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In the present paper, the expected-value (EV) reformulation of a class of stochastic vector variational inequalities (SVVI) is investigated. By using the regularized gap function, the EV reformulation of SVVI is transformed into a constrained optimization problem. Then a sample average approximation (SAA) method is proposed for solving the constrained optimization problem. Under suitable assumptions, the limiting behaviors of the optimal values and optimal solutions of the approximation problem are investigated. Finally, the rates of convergence in the different senses of optimal solutions for sample average approximation problem are discussed under the error bound condition. [ABSTRACT FROM AUTHOR]

This study presented a new generalized regularized gap function (short for GRGF) for random generalized variational-like inequality (short for RGVLIP) in fuzzy contexts. A new D-gap function for RGVLIP was also introduced, utilizing the reverse Schwarz inequality and GRGF, which we also established for real Hilbert space in fuzzy settings. Furthermore, we developed error bound outcomes for the new D-gap function and GRGF as an application. Our findings are new and generalize several established findings for variational inequality problems (abbreviated VIPs) and generalized variational inequality problems (abbreviated GVIPs) in fuzzy contexts. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the upper bound and H¨older continuity for a general class of parametric elliptical variational-hemivariational inequalities via regularized gap functions. More precisely, we deliver a formulation of the elliptical variational-hemivariational inequalities in the case of the perturbed parameters governed by both the set of constraints and the mappings (for brevity, PEVHI (CM)). Based on the arguments of monotonicity and properties of the Clarke's generalized directional derivative, we establish an upper bound result for the PEVHI (CM) and provide the H¨older continuity of the solution mapping for the PEVHI (CM) under suitable assumptions on the data. [ABSTRACT FROM AUTHOR]

In this paper, we consider a new class of split weak vector mixed quasi-variational inequality problems in fuzzy environments (for short, SWQVIP). Our aim is to establish error bounds for the underlying problem SWQVIP via regularized gap functions. We first propose some regularized gap functions of the problem SWQVIP using the method of nonlinear scalarization functions. Then, error bounds for the problem SWQVIP are investigated based on regularized gap functions with some suitable conditions without monotonicity. Finally, some examples are given to illustrate our results. The main results obtained in this paper are new and extend some corresponding known results in the literature. [ABSTRACT FROM AUTHOR]

The goal of this paper is further to study a kind of generalized vector inverse quasi-variational inequality problems and to obtain error bounds in terms of the residual gap function, the regularized gap function, and the global gap function by utilizing the relaxed monotonicity and Hausdorff Lipschitz continuity. These error bounds provide effective estimated distances between an arbitrary feasible point and the solution set of generalized vector inverse quasi-variational inequality problems.

The purpose of this paper is to establish some new results on the Levitin–Polyak well-posedness to a class of split hemivariational inequality problems on Hadamard manifolds. We first consider a new class of split hemivariational inequality problems (for short, SHIP) on Hadamard manifolds and introduce the regularized gap functions for these problems. Then, we study the notion of Levitin–Polyak well-posedness by perturbations to SHIP and show the equivalence between the Levitin–Polyak well-posedness by perturbations and the existence of solutions for SHIP under suitable conditions. Furthermore, based on the regularized gap functions for the perturbed SHIP, we establish the criterion for the Levitin–Polyak well-posedness by perturbations for SHIP via the split optimization problems on Hadamard manifolds. Our main results presented in paper are new even in the special case of hemivariational inequality problems. [ABSTRACT FROM AUTHOR]

The main goal of this article is to investigate upper-bound error estimates (also called error bounds) for a class of double phase obstacle problems. We first recall double phase implicit obstacle problems involving Clarke's subdifferential given by Zeng et al. [Calc. Var. Partial Differential Equations 59 (2020): 176]. Then, based on regularized gap functions introduced by Yamashita and Fukushima, we establish some regularized gap functions for the double phase obstacle problem. Finally, using the properties of Clarke's subdifferential and double phase operators, the upper-bound error estimates for such double phase obstacle problems in terms of regularized gap functions are provided. [ABSTRACT FROM AUTHOR]

This paper is devoted to presenting new error bounds of regularized gap functions for polynomial variational inequalities with exponents explicitly determined by the dimension of the underlying space and the number/degree of the involved polynomials. The main techniques are based on semialgebraic geometry and variational analysis, which allow us to establish a nonsmooth extension of the seminal Łojasiewicz gradient inequality to regularized gap functions with explicitly calculated exponents. [ABSTRACT FROM AUTHOR]

The aim of this paper is to establish new results on the error bounds for a class of vector equilibrium problems with partial order provided by a polyhedral cone generated by some matrix. We first propose some regularized gap functions of this problem using the concept of G A -convexity of a vector-valued function. Then, we derive error bounds for vector equilibrium problems with partial order given by a polyhedral cone in terms of regularized gap functions under some suitable conditions. Finally, a real-world application to a vector network equilibrium problem is given to illustrate the derived theoretical results. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this paper is to introduce and study the mixed set-valued vector inverse quasi-variational inequality problems (MSVIQVIPs) and to obtain error bounds for this kind of MSVIQVIP in terms of the residual gap function, the regularized gap function, and the D-gap function. These bounds provide effective estimated distances between an arbitrary feasible point and the solution set of mixed set-valued vector inverse quasi-variational inequality problem. The results presented in the paper improve and generalize some recent results.

