Top PDF The Monte Carlo method to find eigenvalues and eigenvectors

The Monte Carlo method to find eigenvalues and eigenvectors

The Monte Carlo method to find eigenvalues and eigenvectors

where C is a ( k − j ) ( × k − j ) matrix with the eigenvalues equal to the other k j eigenvalues of B. We apply the above algorithm to find an eigenvalue of C and the corresponding eigenvector if we consider for the normal vectors the last k j components in the new coordinates system (after the rotation). We do the same with the uniform vectors, but for null vectors we set the first component to 1, and the other vectors are normalized. After the computation of an eigenvector of C we set the first j components to 0 and we make the inverse rotation to obtain a new eigenvector of B.
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I. Eigenvectors and eigenvalues

I. Eigenvectors and eigenvalues

In theory, the theorem reduces the computation of eigenvalues to computing the roots of the characteristic polynomial and the computation of eigenvectors to finding the solutions to the homogeneous linear systems of equations (A − λI)x = 0 where λ runs through all the eigenvalues of A. For 2 × 2 matrices with integer entries, it is easy to compute the roots of the characteristic polynomial and the solutions to the associated systems of homogeneous linear equations, and even in the 3 × 3 case one can carry out the computations without too much trouble if the characteristic polynomial is well behaved (in particular, if one of its roots is an integer). However, in more complicated situations one needs better techniques. For the most part these are outside the scope of this course (and are treated in courses on numerical methods), but later on we shall give a reasonably efficient method for computing χ A (t). In this course most of the emphasis will be on
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A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

So far, the research of node’s localization algorithm in wireless sensor networks has been widely carried out. The main purpose of sensor localization is to deter- mine the location of sensors in WSNs via noisy mea- surements, and most of the methods for localization can be classified into geometrical techniques, multidi- mensional scaling, stochastic proximity embedding, convex and nonconvex optimization, and hybrid. In range-based measurement localization, the major task is to find the accurate position in non-line-of-sight (NLOS) paths. These range-based measurements may include time-of-arrival (TOA) [6], time-difference-of- arrival (TDOA) [7], angle-of-arrival (AOA) [8], and received signal strength (RSS). After evaluating the distance between the nodes, the position of the blind node can be obtained based on three edge-measuring or maximum likelihood methods [9].
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Monte Carlo methods

Monte Carlo methods

the other hand, if q proposes too big steps, then it is likely that they will be rejected, especially if the current state of the chain has a high value of π. This will lead to the chain being blocked for several iterations, which is an even worse case of autocorrelation, see Figure 6(b) for a typical traceplot. There is thus a compromise to find between small steps and large acceptance rate, and large steps and small acceptance rate. Theory suggests that when the target and the proposals are Gaussian, the acceptance rate to reach minimum variance of I N is approximately 0.5 when d ≤ 2 and 0.25 otherwise [19, 20].
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Applications of the Fixed-node Quantum Monte Carlo Method.

Applications of the Fixed-node Quantum Monte Carlo Method.

The Full Configuration Interaction (Full CI) method might build an exact wave function as a linear combination of the HF determinant and all excited determinants (i.e., the full spectrum of the particle-hole expansion) in a complete basis set (CBS) limit. However, there are a few drawbacks in the application of Full CI. First of all, unfortunately, the CBS limit is currently not possible; truncated basis sets are employed. Second, the number of excited determinants grows exponentially with system size and the number of basis functions. Therefore, the determinant space (or configuration space) also needs to be truncated, resulting in the truncated CI. Third, in either the Full CI or the truncated CI, the hamiltonian matrix is diagonalized to find the optimum linear combination of the determinants [79]. This full matrix diagonalization is a significantly time-consuming step of the method and can be another obstacle to considering a larger space of determinants. Finally, following a CI calculation, a space with a large number of determinants is generated. The construction of a trial wave function in QMC by using this s determinant expansion is done usually by selecting the determinants according to the absolute values of their (normalized) coefficients, not according to the energy drop that they cause.
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Inference for Eigenvalues and Eigenvectors in Exponential Families of Random Symmetric Matrices.

Inference for Eigenvalues and Eigenvectors in Exponential Families of Random Symmetric Matrices.

