Inertial Step Research Articles

A two-step relaxed-inertial derivative-free projection based algorithm for solving standard nonlinear pseudo-monotone equations and logistic regression problems

This paper explores a two-step inertial derivative-free projection method with a relaxation factor γ∈(0,2) for solving nonlinear pseudo-monotone equations. Unlike existing inertial algorithms for the system of nonlinear pseudo-monotone equations, the inertial step of our method involves the current iteration point and the previous two iteration points. In particular, one of the inertial parameters is nonpositive. In the proposed algorithm, the search direction possesses not only the sufficient descent property but also the trust region property, independent of the line search technique. Moreover, we also establish the global convergence and the convergence rate of the algorithm without the Lipschitz continuity of the underlying mapping. Finally, our method provides competitive results on standard nonlinear monotone and pseudo-monotone equations and logistic regression problems compared with two inertial algorithms existing in the literature.

Outer reflected forward–backward splitting algorithm with inertial extrapolation step

ABSTRACT This paper studies an outer reflected forward–backward splitting algorithm with an inertial step to find a zero of the sum of three monotone operators composing the maximal monotone operator, Lipschitz monotone operator, and a cocoercive operator in real Hilbert spaces. One of the interesting features of the proposed method is that both the Lipschitz monotone operator and the cocoercive operator are computed explicitly each with one evaluation per iteration. We obtain weak and strong convergence results under some easy-to-verify assumptions. We also obtain a non-asymptotic O ( 1 / n ) convergence rate of our proposed algorithm in a non-ergodic sense. We finally give some numerical illustrations arising from compressed sensing and image processing and show that our proposed method is effective and competitive with other related methods in the literature.

A Golden Ratio Algorithm With Backward Inertial Step For Variational Inequalities

In this paper we study the convergence analysis of a Golden Ratio Algorithm with a backward inertial step and a fully adaptive step size procedure for the purpose of approximating solutions of variational inequalities in Hilbert spaces. We present a weak convergence result when the operator is quasi-monotone and locally Lipschitz continuous, and a strong convergence result in the setting of strong pseudo-monotonicity. Our algorithm features one operator evaluation and one projection computation at the current iteration. We recover interesting algorithms in the literature, for instance, the Golden Ratio Algorithm. Numerical experiments were conducted to validate the theoretical convergence analysis.

A Method with Double Inertial Type and Golden Rule Line Search for Solving Variational Inequalities

In this work, we study a new line-search rule for solving the pseudomonotone variational inequality problem with non-Lipschitz mapping in real Hilbert spaces as well as provide a strong convergence analysis of the sequence generated by our suggested algorithm with double inertial extrapolation steps. In order to speed up the convergence of projection and contraction methods with inertial steps for solving variational inequalities, we propose a new approach that combines double inertial extrapolation steps, the modified Mann-type projection and contraction method, and the line-search rule, which is based on the golden ratio (5+1)/2. We demonstrate the efficiency, robustness, and stability of the suggested algorithm with numerical examples.

A proximal gradient method with double inertial steps for minimization problems involving demicontractive mappings

In this article, we present a novel proximal gradient method based on double inertial steps for solving fixed points of demicontractive mapping and minimization problems. We also establish a weak convergence theorem by applying this method. Additionally, we provide a numerical example related to a signal recovery problem.

Self-adaptive Technique with Double Inertial Steps for Inclusion Problem on Hadamard Manifolds

AbstractIn this article, we investigate monotone and Lipschitz continuous variational inclusion problem in the settings of Hadamard manifolds. We propose a forward–backward method with a self-adaptive technique for solving variational inclusion problem. To increase the rate of convergence of our proposed method, we incorporate our iterative method with double inertial steps and establish a convergence result of our iterative method under some mild conditions. Finally, in order to illustrate the computational effectiveness of our method, some numerical examples are also discussed. The result present in this article is new in this space and extends many related results in the literature.

