arXiv subjects

Anton Braverman

Publications and source records attributed to Anton Braverman.

17 recordsLinked to original sources

arXiv2026

Diffusion approximation error for queueing systems with general primitives

We investigate the steady-state diffusion-approximation error for continuous-time queueing systems with generally distributed primitives. A common picture emerges after analyzing a number of canonical systems: the error decomposes into interior and boundary terms. The former are simpler to handle and can be bounded using only low-order moments of the system's primitives -- when the approximation error is measured using the Wasserstein distance, three moments suffice. The boundary terms are inherently more delicate: while crude bounds are easy to obtain, sharper (e.g., order optimal) bounds require deeper, model specific, insights. Methodologically, we extend the generator comparison approach of Stein's method to piecewise-deterministic Markov processes (PDMPs). The discontinuous nature of the PDMP at jump times necessitates using the basic adjoint relationship (BAR), instead of the infinitesimal generator, to characterize the stationary distribution. A second-order Taylor expansion of the BAR jump terms, coupled with a Palm-inversion step that converts event-averaged quantities into time averages, yields the candidate diffusion generator and a transparent interior/boundary error decomposition. In parallel, we show how the prelimit generator approach -- working with the Poisson equation of the queueing system instead of the diffusion process -- offers a promising avenue for bounding the challenging boundary terms.

math.PR↗

arXiv2026

Stein's method for models with general clocks: A tutorial

Diffusion approximations are widely used in the analysis of service systems, providing tractable insights into complex models. While heavy-traffic limit theorems justify these approximations asymptotically, they do not quantify the error when the system is not in the limit regime. This paper presents a tutorial on the generator comparison approach of Stein's method for analyzing diffusion approximations in Markovian models where state transitions are governed by general clocks, which extends the well-established theory for continuous-time Markov chains and enables non-asymptotic error bounds for these approximations. Building on recent work that applies this method to single-clock systems, we develop a framework for handling models with multiple general clocks. Our approach is illustrated through canonical queueing systems, including the G/G/1 queue, the join-the-shortest-queue system, and the tandem queue. We highlight the role of the Palm inversion formula and the compensated queue-length process in extracting the diffusion generator. Most of our error terms depend only on the first three moments of the general clock distribution. The rest require deeper, model-specific, insight to bound, but could in theory also depend on only the first three moments.

math.PR↗

arXiv2024

The BAR approach for multiclass queueing networks with SBP service policies

The basic adjoint relationship (BAR) approach is an analysis technique based on the stationary equation of a Markov process. This approach was introduced to study heavy-traffic, steady-state convergence of generalized Jackson networks in which each service station has a single job class. We extend it to multiclass queueing networks operating under static-buffer-priority (SBP) service disciplines. Our extension makes a connection with Palm distributions that allows one to attack a difficulty arising from queue-length truncation, which appears to be unavoidable in the multiclass setting. For multiclass queueing networks operating under SBP service disciplines, our BAR approach provides an alternative to the "interchange of limits" approach that has dominated the literature in the last twenty years. The BAR approach can produce sharp results and allows one to establish steady-state convergence under three additional conditions: stability, state space collapse (SSC) and a certain matrix being "tight." These three conditions do not appear to depend on the interarrival and service-time distributions beyond their means, and their verification can be studied as three separate modules. In particular, they can be studied in a simpler, continuous-time Markov chain setting when all distributions are exponential. As an example, these three conditions are shown to hold in reentrant lines operating under last-buffer-first-serve discipline. In a two-station, five-class reentrant line, under the heavy-traffic condition, the tight-matrix condition implies both the stability condition and the SSC condition. Whether such a relationship holds generally is an open problem.

math.PR↗

arXiv2023

Steady-state Dirichlet approximation of the Wright-Fisher model using the prelimit generator comparison approach of Stein's method

The Wright-Fisher model, originating in Wright (1931) is one of the canonical probabilistic models used in mathematical population genetics to study how genetic type frequencies evolve in time. In this paper we bound the rate of convergence of the stationary distribution for a finite population Wright-Fisher Markov chain with parent independent mutation to the Dirichlet distribution. Our result improves the rate of convergence established in Gan et al. (2017) from $O(1/\sqrt{N})$ to $O(1/N)$. The results are derived using Stein's method, in particular, the prelimit generator comparison method.

