A Legendre spectral element method for eigenvalues in hydrodynamic stability

A Legendre polynomial-based spectral technique is developed to be applicable to solving eigenvalue problems which arise in linear and nonlinear stability questions in porous media, and other areas of Continuum Mechanics. The matrices produced in the corresponding generalised eigenvalue problem are sparse, reducing the computational and storage costs, where the superimposition of boundary conditions is not needed due to the structure of the method. Several eigenvalue problems are solved using both the Legendre polynomial-based and Chebyshev tau techniques. In each example, the Legendre polynomial-based spectral technique converges to the required accuracy utilising less polynomials than the Chebyshev tau method, and with much greater computational efficiency.     

An inexact spectral bundle method for convex quadratic semidefinite programming

We present an inexact spectral bundle method for solving convex quadratic semidefinite optimization problems. This method is a first-order method, hence requires much less computational cost in each iteration than second-order approaches such as interior-point methods. In each iteration of our method, we solve an eigenvalue minimization problem inexactly, and solve a small convex quadratic semidefinite program as a subproblem. We give a proof of the global convergence of this method using techniques from the analysis of the standard bundle method, and provide a global error bound under a Slater type condition for the problem in question. Numerical experiments with matrices of order up to 3000 are performed, and the computational results establish the effectiveness of this method.     

Properties of discrete Chebyshev collocation differential operators in curvilinear geometries

The properties of discrete systems resulting from spectral Chebyshev collocation discretizations are investigated with respect to the solution efficiency of corresponding solvers. Complex geometries are encountered by a mapping technique to connect computational and physical domains. Several representative transformation techniques are considered. The influences of the differential operators, the boundary conditions, the geometry, and the number of grid points are systematically studied. The convergence properties of the BiCGSTAB method when iteratively solving the discrete systems are investigated. Copyright © 2008 John Wiley & Sons, Ltd.     

Numerical solution of non‐stationary aero‐autoelasticity integro‐differential operator equations

The spectral method of G. N. Elnagar, which yields spectral convergence rate for the approximate solutions of Fredholm and Volterra–Hammerstein integral equations, is generalized in order to solve the larger class of integro‐differential functional operator equations with spectral accuracy. In order to obtain spectrally accurate solutions, the grids on which the above class of problems is to be solved must also be obtained by spectrally accurate techniques. The proposed method is based on the idea of relating, spectrally constructed, grid points to the structure of projection operators which will be used to approximate the control vector and the associated state vector. These projection operators are spectrally constructed using Chebyshev–Gauss–Lobatto grid points as the collocation points, and Lagrange polynomials as trial functions. Simulation studies demonstrate computational advantages relative to other methods in the literature. Copyright © 1999 John Wiley & Sons, Ltd.     

Numerical solution of calcium-mediated dendritic branch model

The standard algorithms for spatial discretizations of calcium-mediated dendritic branch models via finite difference methods are quite accurate, but they are also extremely slow. To improve computational efficiency we apply spatial discretization using a spectral collocation method. Simulations using the spectral collocation method are compared to the finite difference approach using a model for calcium-mediated restructuring with spine pruning. We find that the spectral collocation method is about fifteen times more efficient to achieve similar accuracy than the finite difference approach even though spectral collocation requires more steps.     

A numerical study for the KdV and the good Boussinesq equations using Fourier Chebyshev tau meshless method

The current paper presents a scheme, which combines Fourier spectral method and Chebyshev tau meshless method based on the highest derivative (CTMMHD) to solve the nonlinear KdV equation and the good Boussinesq equation. Fourier spectral method is used to approximate the spatial variable, and the problem is converted to a series of equations with Fourier coefficients as unknowns. Then, CTMMHD is applied blockwise in time direction. For the long time computing of solitons, we introduce the computational area moving technique. The numerical results show that the accuracy of Fourier-CTMMHD is good and the computational area moving technique makes the long-time numerical behavior well for the problems with solitons moving towards the same direction.     

A Method of Systematic Search for Optimal Multipliers in Congruential Random Number Generators

This paper presents a method of systematic search for optimal multipliers for congruential random number generators. The word-size of computers is a limiting factor for development of random numbers. The generators for computers up to 32 bit word-size are already investigated in detail by several authors. Some partial works are also carried out for moduli of 2⁴⁸ and higher sizes. Rapid advances in computer technology introduced recently 64 bit architecture in computers. There are considerable efforts to provide appropriate parameters for 64 and 128 bit moduli. Although combined generators are equivalent to huge modulus linear congruential generators, for computational efficiency, it is still advisable to choose the maximum moduli for the component generators. Due to enormous computational price of present algorithms, there is a great need for guidelines and rules for systematic search techniques. Here we propose a search method which provides ‘fertile’ areas of multipliers of perfect quality for spectral test in two dimensions. The method may be generalized to higher dimensions. Since figures of merit are extremely variable in dimensions higher than two, it is possible to find similar intervals if the modulus is very large. This revised version was published online in July 2006 with corrections to the Cover Date.     

A computational approach for the non-smooth solution of non-linear weakly singular Volterra integral equation with proportional delay

This paper develops a well-conditioned Jacobi spectral Galerkin method for the analysis of Volterra-Hammerstein integral equations with weakly singular kernels and proportional delay. A recursive formula reduces the computational load when approximating the solutions of badly conditioned and complex non-linear algebraic systems. Additionally, the convergence properties of the method are also investigated. The spectral accuracy is obtained regardless of the discontinuities in the derivatives solution. Three examples illustrate the performance of the new approach.

