On Numerical Approximations of Fractional and Nonlocal Mean Field Games

We construct numerical approximations for Mean Field Games with fractional or nonlocal diffusions. The schemes are based on semi-Lagrangian approximations of the underlying control problems/games along with dual approximations of the distributions of agents. The methods are monotone, stable, and consistent, and we prove convergence along subsequences for (i) degenerate equations in one space dimension and (ii) nondegenerate equations in arbitrary dimensions. We also give results on full convergence and convergence to classical solutions. Numerical tests are implemented for a range of different nonlocal diffusions and support our analytical findings.

     

Approximating the Mean Waiting Time in the <Emphasis Type="Italic">GI/G/s</Emphasis> Queue

We provide two distribution-dependent approximations for the mean waiting time in a GI/G/s queue. Both approximations are weighted combinations of the exact mean waiting times for the GI/M/s and M/D/s queues each of which has the same mean service time and traffic intensity as in the approximating GI/G/s queue. The weights in the approximations are expressed by the service-time c.d.f. and the first two moments of interarrival and service times. To examine the performance of our approximations, they are numerically compared with exact solutions and previous two-moment approximations for various cases. Extensive numerical comparisons indicate that the relative percentage errors of the approximations are of the order of 5% in moderate traffic and 1% in heavy traffic, except for extreme cases.     

Equivalence relations in the approximations for the M/G/s/s + r queue

This paper investigates some equivalence relations among previously established approximations for the steady-state distribution in an M/G/s queue with finite waiting spaces. The focus is on four approximations developed by Hokstad [1], Tijms and van Hoorn [2], Miyazawa [3] and Kimura [4]. These approximations have been obtained by completely different approaches and they have different expressions. Equivalence theorems show conditions under which some of the approximations coincide.     

Approximations for multi-server queues: System interpolations

This paper provides a unifying method of generating and/or evaluating approximations for the principal congestion measures in aGI/G/s queueing system. The main focus is on the mean waiting time, but approximations are also developed for the queue-length distribution, the waiting-time distribution and the delay probability for the Poisson arrival case. The approximations have closed forms that combine analytical solutions of simpler systems, and hence they are referred to as system-interpolation approximations or, simply, system interpolations. The method in this paper is consistent with and generalizes system interpolations previously presented for the mean waiting time in theGI/G/s queue.     

A graphical investigation of error bounds for moment-based queueing approximations

Many approximations of queueing performance measures are based on moment matching. Empirical and theoretical results show that although approximations based on two moments are often accurate, two-moment approximations can be arbitrarily bad and sometimes three-moment approximations are far better. In this paper, we investigate graphically error bounds for two- and three-moment approximations of three performance measures forGI/M/ · type models. Our graphical analysis provides insight into the adequacy of two- and three-moment approximations as a function of standardized moments of the interarrival-time distribution. We also discuss how the behavior of these approximations varies with other model parameters and with the performance measure being approximated.     

On surface radiation conditions for high-frequency wave scattering

A new approximation of the logarithmic derivative of the Hankel function is derived and applied to high-frequency wave scattering. We re-derive the on surface radiation condition (OSRC) approximations that are well suited for a Dirichlet boundary in acoustics. These correspond to the Engquist–Majda absorbing boundary conditions. Inverse OSRC approximations are derived and they are used for Neumann boundary conditions. We obtain an implicit OSRC condition, where we need to solve a tridiagonal system. The OSRC approximations are well suited for moderate wave numbers. The approximation of the logarithmic derivative is also used for deriving a generalized physical optics approximation, both for Dirichlet and Neumann boundary conditions. We have obtained similar approximations in electromagnetics, for a perfect electric conductor. Numerical computations are done for different objects in 2D and 3D and for different wave numbers. The improvement over the standard physical optics is verified.     

Approximating Rough Stochastic PDEs

We study approximations to a class of vector‐valued equations of Burgers type driven by a multiplicative space‐time white noise. A solution theory for this class of equations has been developed recently in Probability Theory Related Fields by Hairer and Weber. The key idea was to use the theory of controlled rough paths to give definitions of weak/mild solutions and to set up a Picard iteration argument. In this article the limiting behavior of a rather large class of (spatial) approximations to these equations is studied. These approximations are shown to converge and convergence rates are given, but the limit may depend on the particular choice of approximation. This effect is a spatial analogue to the Itô‐Stratonovich correction in the theory of stochastic ordinary differential equations, where it is well known that different approximation schemes may converge to different solutions.© 2014 Wiley Periodicals, Inc.     

