Search

Purpose - This paper describes an approach for the automatic calculation of view factors between surfaces of arbitrary shape, when taking into account possible screening effects due to intermediate surfaces. Design/methodology/approach - The specifically developed numerical code is based on the utilization of boundary elements to fit the surfaces of an algorithm solving the shadow effect and on a Monte Carlo method for the numerical integrations. Findings - The code has been tested for a set of geometrical configurations. It was clearly shown that it obtains good results in terms of accuracy and computing time. Its accuracy increases when the mesh of radiative surfaces is finer. Research limitations/implications - The use of the code is limited to opaque surfaces separated by an isothermal semi-transparent medium which can be absorbent but not diffusing of the thermal radiation. Practical implications - The study of the radiative exchanges between opaque surfaces with shadow effects due to intermediate surfaces may have concrete practical applications by using this code. Indeed, the code has been used for an industrial application, in order to evaluate view factors inside an enclosure, in the framework of studies concerned with the thermal comfort inside cars. Originality/value - The originality of this paper lies in taking into account the surfaces of complex geometries by using a boundary elements approximation, the algorithm solving the shadow effect, based on the convexity of the quadrilateral in 2D or the polyhedron in 3D. [ABSTRACT FROM AUTHOR]

This paper describes a new Monte Carlo method based on a novel stochastic potential switching algorithm. This algorithm enables the equilibrium properties of a system with potential V to be computed using a Monte Carlo simulation for a system with a possibly less complex stochastically altered potential V. By proper choices of the stochastic switching and transition probabilities, it is shown that detailed balance can be strictly maintained with respect to the original potential V. The validity of the method is illustrated with a simple one-dimensional example. The method is then generalized to multidimensional systems with any additive potential, providing a framework for the design of more efficient algorithms to simulate complex systems. A near-critical Lennard-Jones fluid with more than 20 000 particles is used to illustrate the method. The new algorithm produced a much smaller dynamic scaling exponent compared to the Metropolis method and improved sampling efficiency by over an order of magnitude. [ABSTRACT FROM AUTHOR]

The ziggurat method is a fast random variable generation method introduced by Marsaglia and Tsang in a series of papers. We discuss how the ziggurat method can be implemented for low-discrepancy sequences, and present algorithms and numerical results when the method is used to generate samples from the normal and gamma distributions. [ABSTRACT FROM AUTHOR]

A recent paper by Matuttis and Ito questions the numerical accuracy of a widely-used fermion Monte Carlo algorithm. They also claim that the increase in the d-wave pairfield susceptibility χd(T) of a doped 4×4 Hubbard model at low temperature, previously found using this algorithm, is an artifact due to numerical errors. Here, we provide tests which show that this algorithm is numerically accurate and show that the simulation of χd for a 2×2 lattice agrees with exact diagonalization results. We also provide more complete data for χd on a 4×4 lattice that is consistent with our previous results. [ABSTRACT FROM AUTHOR]

This paper addresses the task of locating and identifying an unknown number of objects of different types in an image. Baddeley & Van Lieshout (1993) advocate marked point processes as object priors, whereas Grenander & Miller (1994) use deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types, using reversible jump Markov chain Monte Carlo methods for inference (Green, 1995). The naive application of these methods here leads to slow mixing and we adapt the model and algorithm in tandem in proposing three strategies to deal with this. The first two expand the model space by introducing an additional 'unknown' object type and the idea of a variable resolution template. The third strategy, utilising the first two, augments the algorithm with classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects. [ABSTRACT FROM PUBLISHER]

This paper focuses on Benders decomposition techniques and Monte Carlo sampling (importance sampling) for solving two-stage stochastic linear programs with recourse, a method first introduced by Dantzig and Glynn [7]. The algorithm is discussed and further developed. The paper gives a complete presentation of the method as it is currently implemented. Numerical results from test problems of different areas are presented. Using small test problems, we compare the solutions obtained by the algorithm with universe solutions. We present the solutions of large-scale problems with numerous stochastic parameters, which in the deterministic formulation would have billions of constraints. The problems concern expansion planning of electric utilities with uncertainty in the availabilities of generators and transmission lines and portfolio management with uncertainty in the future returns. [ABSTRACT FROM AUTHOR]