This paper is devoted to the study of the difference gap (for brevity, D-gap) function and global error bounds for a class of elliptic variational inequalities (for brevity, EVIs). Firstly, we establish the regularized gap function introduced by Yamashita and Fukushima for EVIs under some suitable conditions. Then the D-gap function for EVIs is proposed by employing these regularized gap functions. Furthermore, we also develop the global error bounds for EVIs in terms of the regularized gap function of the Fukushima type and the D-gap function. Finally, an application to frictional contact problem is given to illustrate our main results. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study a class of mixed quasi-hemivariational inequality problems on Hadamard manifolds (in short, (MQHIP)). Some regularized gap functions for (MQHIP) are proposed under suitable conditions. Finally, global error bounds for (MQHIP) in terms of regularized gap functions are derived by employing some strong monotonicity conditions and using the properties of Clarke's generalized directional derivative. The main results presented in this paper improve and generalize corresponding results in literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce and study the mixed set-valued vector inverse quasi-variational inequality problems (MSVIQVIPs) and to obtain error bounds for this kind of MSVIQVIP in terms of the residual gap function, the regularized gap function, and the D-gap function. These bounds provide effective estimated distances between an arbitrary feasible point and the solution set of mixed set-valued vector inverse quasi-variational inequality problem. The results presented in the paper improve and generalize some recent results. [ABSTRACT FROM AUTHOR]

In this paper, the Clarke generalized Jacobian of the generalized regularized gap function for a nonmonotone Ky Fan inequality is studied. Then, based on the Clarke generalized Jacobian, we derive a global error bound for the nonmonotone Ky Fan inequalities. Finally, an application is given to provide a descent method. [ABSTRACT FROM AUTHOR]

Abstract This paper is devoted to the study of gap functions of random generalized variational inequality problems in a fuzzy environment. Further by using the residual vector we compute error bounds for random generalized variational inequality problems and we generalize the regularized gap functions proposed by Fukushima. Furthermore we study various properties of the generalized regularized gap functions in random fuzzy mappings and derive the global error bounds for random generalized variational inequality problems. Our results are new and generalize a number of known results to generalized variational inequality problems with fuzzy mappings.

In this paper, we consider generalized mixed strong vector quasiequilibrium problems in infinite-dimensional spaces. Using the nonlinear scalarization method, we first propose gap functions for such problems. Then, based on the Moreau-Yosida regularization of these functions, we introduce regularized gap functions for the reference problems. Next, we study sufficient conditions for the mentioned functions to be continuous. Finally, error bounds for the concerning problems in terms of regularized gap functions are investigated. Some examples are provided to illustrate our results. [ABSTRACT FROM AUTHOR]

We propose a two-stage stochastic variational inequality model to deal with random variables in variational inequalities, and formulate this model as a two-stage stochastic programming with recourse by using an expected residual minimization solution procedure. The solvability, differentiability and convexity of the two-stage stochastic programming and the convergence of its sample average approximation are established. Examples of this model are given, including the optimality conditions for stochastic programs, a Walras equilibrium problem and Wardrop flow equilibrium. We also formulate stochastic traffic assignments on arcs flow as a two-stage stochastic variational inequality based on Wardrop flow equilibrium and present numerical results of the Douglas-Rachford splitting method for the corresponding two-stage stochastic programming with recourse. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of gap functions of random generalized variational inequality problems in a fuzzy environment. Further by using the residual vector we compute error bounds for random generalized variational inequality problems and we generalize the regularized gap functions proposed by Fukushima. Furthermore we study various properties of the generalized regularized gap functions in random fuzzy mappings and derive the global error bounds for random generalized variational inequality problems. Our results are new and generalize a number of known results to generalized variational inequality problems with fuzzy mappings. [ABSTRACT FROM AUTHOR]

In this paper, we define some new notions of gap functions for set-valued mixed quasi-variational inequalities under suitable conditions. Further, we obtain local/global bounds for the solution of set-valued mixed quasi-variational inequality problems in terms of the residual gap function, the regularized gap function, and the D-gap function. The results obtained in this paper are generalization and refinement of previously known results for some class of variational inequality problems. [ABSTRACT FROM AUTHOR]

In this paper, we obtain some gap functions for generalized mixed quasi variational inequality problems in terms of regularized gap function and D -gap function. Further, by using these gap functions we obtain global error bounds for the solution of generalized mixed quasi variational inequality problems in Hilbert spaces. The results obtained in this paper generalize and improve some corresponding known results in literatures. [ABSTRACT FROM AUTHOR]