On the real accuracy of approximation in the central limit theorem

We consider various approximations in the central limit theorem for distributions of sums of independent random variables. We study how many summands in the normalized sums guarantee the precision 10⁻³ for these approximations. It turns out that for the same distribution but different approximations this number varies from hundreds of thousands to a few tens.     

Simple Approximations for the GI/G/c Queue-II: The Moments,the Inverse Distribution Function and the Loss Function of the Number in the System and of the Queue Delay

Based on results obtained in part I of this paper, approximations for the first four moments of the number in the system are developed and thence used to approximate the inverse distribution function (IDF) and the loss functions (LF), employing Shore's general approximations. Existing approximations for the first two moments of queueing time in a GI/G/l queue serve to approximate the IDF and the LF of queueing time in the corresponding GI/G/c queue. The accuracy attained is generally satisfactory, while a remarkable algebraic simplicity is preserved. A numerical example demonstrates the applicability of some of the new approximations to solve optimization problems.     

On the implementation of boundary conditions for the method of lines

The method of lines for difference approximations of hyperbolic first order systems of partial differential equations is analyzed. The approximations are based on strictly semibounded difference operators including high order ones. The formulation of the ODE-system requires that the implementation of the boundary conditions is done carefully. We shall illustrate how different ways of implementation give rise to different stability properties. In particular, we derive a way of implementation that leads to an approximation that is strongly stable. It has been an open problem, whether for semidiscrete approximations with this strong stability property, the timestep for the ODE-solver is governed by the Cauchy problem. We present a counterexample showing that it is not. The analysis presented in this paper also serves as an illustration of the significant difference between different stability concepts.     

On comparison of approximate solutions in vector optimization problems

The problem of comparison of approximations (approximate solutions to a vector optimization problem) obtained using different numerical methods is considered. In the absence of a priori information about the set of weakly efficient vectors, a scalar function is introduced that enables pair-wise comparison of approximations and establishes a binary preference relation according to which the approximations close (in the sense of the Hausdorff distance) to the set containing all possible efficient vectors are preferable to other approximations.     

Efficient numerical methods for radiation in gas turbines

Numerical methods for radiative heat transfer equations coupled to a temperature equation are considered. Efficient solution methods and approximate equations for this system are investigated and a comparative numerical study of the different approximations is given. The approximate equations considered in this paper include moment methods and diffusive approximations. Fast iterative solvers for the problem like multilevel methods with suitable preconditioning are considered in detail. Numerical experiments and comparisons in different space dimensions and for various physical situations are presented.     

Stochastic and deterministic simulations of a delayed genetic oscillation model: Investigating the validity of reductions

Quasi-stationary approximations are commonly used in order to simplify and reduce the number of equations of genetic circuit models. Protein/protein and protein/DNA binding reactions are considered to occur on much shorter time scale than protein production and degradation processes and often tacitly assumed at a quasi-equilibrium. Taking a biologically inspired, typical, small, abstract, negative feedback, genetic circuit model as study case, we investigate in this paper how different quasi-stationary approximations change the system behaviour both in deterministic and stochastic frameworks. We investigate the consistence between the deterministic and stochastic behaviours of our time-delayed negative feedback genetic circuit model with different implementations of quasi-stationary approximations. Quantitative and qualitative differences are observed among the various reduction schemes and with the underlying microscopic model, for biologically reasonable ranges and combinations of the microscopic model kinetic rates. The different reductions do not behave in the same way: correlations and amplitudes of the stochastic oscillations are not equally captured and the population behaviour is not always in consistence with the deterministic curves.     

A numerically efficient method for the MAP/D/1/K queue via rational approximations

The Markovian Arrival Process (MAP), which contains the Markov Modulated Poisson Process (MMPP) and the Phase-Type (PH) renewal processes as special cases, is a convenient traffic model for use in the performance analysis of Asynchronous Transfer Mode (ATM) networks. In ATM networks, packets are of fixed length and the buffering memory in switching nodes is limited to a finite numberK of cells. These motivate us to study the MAP/D/1/K queue. We present an algorithm to compute the stationary virtual waiting time distribution for the MAP/D/1/K queue via rational approximations for the deterministic service time distribution in transform domain. These approximations include the well-known Erlang distributions and the Padé approximations that we propose. Using these approximations, the solution for the queueing system is shown to reduce to the solution of a linear differential equation with suitable boundary conditions. The proposed algorithm has a computational complexity independent of the queue storage capacityK. We show through numerical examples that, the idea of using Padé approximations for the MAP/D/1/K queue can yield very high accuracy with tractable computational load even in the case of large queue capacities.This work was done when the author was with the Bilkent University, Ankara, Turkey and the research was supported by TÜBITAK under Grant No. EEEAG-93.     