This paper illustrates how problems based on the Probabilistic Potential Method can be solved using a parallelized form of the Exodus Monte Carlo technique. The Exodus technique which does not use random number generation is shown to be more efficient for moderately large problems than the conventional fixed random walk method when programmed in both sequential and concurrent forms. The technique is illustrated for an electrostatic field problem by using as an example internal potential evaluation for a rectangular coaxial line. The problem geometry is torn into subsections which are then analysed independently using concurrent programming methods. The resulting algorithm is compared with the same problem executed using a conventional sequential algorithm. Computation is achieved by use of an array of transputers. The design of the concurrent algorithm is described in detail and typical results are compared with the fixed random walk Monte Carlo method and also with the finite difference relaxation method. Parallel Fortran is used throughout as the programming language. [ABSTRACT FROM AUTHOR]

In this paper a new algorithm is presented that improves the efficiency of Wang and Landau algorithm or density of states (DOS) Monte Carlo simulations by employing rejected states. The algorithm is shown to have a performance superior to that of the original Wang-Landau [F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001)] algorithm and the more recent configurational temperature DOS algorithm. The performance of the method is illustrated in the context of results for the Lennard-Jones fluid. [ABSTRACT FROM AUTHOR]

This paper formulates a hybrid Monte Carlo implementation of the Fourier path integral (FPI-HMC) approach with partial averaging. Such a hybrid Monte Carlo approach allows one to generate collective moves through configuration space using molecular dynamics while retaining the computational advantages associated with the Fourier path integral Monte Carlo method. In comparison with the earlier Metropolis Monte Carlo implementations of the FPI algorithm, the present HMC method is shown to be significantly more efficient for quantum Lennard-Jones solids and suggests that such algorithms may prove useful for efficient simulations of a range of atomic and molecular systems. [ABSTRACT FROM AUTHOR]

In this paper we propose a model to evaluate the performance of a Constant Proportion Debt Obligation (CPDO) and assess its rating. We model credit spread evolution in a HJM framework and default events for CPDO are generated by using a reduced form approach. Implementing a numerical algorithm that simulates the strategy of a CPDO, we obtain a rating for a CPDO by using Monte Carlo simulations. We find a rating inferior to the one assigned by rating agencies. Using our model for credit spread dynamics, the revealed default probability for CPDO could have been predicted. [ABSTRACT FROM AUTHOR]

We propose a new robust and accurate synchronization procedure using a training sequence composed of chirp signals. We use a new integer frequency estimation algorithm and propose a new combination of a known fractional frequency offset estimation algorithm and timing synchronization algorithm. The training sequence is composed of one up and two down chirp symbols, also known as Newman phases. The integer frequency offset estimation algorithm uses the effect of timing and frequency offsets on the matched filter outputs of the chirp signals. Autocorrelation and reversed autocorrelation are used to acquire the timing instant and the fractional frequency offset. We present the complete timing and frequency synchronization procedure and study the output signals of the autocorrelation and reversed autocorrelation algorithms. Finally, we check the performance of the synchronization procedure via Monte Carlo simulation in several multipath channels. Our algorithms are accurate and more robust compared to previously published state-of-the art algorithms. [ABSTRACT FROM AUTHOR]

In a regression kink (RK) design with a finite sample, a confounding smooth nonlinear relationship between an assignment variable and an outcome variable around a threshold can be spuriously picked up as a kink and results in a biased estimate. In order to investigate how well RK designs handle such confounding nonlinearity, I firstly implement Monte Carlo (MC) simulations and then study the effect of fiscal equalization grants on local expenditure in Japan using an RK design. Results in both the MC simulations and the empirical application suggest that RK estimation without covariates can be easily biased, and this problem can be mitigated by adding observed covariates to the regressors. On the other hand, a smaller bandwidth or a higher-order polynomial, even a quadratic polynomial, tends to result in imprecise estimates although they may be able to reduce estimation bias. In sum, RK estimation with confounding nonlinearity often suffers from bias or imprecision, and estimates are credible only when relevant covariates are controlled for. I also examine how placebo RK estimation can effectively address these issues. [ABSTRACT FROM AUTHOR]