math.PR↗

arXiv2022

The join-the-shortest-queue system in the Halfin-Whitt regime: rates of convergence to the diffusion limit

We show that the steady-state distribution of the join-the-shortest-queue (JSQ) system converges, in the Halfin-Whitt regime, to its diffusion limit at a rate of at least $1/\sqrt{n}$, where $n$ is the number of servers. Our proof uses Stein's method and, specifically, the recently proposed prelimit generator comparison approach. The JSQ system is non-trivial, high-dimensional, and has a state-space collapse component, and our analysis may serve as a helpful example to readers wishing to apply the approach to their own setting.

math.PR↗

arXiv2022

High order steady-state diffusion approximations

We derive and analyze new diffusion approximations of stationary distributions of Markov chains that are based on second- and higher-order terms in the expansion of the Markov chain generator. Our approximations achieve a higher degree of accuracy compared to diffusion approximations widely used for the past fifty years, while retaining a similar computational complexity. To support our approximations, we present a combination of theoretical and numerical results across three different models. Our approximations are derived recursively through Stein/Poisson equations, and the theoretical results are proved using Stein's method.

math.PR↗

arXiv2022

Steady-state diffusion approximations of Markov chains: error analysis via the discrete Poisson equation

This paper uses the generator approach of Stein's method to analyze the gap between steady-state distributions of Markov chains and diffusion processes. Until now, the standard way to invoke Stein's method for this problem was to use the Poisson equation for the diffusion as a starting point. The main technical difficulty with this approach is obtaining bounds on the derivatives of the solution to the Poisson equation, also known as Stein factor bounds. In this paper we propose starting with the discrete Poisson equation of the Markov chain. An important step in our approach is extending the discrete Poisson equation to be defined on the continuum where the diffusion is defined, and we achieve this by using interpolation. Although there are still Stein factor bounds to prove, these now correspond to the finite differences of the discrete Poisson equation solution, as opposed to the derivatives of the solution to the continuous one. Discrete Stein factor bounds can be easier to obtain, for instance when the drift is not everywhere differentiable, when the diffusion has a state-dependent diffusion coefficient, or in the presence of a reflecting boundary condition. We use the join the shortest queue model in the Halfin-Whitt regime as a working example to illustrate the methodology. We show that the steady-state approximation error of the diffusion limit converges to zero at a rate of $1/\sqrt{n}$, where $n$ is the number of servers in the system.

math.PR↗

arXiv2021

The prelimit generator comparison approach of Stein's method

This paper uses the generator comparison approach of Stein's method to analyze the gap between steady-state distributions of Markov chains and diffusion processes. The "standard" generator comparison approach starts with the Poisson equation for the diffusion, and the main technical difficulty is to obtain bounds on the derivatives of the solution to the Poisson equation, also known as Stein factor bounds. In this paper we propose starting with the Poisson equation of the Markov chain; we term this the prelimit approach. Although one still needs Stein factor bounds, they now correspond to finite differences of the Markov chain Poisson equation solution rather than the derivatives of the solution to the diffusion Poisson equation. In certain cases, the former are easier to obtain. We use the $M/M/1$ model as a simple working example to illustrate our approach.

math.PR↗

arXiv2019

Steady-state analysis of the Join the Shortest Queue model in the Halfin-Whitt regime

This paper studies the steady-state properties of the Join the Shortest Queue model in the Halfin-Whitt regime. We focus on the process tracking the number of idle servers, and the number of servers with non-empty buffers. Recently, Eschenfeldt & Gamarnik (2015) proved that a scaled version of this process converges, over finite time intervals, to a two-dimensional diffusion limit as the number of servers goes to infinity. In this paper we prove that the diffusion limit is exponentially ergodic, and that the diffusion scaled sequence of the steady-state number of idle servers and non-empty buffers is tight. Our results mean that the process-level convergence proved in Eschenfeldt & Gamarnik (2015) implies convergence of steady-state distributions. The methodology used is the generator expansion framework based on Stein's method, also referred to as the drift-based fluid limit Lyapunov function approach in Stolyar (2015). One technical contribution to the framework is to show that it can be used as a general tool to establish exponential ergodicity.

