The Bruck's ergodic iteration method for the Ky Fan inequality over the fixed point set

We introduce a new iteration algorithm for solving the Ky Fan inequality over the fixed point set of a nonexpansive mapping, where the cost bifunction is monotone without Lipschitz-type continuity. The algorithm is based on the idea of the ergodic iteration method for solving multi-valued variational inequality which is proposed by Bruck [On the weak convergence of an ergodic iteration for the solution of variational inequalities for monotone operators in Hilbert space, J. Math. Anal. Appl. 61 (1977), pp. 159–164] and the auxiliary problem principle for equilibrium problems P.N. Anh, T.N. Hai, and P.M. Tuan. [On ergodic algorithms for equilibrium problems, J. Glob. Optim. 64 (2016), pp. 179–195]. By choosing suitable regularization parameters, we also present the convergence analysis in detail for the algorithm and give some illustrative examples.     

A multigrid optimization algorithm for the numerical solution of quasilinear variational inequalities involving the p-Laplacian

In this paper we propose a multigrid optimization algorithm (MG/OPT) for the numerical solution of a class of quasilinear variational inequalities of the second kind. This approach is enabled by the fact that the solution of the variational inequality is given by the minimizer of a nonsmooth energy functional, involving the $p$-Laplace operator. We propose a Huber regularization of the functional and a finite element discretization for the problem. Further, we analyze the regularity of the discretized energy functional, and we are able to prove that its Jacobian is slantly differentiable. This regularity property is useful to analyze the convergence of the MG/OPT algorithm. In fact, we demonstrate that the algorithm is globally convergent by using a mean value theorem for semismooth functions. Finally, we apply the MG/OPT algorithm to the numerical simulation of the viscoplastic flow of Bingham, Casson and Herschel–Bulkley fluids in a pipe. Several experiments are carried out to show the efficiency of the proposed algorithm when solving this kind of fluid mechanics problems.     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.     

The Nonlocal Newton Method for Convex Optimization Problems and Monotone Variational Inequalities

The nonlocal Newton method is developed for nonlinear problems of conditional convex optimization and monotone variational inequalities in a finite-dimensional space. The Newton direction vector is calculated from a solution of a linear-approximating variational inequality. A new penalty function is proposed to define a step length.     

Substructuring of a Signorini-type problem and Robin’s method for the Richards equation in heterogeneous soil

We prove a substructuring result for variational inequalities. It concerns but is not restricted to the Richards equation in heterogeneous soil, and it includes boundary conditions of Signorini’s type. This generalizes existing results for the linear case and leads to interface conditions known from linear variational equalities: continuity of Dirichlet and flux values in a weak sense. In case of the Richards equation, these are the continuity of the physical pressure and of the water flux, which is hydrologically reasonable. We use these interface conditions in a heterogeneous problem with piecewise constant soil parameters, which we address by the Robin method. We prove that, for a certain time discretization, the homogeneous problems in the subdomains including Robin and Signorini-type boundary conditions can be solved by convex minimization. As a consequence, we are able to apply monotone multigrid in the discrete setting as an efficient and robust solver for the local problems. Numerical results demonstrate the applicability of our approach.     

Convergence Analysis for a Primal-Dual Monotone + Skew Splitting Algorithm with Applications to Total Variation Minimization

In this paper we investigate the convergence behavior of a primal-dual splitting method for solving monotone inclusions involving mixtures of composite, Lipschitzian and parallel sum type operators proposed by Combettes and Pesquet (in Set-Valued Var. Anal. 20(2):307–330, 2012). Firstly, in the particular case of convex minimization problems, we derive convergence rates for the partial primal-dual gap function associated to a primal-dual pair of optimization problems by making use of conjugate duality techniques. Secondly, we propose for the general monotone inclusion problem two new schemes which accelerate the sequences of primal and/or dual iterates, provided strong monotonicity assumptions for some of the involved operators are fulfilled. Finally, we apply the theoretical achievements in the context of different types of image restoration problems solved via total variation regularization.     