The Linear Programming Problem is by far the most widely used optimization model. Its impact on economic and government modeling is immense. The Simplex Method for solving the Linear Programming (LP) Problem, due to George Dantzig, has been an extremely efficient computational tool for almost four decades. The method has been the subject of intense investigations for many years, but some major aspects of its behavior are not fully understood yet. The purpose of this paper is to survey the body of knowledge on the efficiency of the Simplex Method, from both practical and theoretical points of view. Adopting the number of iterations (pivot steps) as the yardstick for efficiency, we survey four aspects of the issue: 1. Reports on practical experience of the method's performance on real-life LP problems. 2. Results on controlled (Monte-Carlo) experiments solving LP problems which were randomly generated according to some predetermined distributions. 3. Complexity results, including theoretical analyses on both upper and lower bounds for the performance of the Simplex as well as non-Simplex algorithms for LP. 4. Results of recent theoretical studies using probabilistic analysis to derive bounds on the average behavior of the Simplex Method. We discuss the consequences and limitations of the various studies. Special emphasis is given to open questions. [ABSTRACT FROM AUTHOR]

In large-scale networks, the structure of the underlying network changes frequently, and thus the power iteration method for Personalized PageRank computation cannot deal with this kind of dynamic network efficiently In this paper, we design a Monte Carlo-based incremental method for Personalized PageRank computation. In a dynamic network, first, we do a random walk starting from each node and save the performed walks into a fingerprint database; second, we update the fingerprint database in a fixed time interval with our proposed update algorithm; finally, when a query is issued by a user, we estimate the Personalized PageRank vector by our proposed approximation algorithm. Experiments on real-world networks show that our method can handle multichanges of the underlying network at a time and is more efficient than related work, so it can be used in real incremental Personalized PageRank-based applications. [ABSTRACT FROM AUTHOR]

Objectives: Given time-series data from an unknown target system, one often wants to build a model for the system behind the data and make predictions. If the target system can be assumed to be linear, there are means of modeling and predicting the target system in question. If, however, one cannot assume the system is linear, various linear theories have natural limitations in terms of modeling and predictive capabilities. This paper attempts to construct a model from time-series data and make an online prediction when the linear assumption is not valid.Methods: The problem is formulated within a Bayesian framework implemented by the Sequential Monte Carlo method. Online Bayesian learning/prediction requires computation of a posterior distribution in a sequential manner as each datum arrives. The Sequential Monte Carlo method computes the importance weight in order to draw samples from the posterior distribution. The scheme is tested against time-series data from a noisy Rossler system.Results: The test time-series data is the x-coordinate of the trajectory generated by a noisy Roessler system. Attempts are made with regard to online reconstruction of the attractor and online prediction of the time-series data.Conclusions: The proposed algorithm appears to be functional. The algorithm should be tested against real world data. [ABSTRACT FROM AUTHOR]

The article focuses on a Monte Carlo method, known as sequential importance sampling (SIS). SIS has proved to be very effective against a wide variety of problems. The article examines counting via importance sampling and sequential importance sampling. It discusses the history of the algorithm that was specifically designed for counting.

In this paper we derive bounds on the conductance and hence on the spectral gap of a Metropolis algorithm with a monotone, log-concave target density on an interval of R. We show that the minimal conductance set has measure ½ and we use this characterization to bound the conductance in terms of the conductance of the algorithm restricted to a smaller domain. Whereas previous work on conductance has resulted in good bounds for Markov chains on bounded domains, this is the first conductance bound applicable to unbounded domains. We then show how this result can be combined with the state-decomposition theorem of Madras and Randall (2002) to bound the spectral gap of Metropolis algorithms with target distributions with monotone, log-concave tails on R. [ABSTRACT FROM AUTHOR]