math.PR↗

arXiv2018

Empty-car routing in ridesharing systems

This paper considers a closed queueing network model of ridesharing systems such as Didi Chuxing, Lyft, and Uber. We focus on empty-car routing, a mechanism by which we control car flow in the network to optimize system-wide utility functions, e.g. the availability of empty cars when a passenger arrives. We establish both process-level and steady-state convergence of the queueing network to a fluid limit in a large market regime where demand for rides and supply of cars tend to infinity, and use this limit to study a fluid-based optimization problem. We prove that the optimal network utility obtained from the fluid-based optimization is an upper bound on the utility in the finite car system for any routing policy, both static and dynamic, under which the closed queueing network has a stationary distribution. This upper bound is achieved asymptotically under the fluid-based optimal routing policy. Simulation results with real-world data released by Didi Chuxing demonstrate the benefit of using the fluid-based optimal routing policy compared to various other policies.

math.PR↗

arXiv2018

On the Taylor Expansion of Value Functions

We introduce a framework for approximate dynamic programming that we apply to discrete time chains on $\mathbb{Z}_+^d$ with countable action sets. Our approach is grounded in the approximation of the (controlled) chain's generator by that of another Markov process. In simple terms, our approach stipulates applying a second-order Taylor expansion to the value function to replace the Bellman equation with one in continuous space and time where the transition matrix is reduced to its first and second moments. In some cases, the resulting equation (which we label {\bf TCP}) can be interpreted as corresponding to a Brownian control problem. When tractable, the TCP serves as a useful modeling tool. More generally, the TCP is a starting point for approximation algorithms. We develop bounds on the optimality gap---the sub-optimality introduced by using the control produced by the "Taylored" equation. These bounds can be viewed as a conceptual underpinning, analytical rather than relying on weak convergence arguments, for the good performance of controls derived from Brownian control problems. We prove that, under suitable conditions and for suitably "large" initial states, (i) the optimality gap is smaller than a $1-α$ fraction of the optimal value, where $α\in (0,1)$ is the discount factor, and (ii) the gap can be further expressed as the infinite horizon discounted value with a "lower-order" per period reward. Computationally, our framework leads to an "aggregation" approach with performance guarantees. While the guarantees are grounded in PDE theory, the practical use of this approach requires no knowledge of that theory.

math.OC↗

arXiv2017

Heavy traffic approximation for the stationary distribution of a generalized Jackson network: the BAR approach

In the seminal paper of Gamarnik and Zeevi (2006), the authors justify the steady-state diffusion approximation of a generalized Jackson network (GJN) in heavy traffic. Their approach involves the so-called limit interchange argument, which has since become a popular tool employed by many others who study diffusion approximations. In this paper we illustrate a novel approach by using it to justify the steady-state approximation of a GJN in heavy traffic. Our approach involves working directly with the basic adjoint relationship (BAR), an integral equation that characterizes the stationary distribution of a Markov process. As we will show, the BAR approach is a more natural choice than the limit interchange approach for justifying steady-state approximations, and can potentially be applied to the study of other stochastic processing networks such as multiclass queueing networks.

math.PR↗

arXiv2017

Stein's method for steady-state diffusion approximations

Diffusion approximations have been a popular tool for performance analysis in queueing theory, with the main reason being tractability and computational efficiency. This dissertation is concerned with establishing theoretical guarantees on the performance of steady-state diffusion approximations of queueing systems. We develop a modular framework based on Stein's method that allows us to establish error bounds, or convergence rates, for the approximations. We apply this framework three queueing systems: the Erlang-C, Erlang-A, and $M/Ph/n+M$ systems. The former two systems are simpler and allow us to showcase the full potential of the framework. Namely, we prove that both Wasserstein and Kolmogorov distances between the stationary distribution of a normalized customer count process, and that of an appropriately defined diffusion process decrease at a rate of $1/\sqrt{R}$, where $R$ is the offered load. Futhermore, these error bounds are \emph{universal}, valid in any load condition from lightly loaded to heavily loaded. For the Erlang-C model, we also show that a diffusion approximation with state-dependent diffusion coefficient can achieve a rate of convergence of $1/R$, which is an order of magnitude faster when compared to approximations with constant diffusion coefficients.

