arXiv subjects

George Yin

Publications and source records attributed to George Yin.

At least 55 records · Page 3Linked to original sources

arXiv2017

Recurrence for linearizable switching diffusion with past dependent switching and countable state space

This work continues and substantially extends our recent work on switching diffusions with the switching processes that depend on the past states and that take values in a countable state space. That is, the discrete components of the two-component processes take values in a countably infinite set and its switching rates at current time depend on the current value of the continuous component. This paper focuses on recurrence, positive recurrence, and weak stabilization of such systems. In particular, the paper aims to providing more verifiable conditions on recurrence and positive recurrence and related issues. Assuming that the system is linearizable, it provides feasible conditions focusing on the coefficients of the systems for positive recurrence. Then linear feedback controls for weak stabilization are considered. Some illustrative examples are also given.

math.PR↗

arXiv2017

Long-Run Average Sustainable Harvesting Policies: Near Optimality

This paper develops near-optimal sustainable harvesting strategies for the predator in a predator-prey system. The objective function is of long-run average per unit time type. To date, ecological systems under environmental noise are usually modeled as stochastic differential equations driven by a Brownian motion. Recognizing that the formulation using a Brownian motion is only an idealization, in this paper, it is assumed that the environment is subject to disturbances characterized by a jump process with rapid jump rates. Under broad conditions, it is shown that the systems under consideration can be approximated by a controlled diffusion system. Based on the limit diffusion system, control policies of the original systems are constructed. Such an approach enables us to develop sustainable harvesting policies leading to near optimality. To treat the underlying problems, one of the main difficulties is due to the long-run average objective function. This in turn, requires the handling of a number of issues related to ergodicity. New approaches are developed to obtain the tightness of the underlying processes based on the population dynamic systems.

math.PR↗

arXiv2017

Cyber-Physical Interference Modeling for Predictable Reliability of Inter-Vehicle Communications

Predictable inter-vehicle communication reliability is a basis for the paradigm shift from the traditional singlevehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing mechanisms of inter-vehicle communications, however, makes them incapable of ensuring predictable communication reliability. For predictable interference control, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility to PRK-based scheduling. In particular, CPS leverage physical locations of vehicles to define the gPRK interference model, a geometric approximation of the PRK model, for effective interference relation estimation, and CPS leverages cyber-physical structures of vehicle traffic flows (particularly, spatiotemporal interference correlation as well as macro- and micro-scopic vehicle dynamics) for effective use of the gPRK model. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications.

cs.DC↗

arXiv2017

Limit Cycles of Dynamic Systems under Random Perturbations with Rapid Switching and Slow Diffusion: A Multi-Scale Approach

This work is devoted to examining qualitative properties of dynamic systems, in particular, limit cycles of stochastic differential equations with both rapid switching and small diffusion. The systems are featured by multi-scale formulation, highlighted by the presence of two small parameters $ε$ and $δ$. Associated with the underlying systems, there are averaged or limit systems. Suppose that for each pair of the parameters, the solution of the corresponding equation has an invariant probability measure $μ^{ε,δ}$, and that the averaged equation has a limit cycle in which there is an averaged occupation measure $μ^0$ for the averaged equation. Our main effort is to prove that $μ^{ε,δ}$ converges weakly to $μ^0$ as $ε\to 0$ and $δ\to 0$ under suitable conditions. Moreover, our results are applied to a stochastic predator-prey model together with numerical examples for demonstration.

math.DS↗

arXiv2017

Recurrence and Ergodicity of Switching Diffusions with Past-Dependent Switching Having A Countable State Space

This work focuses on recurrence and ergodicity of switching diffusions consisting of continuous and discrete components, in which the discrete component takes values in a countably infinite set and the rates of switching at current time depend on the value of the continuous component over an interval including certain past history. Sufficient conditions for recurrence and ergodicity are given. Moreover, the relationship between systems of partial differential equations and recurrence when the switching is past-independent is established under suitable conditions.

math.PR↗

arXiv2017

Certain Properties Related to Well Posedness of Switching Diffusions

This work is devoted to switching diffusions that have two components (a continuous component and a discrete component). Different from the so-called Markovian switching diffusions, in the setup, the discrete component (the switching) depends on the continuous component (the diffusion process). The objective of this paper is to provide a number of properties related to the well posedness. First, the differentiability with respect to initial data of the continuous component is established. Then, further properties including uniform continuity with respect to initial data, and smoothness of certain functionals are obtained. Moreover, Feller property is obtained under only local Lipschitz continuity. Finally, an example of Lotka-Voterra model under regime switching is provided as an illustration.

math.PR↗

arXiv2017

Stochastic population growth in spatially heterogeneous environments: The density-dependent case