A class of projection and contraction methods for asymmetric linear variational inequalities and their relations to Fukushima's descent method

For solving asymmetric linear variational inequalities, we present a class of projection and contraction methods under the general G-norm. The search direction of our methods is just a convex combination of two descent directions of Fukushima's merit function. However, we use the direction to reduce the distance function (1/2)u − u*²_G, where μ* is a solution point of the problem. Finally, we report some numerical results for spatial price equilibrium problems by using the presented methods.     

Multiphase Soft Segmentation with Total Variation and <Emphasis Type="Italic">H</Emphasis><Superscript>1</Superscript> Regularization

In this paper, we propose a variational soft segmentation framework inspired by the level set formulation of multiphase Chan-Vese model. We use soft membership functions valued in [0,1] to replace the Heaviside functions of level sets (or characteristic functions) such that we get a representation of regions by soft membership functions which automatically satisfies the sum to one constraint. We give general formulas for arbitrary N-phase segmentation, in contrast to Chan-Vese’s level set method only 2 ^m -phase are studied. To ensure smoothness on membership functions, both total variation (TV) regularization and H ¹ regularization used as two choices for the definition of regularization term. TV regularization has geometric meaning which requires that the segmentation curve length as short as possible, while H ¹ regularization has no explicit geometric meaning but is easier to implement with less parameters and has higher tolerance to noise. Fast numerical schemes are designed for both of the regularization methods. By changing the distance function, the proposed segmentation framework can be easily extended to the segmentation of other types of images. Numerical results on cartoon images, piecewise smooth images and texture images demonstrate that our methods are effective in multiphase image segmentation.     

Nonlinear equation approach for inequality elastostatics: a two-dimensional BEM implementation

The numerical solution of variational inequality problems in elastostatics is investigated by means of recently proposed equivalent nonlinear equations. Symmetric and nonsymmetric variational inequalities and linear or nonlinear, but monotone, complementarity problems can be solved this way without explicit use of nonsmooth (nondifferentiable) solvers. As a model application, two-dimentional unilateral contact problems with and without friction effects approximated by the boundary element method are formulated as nonsymmetric variational inequalities, or, for the two-dimensional case as linear complementarity problems, and are numerically solved. Performance comparisons using two standard, smooth, general purpose nonlinear equation solvers are included.     

二阶总广义变分小波修复模型

目的 针对全变分小波修复模型易导致阶梯效应的缺陷,提出一种加权的二阶总广义变分小波修复模型。方法 不同于全变分小波修复模型,假设的新模型引入二阶导数项且能够自动地调解一阶和二阶导数项。另外,为有效地利用图像的局部结构信息,新模型引入了权函数,它既能保护图像的边缘又增强光滑区域的去噪能力。 为有效地计算新模型,利用交替方向法将该模型变为两个子模型, 然后对两个子模型分别给出相应的理论和算法推导。结果 相比最近基于全变分正则小波修复模型(平均信噪比,平均绝对误差及平均结构相似性指标分别为21.884 4,6.857 8,0.827 2),新模型得到更好的修复效果(平均信噪比,平均绝对误差及平均结构相似性指标分别为22.313 8,6.626 1,0.831 8)。结论 与全变分正则相比,二阶总广义变分正则更好地减轻阶梯效应。目前, 国内外学者对该问题的研究取得一些结果。由于原始-对偶算法需要较小的参数,所以运算的速度较慢,因此更快速的算法理论有待进一步研究。另外,该正则能应用于图像去噪、分割、放大等方面。     

Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network

In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network     

Forward–backward-based descent methods for composite variational inequalities

We consider the monotone composite variational inequality (CVI) where the underlying mapping is formed as the sum of two monotone mappings. We combine the forward–backward and descent direction ideas together, and thus present the unified algorithmic framework of forward–backward-based descent methods for solving the CVI. A new iterate of such a method is generated by a prediction–correction fashion, where the predictor is yielded by the forward–backward method and then the predictor is corrected by a descent step. We derive some implementable forward–backward-based descent algorithms for some concrete cases of the CVI, and verify their numerical efficiency via preliminary numerical experiments.     

