-size method, we incorporate the self adaptive step-size to overcome the difficulty of having to compute the operator norm in the proposed method. Under standard and mild assumption on the control sequences, we establish the strong convergence of the algorithm, obtain a common element in the solution set of a split feasibility problem for sum of two monotone operators and fixed point problem of a demimetric mapping. Numerical examples are presented to illustrate the performance and the behavior of our method. Our result extends, improves and unifies other results in the literature.

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Topics: Fixed point (58%), Operator norm (53%), Solution set (52%) ... show more

20 Citations


Open accessJournal ArticleDOI: 10.3934/JIMO.2020092
Abstract: We propose a parallel iterative scheme with viscosity approximation method which converges strongly to a solution of the multiple-set split equality common fixed point problem for quasi-pseudocontractive mappings in real Hilbert spaces. We also give an application of our result to approximation of minimization problem from intensity-modulated radiation therapy. Finally, we present numerical examples to demonstrate the behaviour of our algorithm. This result improves and generalizes many existing results in literature in this direction.

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Topics: Hilbert space (55%), Viscosity (programming) (51%)

20 Citations


Journal ArticleDOI: 10.1007/S11784-020-00834-0
Abstract: In solving the split variational inequality problems in real Hilbert spaces, the co-coercive assumption of the underlying operators is usually required and this may limit its usefulness in many applications. To have these operators freed from the usual and restrictive co-coercive assumption, we propose a method for solving the split variational inequality problem in two real Hilbert spaces without the co-coerciveness assumption on the operators. We prove that the proposed method converges strongly to a solution of the problem and give some numerical illustrations of it in comparison with other methods in the literature to support our strong convergence result.

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19 Citations


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47 results found


Journal ArticleDOI: 10.1137/080716542
Amir Beck1, Marc TeboulleInstitutions (1)
Abstract: We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale problems even with dense matrix data. However, such methods are also known to converge quite slowly. In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. Initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of FISTA which is shown to be faster than ISTA by several orders of magnitude.

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Topics: Rate of convergence (54%), Deblurring (53%), Sparse matrix (51%) ... show more

9,684 Citations


Open accessBook
01 Jan 1980-
Abstract: Preface to the SIAM edition Preface Glossary of notations Introduction Part I. Variational Inequalities in Rn Part II. Variational Inequalities in Hilbert Space Part III. Variational Inequalities for Monotone Operators Part IV. Problems of Regularity Part V. Free Boundary Problems and the Coincidence Set of the Solution Part VI. Free Boundary Problems Governed by Elliptic Equations and Systems Part VII. Applications of Variational Inequalities Part VIII. A One Phase Stefan Problem Bibliography Index.

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3,900 Citations


Journal ArticleDOI: 10.1063/1.2810363
01 Jan 1990-Physics Today
Abstract: These six volumes - the result of a ten year collaboration between the authors, two of France's leading scientists and both distinguished international figures - compile the mathematical knowledge required by researchers in mechanics, physics, engineering, chemistry and other branches of application of mathematics for the theoretical and numerical resolution of physical models on computers. Since the publication in 1924 of the Methoden der mathematischen Physik by Courant and Hilbert, there has been no other comprehensive and up-to-date publication presenting the mathematical tools needed in applications of mathematics in directly implementable form. The advent of large computers has in the meantime revolutionised methods of computation and made this gap in the literature intolerable: the objective of the present work is to fill just this gap. Many phenomena in physical mathematics may be modeled by a system of partial differential equations in distributed systems: a model here means a set of equations, which together with given boundary data and, if the phenomenon is evolving in time, initial data, defines the system. The advent of high-speed computers has made it possible for the first time to caluclate values from models accurately and rapidly. Researchers and engineers thus have a crucial means of using numerical results to modify and adapt arguments and experiments along the way. Every fact of technical and industrial activity has been affected by these developments. Modeling by distributed systems now also supports work in many areas of physics (plasmas, new materials, astrophysics, geophysics), chemistry and mechanics and is finding increasing use in the life sciences. Volumes 5 and 6 cover problems of Transport and Evolution.

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Topics: Physical mathematics (56%), Computation (51%)

2,136 Citations


Journal ArticleDOI: 10.1016/0041-5553(64)90137-5
Abstract: For the solution of the functional equation P (x) = 0 (1) (where P is an operator, usually linear, from B into B, and B is a Banach space) iteration methods are generally used. These consist of the construction of a series x0, …, xn, …, which converges to the solution (see, for example [1]). Continuous analogues of these methods are also known, in which a trajectory x(t), 0 ⩽ t ⩽ ∞ is constructed, which satisfies the ordinary differential equation in B and is such that x(t) approaches the solution of (1) as t → ∞ (see [2]). We shall call the method a k-step method if for the construction of each successive iteration xn+1 we use k previous iterations xn, …, xn−k+1. The same term will also be used for continuous methods if x(t) satisfies a differential equation of the k-th order or k-th degree. Iteration methods which are more widely used are one-step (e.g. methods of successive approximations). They are generally simple from the calculation point of view but often converge very slowly. This is confirmed both by the evaluation of the speed of convergence and by calculation in practice (for more details see below). Therefore the question of the rate of convergence is most important. Some multistep methods, which we shall consider further, which are only slightly more complicated than the corresponding one-step methods, make it possible to speed up the convergence substantially. Note that all the methods mentioned below are applicable also to the problem of minimizing the differentiable functional (x) in Hilbert space, so long as this problem reduces to the solution of the equation grad (x) = 0.

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Topics: Rate of convergence (56%), Functional equation (56%), Differential equation (53%) ... show more

1,591 Citations


Open accessBook
25 Sep 2001-
Abstract: Introduction: Notation, Elementary Results.- Convex Sets: Generalities Convex Sets Attached to a Convex Set Projection onto Closed Convex Sets Separation and Applications Conical Approximations of Convex Sets.- Convex Functions: Basic Definitions and Examples Functional Operations Preserving Convexity Local and Global Behaviour of a Convex Function First- and Second-Order Differentiation.- Sublinearity and Support Functions: Sublinear Functions The Support Function of a Nonempty Set Correspondence Between Convex Sets and Sublinear Functions.- Subdifferentials of Finite Convex Functions: The Subdifferential: Definitions and Interpretations Local Properties of the Subdifferential First Examples Calculus Rules with Subdifferentials Further Examples The Subdifferential as a Multifunction.- Conjugacy in Convex Analysis: The Convex Conjugate of a Function Calculus Rules on the Conjugacy Operation Various Examples Differentiability of a Conjugate Function.

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Topics: Subderivative (80%), Convex analysis (78%), Proper convex function (76%) ... show more

1,167 Citations


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202128
202014