This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for resources, spatio-temporal heterogeneity and dispersal. The population is spread throughout $n$ patches whose population abundances are modelled as the solutions of a system of nonlinear stochastic differential equations living on $[0,\infty)^n$. We prove that $r$, the stochastic growth rate of the total population in the absence of competition, determines the long-term behaviour of the population. The parameter $r$ can be expressed as the Lyapunov exponent of an associated linearized system of stochastic differential equations. Detailed analysis shows that if $r>0$, the population abundances converge polynomially fast to a unique invariant probability measure on $(0,\infty)^n$, while when $r<0$, the population abundances of the patches converge almost surely to $0$ exponentially fast. This generalizes and extends the results of Evans et al (2014 J. Math. Biol.) and proves one of their conjectures. Compared to recent developments, our model incorporates very general density-dependent growth rates and competition terms. Furthermore, we prove that persistence is robust to small, possibly density dependent, perturbations of the growth rates, dispersal matrix and covariance matrix of the environmental noise. Our work allows the environmental noise driving our system to be degenerate. This is relevant from a biological point of view since, for example, the environments of the different patches can be perfectly correlated. As an example we fully analyze the two-patch case, $n=2$, and show that the stochastic growth rate is a decreasing function of the dispersion rate. In particular, coupling two sink patches can never yield persistence, in contrast to the results from the non-degenerate setting treated by Evans et al.

math.PR↗

arXiv2017

Modeling and Analysis of Switching Diffusion Systems: Past-Dependent Switching with a Countable State Space

Motivated by networked systems in random environment and controlled hybrid stochastic dynamic systems, this work focuses on modeling and analysis of a class of switching diffusions consisting of continuous and discrete components. Novel features of the models include the discrete component taking values in a countably infinite set, and the switching depending on the value of the continuous component involving past history. In this work, the existence and uniqueness of solutions of the associated stochastic differential equations are obtained. In addition, Markov and Feller properties of a function-valued stochastic process associated with the hybrid diffusion are also proved. In particular, when the switching rates depend only on the current state, strong Feller properties are obtained. These properties will pave a way for future study of control design and optimization of such dynamic systems.

math.PR↗

arXiv2017

Critical Connectivity and Fastest Convergence Rates of Distributed Consensus with Switching Topologies and Additive Noises

Consensus conditions and convergence speeds are crucial for distributed consensus algorithms of networked systems. Based on a basic first-order average-consensus protocol with time-varying topologies and additive noises, this paper first investigates its critical consensus condition on network topology by stochastic approximation frameworks. A new joint-connectivity condition called extensible joint-connectivity that contains a parameter $δ$ (termed the extensible exponent) is proposed. With this and a balanced topology condition, we show that a critical value of $δ$ for consensus is $1/2$. Optimization on convergence rate of this protocol is further investigated. It is proved that the fastest convergence rate, which is the theoretic optimal rate among all controls, is of the order $1/t$ for the best topologies, and is of the order $1/t^{1-2δ}$ for the worst topologies which are balanced and satisfy the extensible joint-connectivity condition. For practical implementation, certain open-loop control strategies are introduced to achieve consensus with a convergence rate of the same order as the fastest convergence rate. Furthermore, a consensus condition is derived for non stationary and strongly correlated random topologies. The algorithms and consensus conditions are applied to distributed consensus computation of mobile ad-hoc networks; and their related critical exponents are derived from relative velocities of mobile agents for guaranteeing consensus.

math.OC↗

arXiv2017

Feynman-Kac Formulas for Regime-Switching Jump Diffusions and their Applications

This work develops Feynman-Kac formulas for a class of regime-switching jump diffusion processes, in which the jump part is driven by a Poisson random measure associated to a general Lévy process and the switching part depends on the jump diffusion processes. Under broad conditions, the connections of such stochastic processes and the corresponding partial integro-differential equations are established. Related initial, terminal, and boundary value problems are also treated. Moreover, based on weak convergence of probability measures, it is demonstrated that a sequence of random variables related to the regime-switching jump diffusion process converges in distribution to the arcsine law.

math.PR↗

arXiv2016

Coexistence and Exclusion of Stochastic Competitive Lotka-Volterra Models

This work derives sufficient conditions for the coexistence and exclusion of a stochastic competitive Lotka-Volterra model. The conditions obtained are close to the necessary conditions. In addition, convergence in distribution of positive solutions of the model is also established. A number of numerical examples are given to illustrate our results.

math.PR↗

arXiv2016

Two-time-scale stochastic partial differential equations driven by $α$-stable noises: Averaging principles

This paper focuses on stochastic partial differential equations (SPDEs) under two-time-scale formulation. Distinct from the work in the existing literature, the systems are driven by $α$-stable processes with $α\in(1,2)$. In addition, the SPDEs are either modulated by a continuous-time Markov chain with a finite state space or have an addition fast jump component. The inclusion of the Markov chain is for the needs of treating random environment, whereas the addition of the fast jump process enables the consideration of discontinuity in the sample paths of the fast processes. Assuming either a fast changing Markov switching or an additional fast-varying jump process, this work aims to obtain the averaging principles for such systems. There are several distinct difficulties. First, the noise is not square integrable. Second, in our setup, for the underlying SPDE, there is only a unique mild solution and as a result, there is only mild Itô's formula that can be used. Moreover, another new aspect is the addition of the fast regime switching and the addition of the fast varying jump processes in the formulation, which enlarges the applicability of the underlying systems. To overcome these difficulties, a semigroup approach is taken. Under suitable conditions, it is proved that the $p$th moment convergence takes place with $p\in(1,α)$, which is stronger than the usual weak convergence approaches.

