Convergence of Tseng-type self-adaptive algorithms for variational inequalities and fixed point problems

In this paper, we present a Tseng-type self-adaptive algorithm for solving a variational inequality and a fixed point problem involving pseudomonotone and pseudocontractive operators in Hilbert spaces. A weak convergent result for such algorithm is proved under a weaker assumption than sequentially weakly continuous imposed on the pseudomonotone operator. Some corollaries are also included.

On totally global solvability of evolutionary equation with unbounded operator

Let $X$ be a Hilbert space, $U$ be a Banach space, $G\colon X\to X$ be a linear operator such that the operator $B_\lambda=\lambda I-G$ is maximal monotone with some (arbitrary given) $\lambda\in\mathbb{R}$. For the Cauchy problem associated with controlled semilinear evolutionary equation as follows \[x^\prime(t)=Gx(t)+f\bigl( t,x(t),u(t)\bigr),\quad t\in[0;T];\quad x(0)=x_0\in X,\] where $u=u(t)\colon[0;T]\to U$ is a control, $x(t)$ is unknown function with values in $X$, we prove the totally (with respect to a set of admissible controls) global solvability subject to global solvability of the Cauchy problem associated with some ordinary differential equation in the space $\mathbb{R}$. Solution $x$ is treated in weak sense and is sought in the space $\mathbb{C}_w\bigl([0;T];X\bigr)$ of weakly continuous functions. In fact, we generalize a similar result having been proved by the author formerly for the case of bounded operator $G$. The essence of this generalization consists in that postulated properties of the operator $B_\lambda$ give us the possibility to construct Yosida approximations for it by bounded linear operators and thus to extend required estimates from “bounded” to “unbounded” case. As examples, we consider initial boundary value problems associated with the heat equation and the wave equation.

CONVERGENCE OF THE EXTRAPOLATION METHOD FROM THE PAST AND THE OPERATOR EXTRAPOLATION METHOD

One of the popular areas of modern applied nonlinear analysis is the study of variational inequalities. Many important problems of operations research and mathematical physics can be written in the form of variational inequalities. With the advent of generating adversarial neural networks, interest in algorithms for solving variational inequalities arose in the ML-community. This paper is devoted to the study of three new algorithms with Bregman projection for solving variational inequalities in Hilbert space. The first algorithm is the result of a modification of the two-stage Bregman method by low-cost adjusting the step size that without the prior knowledge of the Lipschitz constant of operator. The second algorithm, which we call the operator extrapolation algorithm, is obtained by replacing the Euclidean metric in the Malitsky–Tam method with the Bregman divergence. An attractive feature of the algorithm is only one computation at the iterative step of the Bregman projection onto the feasible set. The third algorithm is an adaptive version of the second, where the used rule for updating the step size does not require knowledge of Lipschitz constants and the calculation of operator values at additional points. For variational inequalities with pseudo-monotone, Lipschitz-continuous, and sequentially weakly continuous operators acting in a Hilbert space, convergence theorems are proved.

Subgame‐perfect equilibrium in games with almost perfect information: Dispensing with public randomization

Harris, Reny, and Robson (1995) added a public randomization device to dynamic games with almost perfect information to ensure existence of subgame perfect equilibria (SPE). We show that when Nature's moves are atomless in the original game, public randomization does not enlarge the set of SPE payoffs: any SPE obtained using public randomization can be “decorrelated” to produce a payoff‐equivalent SPE of the original game. As a corollary, we provide an alternative route to a result of He and Sun (2020) on existence of SPE without public randomization, which in turn yields equilibrium existence for stochastic games with weakly continuous state transitions.

On solving variational inequalities defined on fixed point sets of multivalued mappings in Banach spaces

We are concerned with the problem of solving variational inequalities which are defined on the set of fixed points of a multivalued nonexpansive mapping in a reflexive Banach space. Both implicit and explicit approaches are studied. Strong convergence of the implicit method is proved if the space satisfies Opial’s condition and has a duality map weakly continuous at zero, and the strong convergence of the explicit method is proved if the space has a weakly continuous duality map. An essential assumption on the multivalued nonexpansive mapping is that the mapping be single valued on its nonempty set of fixed points.

Strong Convergent Theorems Governed by Pseudo-Monotone Mappings

The purpose of this paper is to introduce two different kinds of iterative algorithms with inertial effects for solving variational inequalities. The iterative processes are based on the extragradient method, the Mann-type method and the viscosity method. Convergence theorems of strong convergence are established in Hilbert spaces under mild assumption that the associated mapping is Lipschitz continuous, pseudo-monotone and sequentially weakly continuous. Numerical experiments are performed to illustrate the behaviors of our proposed methods, as well as comparing them with the existing one in literature.