We propose an efficient algorithm to perform Monte Carlo simulations of dense systems using multiple particle moves. The method is intended to be used in the atomistic simulation of complex systems, where the computing requirements for a single simulation run make advisable the use of parallel computing. The algorithm is based on the use of steps in which all the particle positions of the system are perturbed simultaneously. A division of the system in clusters of particles is performed, using a bonding criterion which makes feasible that the acceptance or rejection of the new particle coordinates can be carried out independently for each cluster. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of taking random samples over the surface of a 3D mesh describing and evaluating efficient algorithms for generating different distributions. We discuss first the problem of generating a Monte Carlo distribution in an efficient and practical way avoiding common pitfalls. Then, we propose Constrained Poisson-disk sampling, a new Poisson-disk sampling scheme for polygonal meshes which can be easily tweaked in order to generate customized set of points such as importance sampling or distributions with generic geometric constraints. In particular, two algorithms based on this approach are presented. An in-depth analysis of the frequency characterization and performance of the proposed algorithms are also presented and discussed. [ABSTRACT FROM PUBLISHER]

A parallel algorithm based on the pseudo excitation method (PEM) is applied to investigate the non-stationary random response of a vertically coupled vehicle–slab track system subjected to random excitation induced by track irregularity. The vehicle is simplified as a multibody system with 10 degrees of freedom and the slab track is represented by a three layer Bernoulli–Euler beam model which includes the rail, slab and roadbed. Linear wheel–rail contact provides interaction between the vehicle and slab track models. The track irregularity is assumed to be a Gaussian random process with zero mean value and the non-stationary random response of the coupled vehicle–slab track system is solved using a combination of PEM and step-by-step integration. A parallel algorithm based on PEM is used to accelerate the computation and the effectiveness of the proposed method is verified by the Monte Carlo method. The basic random performance of the coupled vehicle–slab track system is analysed and the effects of the vehicle speed and slab track parameters on the random responses of the vehicle and slab track are investigated. The results indicate that the vehicle speed has a significant effect on the random response of the coupled vehicle–slab track system and that the rail pad parameters are the most important of the slab track parameters discussed. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is an optimisation-based algorithm which usually requires a numerical method to calculate the solution of the problem. Inherently, numerical methods for optimisation problems are implemented on a finite-precision hardware platform and are subject to the appearance of numerical instabilities of catastrophic cancellation and ill-conditioned matrices. These anomalies are difficult to detect and overcome, and for safety-critical applications, it is essential to have a mechanism that can at least issue a warning when an arithmetic instability occurs. Towards this direction, Monte Carlo arithmetic (MCA) for the floating-point (FP) number system has been used for both detection and mitigation of catastrophic cancellation and ill-conditioned matrices. An alternative to FP is the Logarithmic Number System (LNS) that recently has been proposed for the real-time hardware implementation of embedded MPC. In this study the authors present the adaptation of MCA to LNS for detecting and mitigating catastrophic cancellation, forming the Monte Carlo Logarithmic Number System (MCLNS). An inherent drawback of MCA is the accuracy deterioration which is a direct consequence of the randomisation in the arithmetic operations. Additionally, multiple simulations of the system result in performance deterioration equal to the number of simulations. Using off-line simulations it is possible to determine the necessary hardware requirements to achieve desired accuracy under performance constraints. These trade-offs are studied and analysed for an MPC algorithm, and the hardware implementation cost of MCLNS is quantified by synthesis on a Xilinx Virtex-IV FPGA. [ABSTRACT FROM AUTHOR]

Using the solution of one-sided exit problem, a procedure to price Parisian barrier options in a jump-diffusion model with two-sided exponential jumps is developed. By extending the method developed in Chesney, Jeanblanc-Picqué and Yor (1997; Brownian excursions and Parisian barrier options, Advances in Applied Probability, 29(1), pp. 165–184) for the diffusion case to the more general set-up, we arrive at a numerical pricing algorithm that significantly outperforms Monte Carlo simulation for the prices of such products. [ABSTRACT FROM AUTHOR]