math.ST↗

arXiv2016

Conditions for Permanence and Ergodicity of Certain Stochastic Predator-Prey Models

This work derives sufficient conditions for the permanence and ergodicity of a stochastic predator-prey model with Beddington-DeAngelis functional response. The conditions obtained in fact are very close to the necessary conditions. Both non-degenerate and degenerate diffusions are considered. One of the distinctive features of our results is that our results enables characterization of the support of a unique invariant probability measure. It proves the convergence in total variation norm of the transition probability to the invariant measure. Comparisons to existing literature and related matters to other stochastic predator-prey models are also given.

math.PR↗

arXiv2015

A Strong Limit Theorem for Two-Time-Scale Fucntional Stochastic Differential Equations

This paper focuses on a class of two-time-scale functional stochastic differential equations, where the phase space of the segment processes is infinite-dimensional. It develops ergodicity of the fast component and obtains a strong limit theorem for the averaging principle in the spirit of Khasminskii's averaging approach for the slow component.

math.PR↗

arXiv2014

Analyzing Convergence and Rates of Convergence of Particle Swarm Optimization Algorithms Using Stochastic Approximation Methods

Recently, much progress has been made on particle swarm optimization (PSO). A number of works have been devoted to analyzing the convergence of the underlying algorithms. Nevertheless, in most cases, rather simplified hypotheses are used. For example, it often assumes that the swarm has only one particle. In addition, more often than not, the variables and the points of attraction are assumed to remain constant throughout the optimization process. In reality, such assumptions are often violated. Moreover, there are no rigorous rates of convergence results available to date for the particle swarm, to the best of our knowledge. In this paper, we consider a general form of PSO algorithms, and analyze asymptotic properties of the algorithms using stochastic approximation methods. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative algorithm. Then we analyze its convergence using weak convergence method. It is proved that a suitably scaled sequence of swarms converge to the solution of an ordinary differential equation. We also establish certain stability results. Moreover, convergence rates are ascertained by using weak convergence method. A centered and scaled sequence of the estimation errors is shown to have a diffusion limit.

math.OC↗

arXiv2014

Adaptive Search Algorithms for Discrete Stochastic Optimization: A Smooth Best-Response Approach

This paper considers simulation-based optimization of the performance of a regime-switching stochastic system over a finite set of feasible configurations. Inspired by the stochastic fictitious play learning rules in game theory, we propose an adaptive simulation-based search algorithm that uses a smooth best-response sampling strategy and tracks the set of global optima, yet distributes the search so that most of the effort is spent on simulating the system performance at the global optima. The algorithm converges weakly to the set of global optima even when the observation data is correlated (as long as a weak law of large numbers holds). Numerical examples show that the proposed scheme yields a faster convergence for finite sample lengths compared with several existing random search and pure exploration methods in the literature.

math.OC↗

arXiv2014

Mean-Variance Type Controls Involving a Hidden Markov Chain: Models and Numerical Approximation

Motivated by applications arising in networked systems, this work examines controlled regime-switching systems that stem from a mean-variance formulation. A main point is that the switching process is a hidden Markov chain. An additional piece of information, namely, a noisy observation of switching process corrupted by white noise is available. We focus on minimizing the variance subject to a fixed terminal expectation. Using the Wonham filter, we convert the partially observed system to a completely observable one first. Since closed-form solutions are virtually impossible be obtained, a Markov chain approximation method is used to devise a computational scheme. Convergence of the algorithm is obtained. A numerical example is provided to demonstrate the results.

math.OC↗

arXiv2013

UAV Circumnavigation of an Unknown Target Without Location Information Using Noisy Range-based Measurements

This paper proposes a control algorithm for a UAV to circumnavigate an unknown target at a fixed radius when the location information of the UAV is unavailable. By assuming that the UAV has a constant velocity, the control algorithm makes adjustments to the heading angle of the UAV based on range and range rate measurements from the target, which may be corrupted by additive measurement noise. The control algorithm has the added benefit of being globally smooth and bounded. Exploiting the relationship between range rate and bearing angle, we transform the system dynamics from Cartesian coordinate in terms of location and heading to polar coordinate in terms of range and bearing angle. We then formulate the addition of measurement errors as a stochastic differential equation. A recurrence result is established showing that the UAV will reach a neighborhood of the desired orbit in finite time. Some statistical measures of performance are obtained to support the technical analysis.

math.OC↗