Extragradient methods with dual inertial steps for solving equilibrium problems

ABSTRACT The primary focus of this study is how to expedite the convergence rate of the extragradient method by tactfully choosing inertial parameters and building up the step-size rule. To achieve this goal, we introduce three improvements to Korpelevich's extragradient method. These improvements include the use of dual inertial steps as well as a self-adaptive step-size rule. The proposed iterative techniques are employed for solving equilibrium problems in real Hilbert spaces. Initially, we establish the results for weak convergence of the proposed methods, assuming the involved bifunction to be both pseudomonotone and Lipschitz-type continuous. Subsequently, we demonstrate linear convergence in scenarios where the bifunction is strongly pseudomonotone and Lipschitz-type continuous. Finally, we present multiple numerical experiments to illustrate the practical effectiveness of the proposed methods.

An efficient inertial subspace minimization CG algorithm with convergence rate analysis for constrained nonlinear monotone equations

Conjugate gradient (CG) methods are efficient algorithms for unconstrained optimization. An inertial step strategy is usually used to accelerate iterative methods. In 1995, Yuan and Stoer (1995) introduced a subspace study on CG algorithms and presented some subspace minimization CG (SMCG) methods. Motivated by these approaches, we incorporate a new inertial step strategy in SMCG methods and present an efficient inertial SMCG method for solving constrained nonlinear monotone equations by combining with the projection technique. The proposed inertial step strategy makes use of information from recent iterations. It can be considered as an extension of the known inertial step strategy. The resulting direction always satisfies the sufficient descent property and that of trust region. Indeed, these properties do not depend on line search rules. The global convergence and Q-linear convergence rate of the proposed method are established under mild assumptions, without any Lipschitz continuity. Numerical results indicate that the proposed method is very promising.

Projection and contraction method with double inertial steps for quasi-monotone variational inequalities

In this paper, we present a modified projection and contraction method for solving quasi-monotone variational inequalities in real Hilbert spaces. Our proposed method is a combination of double inertial extrapolation steps, the subgradient extragradient method and the projection contraction method, which can effectively accelerate the convergence rate. The weak and linear convergence have been obtained under some suitable conditions. Some numerical experiments are given to show that our proposed method outperforms the related methods.

Alternated inertial forward-backward-forward splitting algorithm

In this paper, we propose a forward-backward-forward splitting algorithm with alternated inertial step for finding a zero of the sum of two monotone operators in a real Hilbert space. Then, by making use of primal-dual techniques we derive the alternated inertial primal-dual splitting algorithm of forward-backward-forward type for solving structured monotone inclusion problems involving parallel sums and compositions of maximally monotone operators with linear continuous ones and convex minimization problems. Finally, we demonstrate numerically that our proposed algorithm performs better than the existing algorithms.

Double inertial steps extragadient-type methods for solving optimal control and image restoration problems

<abstract><p>In order to approximate the common solution of quasi-nonexpansive fixed point and pseudo-monotone variational inequality problems in real Hilbert spaces, this paper presented three new modified sub-gradient extragradient-type methods. Our algorithms incorporated viscosity terms and double inertial extrapolations to ensure strong convergence and to speed up convergence. No line search methods of the Armijo type were required by our algorithms. Instead, they employed a novel self-adaptive step size technique that produced a non-monotonic sequence of step sizes while also correctly incorporating a number of well-known step sizes. The step size was designed to lessen the algorithms' reliance on the initial step size. Numerical tests were performed, and the results showed that our step size is more effective and that it guarantees that our methods require less execution time. We stated and proved the strong convergence of our algorithms under mild conditions imposed on the control parameters. To show the computational advantage of the suggested methods over some well-known methods in the literature, several numerical experiments were provided. To test the applicability and efficiencies of our methods in solving real-world problems, we utilized the proposed methods to solve optimal control and image restoration problems.</p></abstract>

Convergence of proximal gradient method with alternated inertial step for minimization problem

Convergence of proximal gradient method with alternated inertial step for minimization problem

Using Double Inertial Steps Into the Single Projection Method with Non-monotonic Step Sizes for Solving Pseudomontone Variational Inequalities