A well-known technique for the solution of quasi-variational inequalities (QVIs) consists in the reformulation of this problem as a constrained or unconstrained optimization problem by means of so-called gap functions. In contrast to standard variational inequalities, however, these gap functions turn out to be nonsmooth in general. Here, it is shown that one can obtain an unconstrained optimization reformulation of a class of QVIs with affine operator by using a continuously differentiable dual gap function. This extends an idea from Dietrich (J. Math. Anal. Appl. 235:380-393 []). Some numerical results illustrate the practical behavior of this dual gap function approach. [ABSTRACT FROM AUTHOR]

In this paper, by the nonlinear scalarization method, a global error bound of a weak vector variational inequality is established via a regularized gap function. The result extends some existing results in the literature. [ABSTRACT FROM AUTHOR]

In optimization problems, it is significant to study the directional derivatives and subdifferentials of objective functions. Using directional derivatives and subdifferentials of objective functions, we can establish optimality conditions, derive error bound properties, and propose optimal algorithms. In this paper, the upper and lower estimates for the Clarke directional derivatives of a class of marginal functions are established. Employing this result, we obtain the exact formulations of the Clarke directional derivatives of the regularized gap functions for nonsmooth quasi-variational inequalities. [ABSTRACT FROM AUTHOR]

This article studies continuity and differentiability properties for a reformulation of a finite-dimensional quasi-variational inequality (QVI) problem using a regularized gap function approach. For a special class of QVIs, this gap function is continuously differentiable everywhere, in general, however, it has nondifferentiability points. We therefore take a closer look at these nondifferentiability points and show, in particular, that under mild assumptions all locally minimal points of the reformulation are differentiability points of the regularized gap function. The results are specialized to generalized Nash equilibrium problems. Numerical results are also included and show that the regularized gap function provides a valuable approach for the solution of QVIs. [ABSTRACT FROM PUBLISHER]

Abstract: This paper is dedicated to inverse quasi-variational inequalities which correspond to a mixture of quasi-variational inequalities and inverse variational inequalities. Our aim is to obtain local/global error bounds for inverse quasi-variational inequality problems in terms of different gap functions/merit functions i.e. the residual gap function, the regularized gap function and the -gap function. These bounds provide effective estimated distances between a specific point and the exact solution of the inverse quasi-variational inequality problem. Since the class of inverse quasi-variational inequalities includes the classes of general quasi-variational inequalities and variational inequalities, our results cover and extend similar results for these problems. [Copyright &y& Elsevier]

The paper aims to obtain new local/global error bounds for quasi variational inequality problems in terms of the regularized gap function and the D-gap function. These bounds provide effective estimated distances between a specific point and the exact solution of quasi variational inequality problem. [ABSTRACT FROM AUTHOR]

Abstract: By using the regularized gap function for variational inequalities, Li and Peng introduced a new penalty function for the problem of minimizing a twice continuously differentiable function in closed convex subset of the n-dimensional space . Under certain assumptions, they proved that the original constrained minimization problem is equivalent to unconstrained minimization of . The main purpose of this paper is to give an in-depth study of those properties of the objective function that can be extended from the feasible set to the whole by . For example, it is proved that the objective function has bounded level sets (or is strongly coercive) on the feasible set if and only if has bounded level sets (or is strongly coercive) on . However, the convexity of the objective function does not imply the convexity of when the objective function is not quadratic, no matter how small α is. Instead, the convexity of the objective function on the feasible set only implies the invexity of on . Moreover, a characterization for the invexity of is also given. [Copyright &y& Elsevier]

Abstract: Under the condition that the involved function F is locally Lipschitz, but not necessarily differentiable, we investigate the regularized gap function defined by a generalized distance function for the variational inequality problem (VIP). First, we compute exactly the Clarke–Rockafellar directional derivatives of the regularized gap functions (and of some modified ones). Second, using these results, we show that, under the strongly monotonicity assumption, the regularized gap functions have fractional exponent error bounds, and thereby we provide an algorithm of Armijo type to solve the VIP. [Copyright &y& Elsevier]

We study the Clarke–Rockafellar directional derivatives of the regularized gap functions (and of some modified ones) for the variational inequality problem (VIP) defined by a locally Lipschitz but not necessarily differentiable function on a closed convex set in an Euclidean space. As applications we show that, under the strong monotonicity assumption, the regularized gap functions have fractional exponent error bounds and consequently that the sequences provided by an algorithm of Armijo type converge to the solution of the (VIP). [ABSTRACT FROM AUTHOR]

The quadratic programme with linear equality constraints, is reformulated as an unconstrained minimization of a differentiable quadratic function. The two set of problems have, exactly the same set of solutions. Unlike other penalty functions, the parameter involved in the unconstrained reformulation can be easily determined by the Frobenius norm of the Hessian matrix of the original objective function. Moreover it has been proved that, whenever the original quadratic programme possesses a solution, the reformulated objective function is convex. [ABSTRACT FROM AUTHOR]