A new method of momentum measurement of charged particles through multiple Coulomb scattering (MCS) in the OPERA lead-emulsion target is presented. It is based on precise measurements of track angular deviations carried out thanks to the very high resolution of nuclear emulsions. The algorithm has been tested with Monte Carlo pions. The results are found to describe within the expected uncertainties the data obtained from test beams. We also present a comparison of muon momenta evaluated through MCS in the OPERA lead-emulsion target with those determined by the electronic detectors for neutrino-charged current interaction events. The two independent measurements agree within the experimental uncertainties, and the results validate the algorithm developed for the emulsion detector of OPERA. [ABSTRACT FROM AUTHOR]

We analyse the convergence properties of the Longstaff-Schwartz algorithm for approximately solving optimal stopping problems that arise in the pricing of American (Bermudan) financial options. Based on a new approximate dynamic programming principle error propagation inequality, we prove sample complexity error estimates for this algorithm for the case in which the corresponding approximation spaces may not necessarily possess any linear structure at all and may actually be any arbitrary sets of functions, each of which is uniformly bounded and possesses finite VC-dimension, but is not required to satisfy any further material conditions. In particular, we do not require that the approximation spaces be convex or closed, and we thus significantly generalize the results of Egloff, Clement et al., and others. Using our error estimation theorems, we also prove convergence, up to any desired probability, of the algorithm for approximating sets defined using L2 orthonormal bases, within a framework depending subexponentially on the number of time steps. In addition, we prove estimates on the overall convergence rate of the algorithm for approximation spaces defined by polynomials. [ABSTRACT FROM AUTHOR]

We propose a sequential Monte Carlo (SMC)-based motif discovery algorithm that can efficiently detect motifs in datasets containing a large number of sequences. The statistical distribution of the motifs is modeled by an underlying position weight matrix (PWM), and both the PWM and the positions of the motifs within the sequences are estimated by the SMC algorithm. The proposed SMC motif discovery technique can locate motifs under a number of scenarios, including the single-block model, two-block model with unknown gap length, motifs of unknown lengths, motifs with unknown abundance, and sequences with multiple unique motifs. The accuracy of the SMC motif discovery algorithm is shown to be superior to that of the existing methods based on MCMC or EM algorithms. Furthermore, it is shown that the proposed method can be used to improve the results of existing motif discovery algorithms by using their results as the priors for the SMC algorithm. [ABSTRACT FROM AUTHOR]

A new multiple-scattering Monte Carlo 3-D radiative transfer model named McSCIA (Monte Carlo for SCIAmachy) is presented. The backward technique is used to efficiently simulate narrow field of view instruments. The McSCIA algorithm has been formulated as a function of the Earth's radius, and can thus perform simulations for both plane-parallel and spherical atmospheres. The latter geometry is essential for the interpretation of limb satellite measurements, as performed by SCIAMACHY on board of ESA's Envisat. The model can simulate UV-vis-NIR radiation. First the ray-tracing algorithm is presented in detail, and then successfully validated against literature references, both in plane-parallel and in spherical geometry. A simple 1-D model is used to explain two different ways of treating absorption. One method uses the single scattering albedo while the other uses the equivalence theorem. The equivalence theorem is based on a separation of absorption and scattering. It is shown that both methods give, in a statistical way, identical results for a wide variety of scenarios. Both absorption methods are included in McSCIA, and it is shown that also for a 3-D case both formulations give identical results. McSCIA limb profiles for atmospheres with and without absorption compare well with the one of the state of the art Monte Carlo radiative transfer model MCC++. A simplification of the photon statistics may lead to very fast calculations of absorption features in the atmosphere. However, these simplifications potentially introduce biases in the results. McSCIA does not use simplifications and is therefore a relatively slow implementation of the equivalence theorem. [ABSTRACT FROM AUTHOR]