Using Double Inertial Steps Into the Single Projection Method with Non-monotonic Step Sizes for Solving Pseudomontone Variational Inequalities

Relaxed Tseng splitting method with double inertial steps for solving monotone inclusions and fixed point problems

Relaxed Tseng splitting method with double inertial steps for solving monotone inclusions and fixed point problems

A Modified Tseng Splitting Method with Double Inertial Steps for Solving Monotone Inclusion Problems

A Modified Tseng Splitting Method with Double Inertial Steps for Solving Monotone Inclusion Problems

Projection methods for quasi-nonexpansive multivalued mappings in Hilbert spaces

<abstract><p>This paper proposes a modified D-iteration to approximate the solutions of three quasi-nonexpansive multivalued mappings in a real Hilbert space. Due to the incorporation of an inertial step in the iteration, the sequence generated by the modified method converges faster to the common fixed point of the mappings. Furthermore, the generated sequence strongly converges to the required solution using a shrinking technique. Numerical results obtained indicate that the proposed iteration is computationally efficient and outperforms the standard forward-backward with inertial step.</p></abstract>

A new spectral method with inertial technique for solving system of nonlinear monotone equations and applications

<abstract><p>Many problems arising from science and engineering are in the form of a system of nonlinear equations. In this work, a new derivative-free inertial-based spectral algorithm for solving the system is proposed. The search direction of the proposed algorithm is defined based on the convex combination of the modified long and short Barzilai and Borwein spectral parameters. Also, an inertial step is introduced into the search direction to enhance its efficiency. The global convergence of the proposed algorithm is described based on the assumption that the mapping under consideration is Lipschitz continuous and monotone. Numerical experiments are performed on some test problems to depict the efficiency of the proposed algorithm in comparison with some existing ones. Subsequently, the proposed algorithm is used on problems arising from robotic motion control.</p></abstract>

A Multi-Step Inertial Proximal Peaceman-Rachford Splitting Method for Separable Convex Programming

In this paper, we propose a multi-step inertial proximal Peaceman-Rachford splitting method (abbreviated as MIP-PRSM) for solving the two-block separable convex optimization problems with linear constraints, which is a unified framework for such Peaceman-Rachford splitting methods (PRSM)-based improved algorithms with inertial step. Furthermore, we establish the global convergence of the MIP-PRSM under some assumptions. Finally, some numerical experimental results on the least squares semidefinite programming (LSSP), LASSO, the convex quadratic programming problem (CQPP), total variation (TV) based denoising and medical image reconstruction problems demonstrate the efficiency of the proposed method.

Subgradient extragradient method with double inertial steps for quasi-monotone variational inequalities

In this paper, we present a modified subgradient extragradient method with double inertial extrapolation terms and a non-monotonic adaptive step size for solving quasi-monotone and Lipschitz continuous variational inequalities in real Hilbert spaces. Under some suitable conditions, we obtain the weak convergence theorem of our proposed algorithm. Moreover, strong convergence is obtained when the cost operator is strongly pseudo-monotone and Lipschitz continuous. Finally, several numerical results illustrate the effectiveness and competitiveness of our algorithm.

Projection method with inertial step for nonlinear equations: Application to signal recovery

<p style='text-indent:20px;'>In this paper, using the concept of inertial extrapolation, we introduce a globally convergent inertial extrapolation method for solving nonlinear equations with convex constraints for which the underlying mapping is monotone and Lipschitz continuous. The method can be viewed as a combination of the efficient three-term derivative-free method of Gao and He [Calcolo. 55(4), 1-17, 2018] with the inertial extrapolation step. Moreover, the algorithm is designed such that at every iteration, the method is free from derivative evaluations. Under standard assumptions, we establish the global convergence results for the proposed method. Numerical implementations illustrate the performance and advantage of this new method. Moreover, we also extend this method to solve the LASSO problems to decode a sparse signal in compressive sensing. Performance comparisons illustrate the effectiveness and competitiveness of our algorithm.</p>