A Variational Approach to Reconstructing Images Corrupted by Poisson Noise

We propose a new variational model to denoise an image corrupted by Poisson noise. Like the ROF model described in [1] and [2], the new model uses total-variation regularization, which preserves edges. Unlike the ROF model, our model uses a data-fidelity term that is suitable for Poisson noise. The result is that the strength of the regularization is signal dependent, precisely like Poisson noise. Noise of varying scales will be removed by our model, while preserving low-contrast features in regions of low intensity. Funded by the Department of Energy under contract W-7405ENG-36. Triet M. Le received his Ph.D. in Mathematics from the University of California, Los Angeles, in 2006. He is now a Gibbs Assistant Professor in the Mathematics Department at Yale University. His research interests are in applied harmonic analysis and function spaces with application to image analysis and inverse problems. Rick Chartrand received a Ph.D. in Mathematics from UC Berkeley in 1999, where he studied functional analysis. He now works as an applied mathematician at Los Alamos National Laboratory. His research interests are image and signal processing, inverse problems, and classification. Tom Asaki is a staff member in the Computer and Computational Science Division at Los Alamos National Laboratory. He obtained his doctorate in physics from Washington State University. His interests are mixed-variable and direct-search optimization, applied inverse problems, and quantitative tomography.     

A Version of the Mirror descent Method to Solve Variational Inequalities<Superscript>*</Superscript>

Nemirovski and Yudin proposed the mirror descent algorithm at the late 1970s to solve convex optimization problems. This method is suitable to solve huge-scale optimization problems. In the paper, we describe a new version of the mirror descent method to solve variational inequalities with pseudomonotone operators. The method can be interpreted as a modification of Popov’s two-step algorithm with the use of Bregman projections on the feasible set. We prove the convergence of the sequences generated by the proposed method.     

Penalty Method for Variational Inequalities with Multivalued Mappings. III

By means of penalty operators, a method of regularization of variational inequalities is generalized to a class of variational inequalities with multivalued mappings.     

Penalty Method for Variational Inequalities with Multivalued Mappings. Part II

By means of penalty operators, a method of regularization of variational inequalities is generalized to a class of variational inequalities with multivalued mappings.     

Projection-splitting algorithms for monotone variational inequalities

We consider and analyze some new projection-splitting algorithms for solving monotone variational inequalities by using the technique of updating the solution. Our modification is in the spirit of the extragradient method. The modified methods converge for monotone continuous operators. The new iterative method differs from the existing projection methods.     

Some algorithms solving the unilateral dirichlet problem with two constraints

We consider the Dirichlet problem with two obstacles in the theory of variational inequalities and a finite-dimensional discretization related with a matrix belonging to the class (P)∩(Z). We construct some algorithms which produce monotone sequences. These sequences converge to the solution of the discrete problem starting from lower or upper obstacle.

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Sommario Consideriamo il problema di Dirichlet dei due ostacoli, ben conosciuto nella teoria delle disequazioni variazionali ed una sua discretizzazione relative ad una matrice della classe (P)∩(Z). In questo conteto costruiamo alcuni algoritmi atti a produrre successioni approssimanti, monotone non decrescenti e non crescenti, convergenti alla soluzione del problema discreto a partire rispettivamente dall’ostacolo inferiore e superiore.

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This paper has been partially supported by GNAFA-CNR.     

Penalty method for variational inequalities with multivalued mappings. Part I

The regularization method of variational inequalities is generalized by means of penalty operators to a class of variational inequalities with multivalued mappings. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 57–69, July–August, 2000.