Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these are amenable to automation and allow online, scalable, universal approximate Bayesian inference. Unfortunately, common Probabilistic Programming Languages (PPLs) with stochastic engines lack efficiency of message passing-based algorithms deterministic...

The purpose of this paper is to present a framework for optimal stochastic power control in interference limited fading wireless channels. The framework is based on applying stochastic programming methods to optimal transmit power allocation. Numerical examples based on applying interior point algorithms to the optimal power control problem are discussed. Furthermore, an application of the stoc...

Linear stochastic programming problems with first order stochastic dominance (FSD) constraints are non-convex. For their mixed 0–1 linear programming formulation we present two convex relaxations based on second order stochastic dominance (SSD). We develop necessary and sufficient conditions for FSD, used to obtain a disjunctive programming formulation and to strengthen one of the SSD-based rel...