A linear quadratic optimal stochastic control problem with random coefficients and indefinite state/control weight costs is usually linked to an indefinite stochastic Riccati equation (SRE), which is a matrix-valued quadratic backward stochastic differential equation along with an algebraic constraint involving the unknown. Either the optimal control problem or the SRE is solvable only if the g...

In this paper, two-dimensional percolation lattices are applied to describe wireless propagation environment, and stochastic rays are employed to model the trajectories of radio waves. We first derive the probability that a stochastic ray undergoes certain number of collisions at a specific spatial location. Three classes of stochastic rays with different constraint conditions are considered: s...

A stochastic H∞ and a mixed, stochastic, H2/H∞ control problem for discrete-time systems are considered and solved. Conditions for existence of a solution are derived, based on the solvability of an equivalent minimax problem. In this framework, it is possible to consider at the same time both stochastic and deterministic disturbances highlighting their mutual effects. The controller can be des...

We introduce a continuous time stochastic broadcast calculus for mobile and wireless networks. The mobility between nodes in a network is modeled by a stochastic mobility function which allows to change part of a network topology depending on an exponentially distributed delay and a network topology constraint. We allow continuous time stochastic behavior of processes running at network nodes, ...

A critical aspect in the modeling of biological systems is the description view point. On the one hand, the Stochastic π-calculus formalism provides an intuitive and compact representation from an internal perspective. On the other hand, other proposed languages such as Hybrid Automata and Stochastic Concurrent Constraint Programming introduce in the system description an external control and p...

For a large number of random constraint satisfaction problems, such as random k-SAT and random graph and hypergraph coloring, we have very good estimates of the largest constraint density for which solutions exist. All known polynomial-time algorithms for these problems, though, already fail to find solutions at much lower densities. To understand the origin of this gap we study how the structu...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to...

In this work we study optimization problems subject to a failure constraint. This constraint is expressed in terms of a condition that causes failure, representing a physical or technical breakdown. We formulate the problem in terms of a probability constraint, where the level of “confidence” is a modelling parameter and has the interpretation that the probability of failure should not exceed t...

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-fie...

In this paper, a retail facility which has front room and back room operations, and crosstrained workers, is considered. In particular, it is of interest to the facility to determine optimal times for switching workers between the two rooms under stochastic customer arrival so that expected customer waiting time is minimized, but all work in the back room is performed. Three constraint programm...