There are several decomposition methods applied to investigate the structure of covariance matrix. We can find several works in the literature that mention decomposition methods used in the estimation of a covari- ance matrix, such as Variance-correlation Decomposition (Barnard et al., 2000), Spectral Decomposition (Boik, 2002), and Cholesky Decomposition (Pourahmadi, 1999; Smith and Kohn, 2002; Chen and Dunson, 2003). Among these decompositions, Cholesky Decomposition is also commonly used to examine the struc- ture of covariance matrices, especially in Time Series Analysis and Longitudinal Data Analysis. Cholesky Decomposition also provides a statistically meaningful reparameterization of a covariance matrix (Pourah- madi, 1999; Pourahmadi, 2007). In DTI, however, the eigenvalues and eigenvectors of a covariance matrix also have their own anatomical meanings. Since our work is motivated by DTI, we use a spectral decompo- sition.
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The quantum Monte Carlo method : application to problems in statistical physics

The quantum Monte Carlo method : application to problems in statistical physics

To examine the boundary condition problems discussed above we have performed importance sampled calculation with a system of 108 particles. A time step of A t = 0.05 x 10“ *'5S W as used and the initial ensemble contained 100 systems distributed according to the variational wave function.. In Figure 4.4 we show the effects of extrapolating the radial distribution function obtained from this run by using the variational distribution. Again the results are compared with GFMC values. Good agreement between the diffusion Monte Carlo and GFMC results is generally observed. The two simulations were performed at slightly different densities, p p^C = 0 . 4 and p GFMC = 0*^01» so the small variations in the results are probably associated with this density difference. Agreement between the predicated eigenvalues (including only long range corrections) is also good, eDMC = “6.78 ± 0.06 and E q ^^ q = -6.743 ± 0.033 K/molecule. Whitlock et a l . (1979) have obtained a perturbation estimate of the three body correction, and at this density they give <V^ b > = 0.206 ± 0.002 K/molecule or about 3 l of the two body values given above. When the three body correction is made both the quantum Monte Carlo calculations give ground state energies which are approximately 0.5 K/molecule higher than the experimental value EeXp = -7.00 K/molecule (Roach, Ketterson and Woo (1970)). This discrepency is a result of the inadequacy of the Lennard-Jones potential (Whitlock et al. (1981) ).
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Transverse complex magnetic susceptibility of single domain ferromagnetic particles with uniaxial anisotropy subjected to a longitudinal uniform magnetic field

Transverse complex magnetic susceptibility of single domain ferromagnetic particles with uniaxial anisotropy subjected to a longitudinal uniform magnetic field

tions of the magnetization vector M in these coordinates. The Fokker-Planck equation in the case of axial symmetry can be solved by the method of separation of the variables. The separation procedure gives rise to an equation of the Sturm-Liouville type. An alternative approach to the prob- lem which is not confined to axial symmetry is to expand W as a series of spherical harmonics so yielding an infinite hi- erarchy of linear differential-recurrence equations for aver- aged spherical harmonics. The infinite hierarchy can be then solved by finding eigenvalues and eigenvectors of the system matrix or, much more efficiently, by a matrix continued- fraction method. 10 This hierarchy can also be obtained by directly averaging Gilbert’s equation without recourse to the Fokker-Planck equation. 11,12
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Mean exit times and the multilevel Monte Carlo method

Mean exit times and the multilevel Monte Carlo method

The work is organized as follows. Section 2 outlines the existing approximation results that are relevant to our analysis. In section 3, we state and prove a new strong convergence result. Section 4 derives the expected computational cost of standard Monte Carlo, defines a multilevel algorithm, and shows that it offers improved complexity. In section 5 we provide illustrative computational results, and concluding remarks appear in section 6.

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Evaluation of Investment Risks in CBA with Monte Carlo Method

Evaluation of Investment Risks in CBA with Monte Carlo Method

CBA method should form the basis of a good appraisal and, on the other hand, of some issues that deserve particular attention. The scope of the feasibility section is to summarise the main input that should, ideally, be included, together with demand prognosis, options for consideration etc., before entering the fi nancial and economic evaluation. In the area of Education and training infrastructure it is recommended to deal with critical factors investment and operating costs, the demographic dynamics in the catchment area and the success of the educational programmes (Guide to CBA, 2008).
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Application of Eigenvalues and Eigenvectors to Systems of First Order Differential Equations

Application of Eigenvalues and Eigenvectors to Systems of First Order Differential Equations

Computing eigenvalues boils down to solving a polynomial equation. But determining solutions to polynomial equations can be a formidable task. It was proven in the nineteenth century that it’s impossible to express the roots of a general polynomial of degree five or higher using radicals of the coefficients. This means that there does not exist a generalized version of the quadratic formula for polynomials of degree greater than four, and general polynomial equations cannot be solved by a finite number of arithmetic operations involving +, −, × and ÷. Unlike solving 𝐴𝑿 = 𝑏, the eigenvalue problem generally requires an infinite algorithm, so all practical eigenvalue computations are accomplished by iterative methods.[2]
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Analysing multi-level Monte Carlo for options with non-globally Lipschitz payoff

Analysing multi-level Monte Carlo for options with non-globally Lipschitz payoff

allow us to quantify an improvement in computational complexity when standard Monte Carlo is replaced by the multi-level version of [4]; they also explain the numerical results presented there. Promising topics for further work in this area include the analysis of (a) the weak error rate α in (1.7) for path-dependent options, (b) methods with higher strong order, and (c) quasi-Monte Carlo.