Extended and unscented filtering algorithms using one-step randomly delayed observations

In this paper, the least squares filtering problem is investigated for a class of nonlinear discrete-time stochastic systems using observations with stochastic delays contaminated by additive white noise. The delay is considered to be random and modelled by a binary white noise with values of zero or one; these values indicate that the measurement arrives on time or that it is delayed by one sampling time. Using two different approximations of the first and second-order statistics of a nonlinear transformation of a random vector, we propose two filtering algorithms; the first is based on linear approximations of the system equations and the second on approximations using the scaled unscented transformation. These algorithms generalize the extended and unscented Kalman filters to the case in which the arrival of measurements can be one-step delayed and, hence, the measurement available to estimate the state may not be up-to-date. The accuracy of the different approximations is also analyzed and the performance of the proposed algorithms is compared in a numerical simulation example.     

Linear Programming Approximations for Markov Control Processes in Metric Spaces

We develop a general framework to analyze the convergence of linear-programming approximations for Markov control processes in metric spaces. The approximations are based on aggregation and relaxation of constraints, as well as inner approximations of the decision variables. In particular, conditions are given under which the control problems optimal value can be approximated by a sequence of finite-dimensional linear programs.     

UPGMA clustering revisited: A weight-driven approach to transitive approximation

A new algorithm is proposed for generating min-transitive approximations of a given similarity matrix (i.e. a symmetric matrix with elements in the unit interval and diagonal elements equal to one). Different approximations are generated depending on the choice of an aggregation operator that plays a central role in the algorithm. If the maximum operator is chosen, then the approximation coincides with the min-transitive closure of the given similarity matrix. In case of the arithmetic mean, a transitive approximation is generated which is, on the average, as close to the given similarity matrix as the approximation generated by the UPGMA hierarchical clustering algorithm. The new algorithm also allows to generate approximations in a purely ordinal setting. As this new approach is weight-driven, the partition tree associated to the corresponding min-transitive approximation can be built layer by layer. Numerical tests carried out on synthetic data are used for comparing different approximations generated by the new algorithm with certain approximations obtained by classical methods.     

Incremental unknowns for solving partial differential equations

Summary Incremental unknowns have been proposed in [T] as a method to approximate fractal attractors by using finite difference approximations of evolution equations. In the case of linear elliptic problems, the utilization of incremental unknown methods provides a new way for solving such problems using several levels of discretization; the method is similar but different from the classical multigrid method.In this article we describe the application of incremental unknowns for solving Laplace equations in dimensions one and two. We provide theoretical results concerning two-level approximations and we report on numerical tests done with multi-level approximations.     

Variance reduction in sample approximations of stochastic programs

This paper studies the use of randomized Quasi-Monte Carlo methods (RQMC) in sample approximations of stochastic programs. In numerical integration, RQMC methods often substantially reduce the variance of sample approximations compared to Monte Carlo (MC). It seems thus natural to use RQMC methods in sample approximations of stochastic programs. It is shown, that RQMC methods produce epi-convergent approximations of the original problem. RQMC and MC methods are compared numerically in five different portfolio management models. In the tests, RQMC methods outperform MC sampling substantially reducing the sample variance and bias of optimal values in all the considered problems.     

Finite approximate approach to the study of the complexity of recursive predicates

An extension of Blum's complexity axiomatics, which enables us to investigate the complexity of bounded approximations of recursive predicates under very general assumptions on the type of the computational process, is considered. The existence of minimal bounded approximations of recursive predicates and the possibility of obtaining unimprovable bounds on the complexity of minimal bounded approximations are proved. Definitive bounds on the complexity of bounded approximations are obtained under fixed concepts of the computational process (for finite automata and for Turing machines). A lower bound is obtained for the complexity of the minimal approximations for the predicate occurrence.Main results announced on Oct. 4, 1973.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 49, pp. 131–158, 1975.