The Tile Calorimeter, which constitutes the central section of the ATLAS hadronic calorimeter, is a non-compensating sampling device made of iron and scintillating tiles. The construction phase of the calorimeter is nearly complete, and most of the effort now is directed toward the final assembly and commissioning in the underground experimental hail. The layout of the calorimeter and the tasks carried out during construction are described, first with a brief reminder of the requirements that drove the calorimeter design. During the last few years a comprehensive test-beam program has been followed in order to establish the calorimeter electromagnetic energy scale, to study its uniformity, and to compare real data to Monte Carlo simulation. The test-beam setup and first results from the data are described. During the test-beam period in 2004, lasting several months, data have been acquired with a complete slice of the central ATLAS calorimeter. The data collected in the test-beam are crucial in order to study algorithms for hadronic energy reconstruction using single particles. The generalization of these algorithms to reconstruct jet energies will be the starting point for numerous physics studies in which jets play a leading role. The results obtained in applying these algorithms to simulated di-jet events are given in the last section of the note. [ABSTRACT FROM AUTHOR]

The Hardy–Weinberg law is among the most important principles in the study of biological systems ( Crow, 1988 , Genetics 119, 473–476). Given its importance, many tests have been devised to determine whether a finite population follows Hardy–Weinberg proportions. Because asymptotic tests can fail, Guo and Thompson (1992, Biometrics 48, 361–372) developed an exact test; unfortunately, the Monte Carlo method they proposed to evaluate their test has a running time that grows linearly in the size of the population N. Here, we propose a new algorithm whose expected running time is linear in the size of the table produced, and completely independent of N. In practice, this new algorithm can be considerably faster than the original method. [ABSTRACT FROM AUTHOR]

Purpose: To develop a pharmacokinetic limited sampling model (LSM) for temozolomide and its metabolite MTIC in infants and children.Methods: LSMs consisting of either two or four samples were determined using a modification of the D-optimality algorithm. This accounted for prior distribution of temozolomide and MTIC pharmacokinetic parameters based on full pharmacokinetic sampling from 38 patients with 120 pharmacokinetic studies (dosage range 145-200 mg/m(2) per day orally). Accuracy and bias of each LSM were determined relative to the full sampling method. We also assessed the predictive performance of the LSMs using Monte-Carlo simulations.Results: The four strategies generated from the D-optimality algorithm were as follows: LSM 1=0.25, 1.25, and 3 h; LSM 2=0.25, 1.25, and 6 h; LSM 3=0.25, 0.5, 1.25, and 3 h; LSM 4=0.25, 0.5, 1.25, and 6 h. LSM 2 demonstrated the best combination of low bias [0.1% (-8.9%, 11%) and 11% (4.3%, 15%)] and high accuracy [-1.0% (-12%, 24%) and 14% (7.9%, 37%)] for temozolomide clearance and MTIC AUC, respectively. Furthermore, adding a fourth sample (e.g., LSM 4) did not substantially decrease the bias or increase the accuracy for temozolomide clearance or MTIC AUC. Results from Monte-Carlo simulations also revealed that LSM 2 had the best combination of lowest bias (0.1+/-6.1% and -0.8+/-6.5%), and the highest accuracy (4.5+/-4.1% and 5.0+/-4.3%) for temozolomide clearance and MTIC apparent clearance, respectively.Conclusions: Using data derived from our population analysis, the sampling times for a limited sample pharmacokinetic model for temozolomide and MTIC in children are prior to the temozolomide dose, and 15 min, 1.25 h and 6 h after the dose. [ABSTRACT FROM AUTHOR]

Hit-and-Run algorithms are Monte Carlo procedures for generating points that are asymptotically distributed according to general absolutely continuous target distributions G over open bounded regions S. Applications include nonredundant constraint identification, global optimization, and Monte Carlo integration. These algorithms are reversible random walks that commonly incorporate uniformly distributed step directions. We investigate nonuniform direction choice and show that, under regularity conditions on the region S and target distribution G, there exists a unique direction choice distribution, characterized by necessary and sufficient conditions depending on S and G, which optimizes the Doob bound on rate of convergence. We include computational results demonstrating greatly accelerated convergence for this optimizing direction choice as well as for more easily implemented adaptive heuristic rules. [ABSTRACT FROM AUTHOR]