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Criticality Analysis and Quality Appraisal of Innoson Injection Mould System

Criticality Analysis and Quality Appraisal of Innoson Injection Mould System

The current dynamic and turbulent manufacturing environment has forced companies that compete globally to change their traditional methods of conducting business. Recent developments in manufacturing and business operations have led to the adoption of preventive maintenance techniques that is based on systems and process that support global competitiveness. This paper employed Monte Carlo Normal distribution model which interacts with a developed Obudulu model to assess reliability and maintenance of Injection Moulding machine. The failure rate, reliability and standard deviations are reliability parameter used. Monte Carlo Normal distribution was used to analyse the reliability and failure rate of the entire system. The result shows that failure rate increases with running time accruing from wear due to poor lubrication systems; while system reliability decreases with increase time (years). Obudulu model was used to evaluate the variance ration of failure between system components under preventive maintenance and those outside preventive maintenance. The result shows that at reliability +0.3 and failure rate - 0.02, preventive maintenance should be done. Interaction between the Monte Carlo normal distribution and obudulu model shows that the total system reliability is 0.489 when maintained which is 49% and 0.412 (41%) when not maintained. Also quality of production increased during Preventive maintenance while system downtime reduced greatly. These models were programmed using Monte Carlo Excel tool package software, showing the graphs of reliability and failure rates for each system.
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Uncertainty analysis for a wave energy converter: the Monte Carlo method

Uncertainty analysis for a wave energy converter: the Monte Carlo method

Here we present a graphical presentation of the Monte Carlo method simulation setup for propagating uncertainties, applied to a generic OWC wave energy converter experiment (fig. 4A), and the MCM simulation output of expanded uncertainty of quantities and DRE’s fig. 4E). Illustrated in fig. 4A are the nominal inputs for each quantity (measured or assumed), their associated standard uncertainties (evaluated or assumed), and the DREs required to calculate the final result of capture width ratio P w . Assumed nominal and uncertainty values have been
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Monte Carlo Simulations

Monte Carlo Simulations

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Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples

Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples

It is impractical to analyse the reliability of bearings based on lifetime data because of the difficulty of obtaining highly reliable bearing data. Reliability analysis based on performance degradation data solves the problem of reliability assessment without lifetime data, although, the existing performance degradation method cannot obtain the ideal results under small samples. In this paper, a method called DDBMC is proposed to solve this problem, and it is suitable for assessing the reliability of products with high reliability under small data sets.

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Ultra-fast Carrier Transport Simulation on the Grid. Quasi-Random Approach

Ultra-fast Carrier Transport Simulation on the Grid. Quasi-Random Approach

In this paper we present quasirandom approach for ultrafast carrier transport simulation. Due to cor- relation, the direct quasirandom variants of Monte Carlo methods do not give adequate results. Instead of them, we propose hybrid algorithms with pseudorandom numbers and scrambled quasirandom sequences. We use scrambled modified Halton [2], scrambled Sobol [1] and scrambled Niederreiter (the scrambling al- gorithm is similar to described in [1]). In this paper we present also the developed grid implementation scheme which uses not only the computational capacity of the grid but also the available grid services in a very efficient way. With this scheme we were able to obtain new estimates about important physical quanti- ties.
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A Comparison of Methods for Computing the Eigenvalues and Eigenvectors of a Matrix  EUR 4055

A Comparison of Methods for Computing the Eigenvalues and Eigenvectors of a Matrix EUR 4055

2 we give the behaviour of the "computation time" taken by the routines VCTR for determining all the eigenvectors of real matrices of order nn ^ 50 on IBM 7090 and by the routine AMAT fo[r]

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Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems

Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems

We are interested in computing the expectation of a functional of a PDE solution under a Bayesian posterior distribution. Using Bayes’ rule, we reduce the problem to estimating the ratio of two related prior expectations. For a model elliptic problem, we provide a full convergence and complexity analysis of the ratio estimator in the case where Monte Carlo, quasi-Monte Carlo or multilevel Monte Carlo methods are used as estimators for the two prior expectations. We show that the computational complexity of the ratio estimator to achieve a given accuracy is the same as the corresponding complexity of the individual estimators for the numerator and the denominator. We also include numerical simulations, in the context of the model elliptic problem, which demonstrate the effectiveness of the approach.
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Multilevel and quasi Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

Multilevel and quasi Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
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