math.PR↗

arXiv2017

Stein's method for steady-state diffusion approximations: an introduction through the Erlang-A and Erlang-C models

This paper provides an introduction to the Stein method framework in the context of steady-state diffusion approximations. The framework consists of three components: the Poisson equation and gradient bounds, generator coupling, and moment bounds. Working in the setting of the Erlang-A and Erlang-C models, we prove that both Wasserstein and Kolmogorov distances between the stationary distribution of a normalized customer count process, and that of an appropriately defined diffusion process decrease at a rate of $1/\sqrt{R}$, where $R$ is the offered load. Futhermore, these error bounds are \emph{universal}, valid in any load condition from lightly loaded to heavily loaded.

math.PR↗

arXiv2016

High order steady-state diffusion approximation of the Erlang-C system

In this paper we introduce a new diffusion approximation for the steady-state customer count of the Erlang-C system. Unlike previous diffusion approximations, which use the steady-state distribution of a diffusion process with a constant diffusion coefficient, our approximation uses the steady-state distribution of a diffusion process with a \textit{state-dependent} diffusion coefficient. We show, both analytically and numerically, that our new approximation is an order of magnitude better than its counterpart. To obtain the analytical results, we use Stein's to show that a variant of the Wasserstein distance between the normalized customer count distribution and our approximation vanishes at a rate of $1/R$, where $R$ is the offered load to the system. In contrast, the previous approximation only achieved a rate of $1/R$. We hope our results motivate others to consider diffusion approximations with state-dependent diffusion coefficients.

math.PR↗

arXiv2015

Stein's method for steady-state diffusion approximations of $M/Ph/n+M$ systems

We consider $M/Ph/n+M$ queueing systems in steady state. We prove that the Wasserstein distance between the stationary distribution of the normalized system size process and that of a piecewise Ornstein-Uhlenbeck (OU) process is bounded by $C/\sqrtλ$, where the constant $C$ is independent of the arrival rate $λ$ and the number of servers $n$ as long as they are in the Halfin-Whitt parameter regime. For each integer $m>0$, we also establish a similar bound for the difference of the $m$th steady-state moments. For the proofs, we develop a modular framework that is based on Stein's method. The framework has three components: Poisson equation, generator coupling, and state space collapse. The framework, with further refinement, is likely applicable to steady-state diffusion approximations for other stochastic systems.

math.PR↗

arXiv2015

Poisson statistics of eigenvalues in the hierarchical Dyson model

Let $(X,d)$ be a locally compact separable ultrametric space. Given a measure $m$ on $X$ and a function $C$ defined on the set $\mathcal{B}$ of all balls $B\subset X$ we consider the hierarchical Laplacian $L=L_{C}$. The operator $L$ acts in $L^{2}(X,m)$, is essentially self-adjoint, and has a purely point spectrum. Choosing a family $\{\varepsilon(B)\}_{B\in \mathcal{B}}$ of i.i.d. random variables, we define the perturbed function $\mathcal{C}(B)=C(B)(1+\varepsilon(B))$ and the perturbed hierarchical Laplacian $\mathcal{L}=L_{\mathcal{C}}$. All outcomes of the perturbed operator $\mathcal{L}$ are hierarchical Laplacians. In particular they all have purely point spectrum. We study the empirical point process $M$ defined in terms of $\mathcal{L}$-eigenvalues. Under some natural assumptions $M$ can be approximated by a Poisson point process. Using a result of Arratia, Goldstein, and Gordon based on the Chen-Stein method, we provide total variation convergence rates for the Poisson approximation. We apply our theory to random perturbations of the operator $\mathfrak{D}^{α}$, the $p$-adic fractional derivative of order $α>0$. This operator, related to the concept of $p$-adic Quantum Mechanics, is a hierarchical Laplacian which acts in $L^{2}(X,m)$ where $X=\mathbb{Q}_{p}$ is the field of $p$-adic numbers and $m$ is Haar measure. It is translation invariant and the set $\mathsf{Spec}(\mathfrak{D}^{α})$ consists of eigenvalues $p^{αk}$, $k\in \mathbb{Z}$, each of which has infinite multiplicity.

math.PR↗