In this paper, we consider an approximation of the ordering cone by a sequence of convex well-based cones that are contained inside the original cone. We derive related results on approximating minimal point sequences and propose a density result being evocative of the well-known ABB theorem.

We tackle the issue of finding a good policy when the number of policy updates is limited. This is done by approximating the expected policy reward as a sequence of concave lower bounds which can be efficiently maximized, drastically reducing the number of policy updates required to achieve good performance. We also extend existing methods to negative rewards, enabling the use of control variates.

Abstract This paper develops strong convergence of the Euler–Maruyama (EM) schemes for approximating McKean–Vlasov stochastic differential equations (SDEs). In contrast to existing work, a novel feature is use much weaker condition—local Lipschitzian in state variable, but under uniform linear growth assumption. To obtain desired approximation, first establishes existence and uniqueness solutio...

<p style='text-indent:20px;'>Using the concept of Bregman divergence, we propose a new subgradient extragradient method for approximating common solution pseudo-monotone and Lipschitz continuous variational inequalities fixed point problem in real Hilbert spaces. The algorithm uses self-adjustment rule selecting stepsize each iteration also, prove strong convergence result sequence genera...

The gap between a recently developed dynamical version of relaxed magnetohydrodynamics (RxMHD) and ideal MHD (IMHD) is bridged by approximating the zero-resistivity "Ideal" Ohm's Law (IOL) constraint using an augmented Lagrangian method borrowed from optimization theory. augmentation combines pointwise vector Lagrange multiplier global penalty function can be used either for iterative enforceme...