Sensitivity Analysis of Stochastic Constraint and Variational Systems via Generalized Differentiation

The ideas of approximation and continuity have been extensively investigated both for optimization problems and variational inequalities. In this paper, we study approximation issues for a class of VIs, called Stochastic Variational Inequalities (SVIs), that arise, for example, in stochastic programming and portfolio choice problems. SVI problems are special cases of a more general class of problems that we will study first, called Stochastic Ky Fan Inequalities (SKFIs). We also analyze the role of monotonicity in the analysis of both SVIs and SKFIs. Our interest in these problems is motivated by recent research in the theory of portfolio choice for investors who are not classical expected utility maximizers.

In this paper, we consider a class of stochastic differential variational inequalities (for short, SDVIs) consisting of an ordinary differential equation and a stochastic variational inequality. The existence of solutions to SDVIs is established under the assumption that the leading operator in the stochastic variational inequality is $P$-function and $P_{0}$-function, respectively. Then, by using the sample average approximation and time stepping methods, two approximated problems corresponding to SDVIs are introduced and convergence results are obtained.

A stochastic fractional differential variational inequality with Lévy jump and its application

The stochastic variational inequality (VI) has been used widely in engineering and economics as an effective mathematical model for a number of equilibrium problems involving uncertain data. This paper presents a new expected residual minimization (ERM) formulation for a class of stochastic VI. The objective of the ERM-formulation is Lipschitz continuous and semismooth which helps us guarantee the existence of a solution and convergence of approximation methods. We propose a globally convergent (a.s.) smoothing sample average approximation (SSAA) method to minimize the residual function; this minimization problem is convex for the linear stochastic VI if the expected matrix is positive semidefinite. We show that the ERM problem and its SSAA problems have minimizers in a compact set and any cluster point of minimizers and stationary points of the SSAA problems is a minimizer and a stationary point of the ERM problem (a.s.). Our examples come from applications involving traffic flow problems. We show that the conditions we impose are satisfied and that the solutions, efficiently generated by the SSAA procedure, have desirable properties.

In this paper, we consider a class of one-dimensional reflected Backward Stochastic Differential Equation (BSDE for short) when the noise is driven by a Brownian motion and an independent Poisson point process. Using a stochastic variational inequality, we characterize its solution.

The progressive hedging algorithm (PHA) is an effective solution method for solving monotone stochastic variational inequalities (SVIs). However, this validity is based on the assumption of global maximal monotonicity. In this paper, we propose a localized PHA for solving nonmonotone SVIs and show that its validity is based on the weaker assumption of locally elicitable maximal monotonicity. Furthermore, we prove that such assumption holds when the mapping involved in the SVI is locally elicitable monotone or locally monotone. The local convergence of the proposed algorithm is established, and it is shown that the localized PHA has the rate of linear convergence under some mild assumptions. Some numerical experiments, including a two-stage orange market problem and randomly generated two-stage piecewise stochastic linear complementarity problems, indicate that the proposed algorithm is efficient. Funding: This work was supported by the National Natural Science Foundation of China [Grant 12171271].

Method with batching for stochastic finite-sum variational inequalities in non-Euclidean setting

The paper deals with the random time-dependent oligopolistic market equilibrium problem. For such a problem the firms’ point of view has been analyzed in Barbagallo and Guarino Lo Bianco (Optim. Lett. 14: 2479–2493, 2020) while here the policymaker’s point of view is studied. The random dynamic optimal control equilibrium conditions are expressed by means of an inverse stochastic time-dependent variational inequality which is proved to be equivalent to a stochastic time-dependent variational inequality. Some existence and well-posedness results for optimal regulatory taxes are obtained. Moreover a numerical scheme to compute the solution to the stochastic time-dependent variational inequality is presented. Finally an example is discussed.

Contributors

<p style='text-indent:20px;'>This paper focuses on the quantitative stability analysis of the expected residual minimization (ERM) formulation for a class of stochastic linear variational inequalities. Firstly, the existence of solutions of the ERM formulation and its perturbed problem is discussed. Then, the quantitative stability of the ERM formulation is derived under suitable probability metrics. Finally, the sample average approximation (SAA) problem of the ERM formulation is studied, and the rates of convergence of optimal solution sets are obtained under different assumptions.</p>

In this paper, we consider stochastic vector variational inequality problems (SVVIPs). Because of the existence of stochastic variable, the SVVIP may have no solutions generally. For solving this problem, we employ the regularized gap function of SVVIP to the loss function and then give a low-risk conditional value-at-risk (CVaR) model. However, this low-risk CVaR model is difficult to solve by the general constraint optimization algorithm. This is because the objective function is nonsmoothing function, and the objective function contains expectation, which is not easy to be computed. By using the sample average approximation technique and smoothing function, we present the corresponding approximation problems of the low-risk CVaR model to deal with these two difficulties related to the low-risk CVaR model. In addition, for the given approximation problems, we prove the convergence results of global optimal solutions and the convergence results of stationary points, respectively. Finally, a numerical experiment is given.

Stochastic variational inequalities for elasto-plastic oscillators

We give a new characterization of the Snell envelope of a given process as the unique solution of the stochastic variational inequality (SVI) in this article. This approach leads to several a priori estimates for the Snell envelopes and their components. The valuation for American Contingent Claims (ACC) in general financial market model is considered as an application. The robustness of the optimal portfolio/consumption processes with respect to the payoff function is established.

Method of weighted expected residual for solving stochastic variational inequality problems

In this paper, we study the competition among the primary users (PUs) in a Dynamic Spectrum Leasing (DSL) system where multiple PUs lease spectrum to the secondary users (SUs) for monetary rewards. Considering the uncertainties of the PUs' channel gains and of the SUs' demands for spectrum, the competition among the PUs is formulated as a stochastic Nash game. Due to the uncertainties, the PUs aim to maximize their long term utilities which are related to the income from leasing spectrum and to their quality of service (QoS) conditions. Resorting to the stochastic variational inequality (SVI) theory, we investigate the existence and uniqueness of the stochastic Nash equilibrium (SNE). Based on the stochastic approximation theory, we propose a distributed learning algorithm for computing the SNE of the game. Rigorous convergence proof of the algorithm is provided. Besides, the features of the algorithm are demonstrated via numerical results.