     

Simultaneous modelling of the Cholesky decomposition of several covariance matrices

A method for simultaneous modelling of the Cholesky decomposition of several covariance matrices is presented. We highlight the conceptual and computational advantages of the unconstrained parameterization of the Cholesky decomposition and compare the results with those obtained using the classical spectral (eigenvalue) and variance-correlation decompositions. All these methods amount to decomposing complicated covariance matrices into “dependence” and “variance” components, and then modelling them virtually separately using regression techniques. The entries of the “dependence” component of the Cholesky decomposition have the unique advantage of being unconstrained so that further reduction of the dimension of its parameter space is fairly simple. Normal theory maximum likelihood estimates for complete and incomplete data are presented using iterative methods such as the EM (Expectation-Maximization) algorithm and their improvements. These procedures are illustrated using a dataset from a growth hormone longitudinal clinical trial.     

On performance of SOR method for solving nonsymmetric linear systems

The main subject of the paper is the study of the performance of SOR algorithms for solving linear systems of the type arising from the difference approximation of nonself-adjoint two-dimensional elliptic partial differential equations. A special attention is paid to the development of efficient techniques for determining the optimum relaxation parameter providing the maximum rate of convergence. Four models of the behaviour of the spectral radius of the SOR matrix as a function of relaxation parameter are analyzed. Numerical experiments are performed for several problems with nonsymmetric coefficient matrices taken from the literature. A comparison of results of the line-SOR method with the results obtained from different GMRES algorithms shows that with the computational work comparable for both methods, the line-SOR method provides the solutions of considered problems with the second norm of the error vector a few orders lesser in the magnitude.     

On bounded solutions of transport equation solved with a spectral method

In this article, a spectral method accompanied by finite difference method has been proposed for solving a boundary value problem that accompanies a stationary transport equation. We also prove that the solution is bounded by a value that depends of the source function. The accuracy and computational efficiency of the proposed method are verified with the help of a numerical example. © 2011 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2012     

Uncertainty propagation in stochastic fractional order processes using spectral methods: A hybrid approach

Stochastic spectral methods are widely used in uncertainty propagation thanks to its ability to obtain highly accurate solution with less computational demand. A novel hybrid spectral method is proposed here that combines generalized polynomial chaos (gPC) and operational matrix approaches. The hybrid method takes advantage of gPC’s efficient handling of large parameter uncertainties and overcomes its limited applicability to systems with relatively highly correlated inputs. The hybrid method’s use of operational matrices allows analyses of systems with low input correlations without suffering its restriction to small parameter uncertainties. The hybrid method is aimed to propagate uncertainties in fractional order systems with random parameters and random inputs with low correlation lengths. It is validated through several examples with different stochastic uncertainties. Comparison with Monte Carlo and gPC demonstrates the superior computational efficiency of the proposed method.     

Legendre spectral method for the fractional Bratu problem

In this paper, the Legendre spectral collocation method (LSCM) is applied for the solution of the fractional Bratu's equation. It shows the high accuracy and low computational cost of the LSCM compared with some other numerical methods. The fractional Bratu differential equation is transformed into a nonlinear system of algebraic equations for the unknown Legendre coefficients and solved with some spectral collocation methods. Some illustrative examples are also given to show the validity and applicability of this method, and the obtained results are compared with the existing studies to highlight its high efficiency and neglectable error.     

Spectral graph wavelet optimized finite difference method for solution of Burger's equation with different boundary conditions

In this paper, spectral graph wavelet optimized finite difference method (SPGWOFD) has been proposed for solving Burger's equation with distinct boundary conditions. Central finite difference approach is utilized for the approximations of the differential operators and the grid on which the numerical solution is obtained is chosen with the help of spectral graph wavelet. Four test problems (with Dirichlet, Periodic, Robin and Neumann's boundary conditions) are considered and the convergence of the technique is checked. For assessing the efficiency of the developed technique, the computational time taken by the developed technique is compared to that of the finite difference method. It has been observed that developed technique is extremely efficient.     

Integration of topological measures for eliminating non-specific interactions in protein interaction networks

High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein–protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. Unfortunately, many computational methods are sensitive to noise in protein interaction networks; and therefore they are not able to make biologically accurate inferences.In this article, we propose a novel technique based on integration of topological measures for removing non-specific interactions in a large-scale protein–protein interaction network. After transforming a given protein interaction network using line graph transformation, we compute clustering coefficient and betweenness centrality measures for all the edges in the network. Motivated by the modular organization of specific protein interactions in a cell, we remove edges with low clustering coefficient and high betweenness centrality values. We also utilize confidence estimates that are provided by probabilistic interaction prediction techniques. We validate our proposed method by comparing the results of a molecular complex detection algorithm (MCODE) to a ground truth set of known Saccharomyces cerevisiae complexes in the MIPS complex catalogue database. Our results show that, by removing false-positive interactions in the S. cerevisiae network, we can significantly increase the biological accuracy of the complexes reported by MCODE.     

An improved spectral homotopy analysis method for MHD flow in a semi-porous channel

In this paper we report on a novel method for solving systems of highly nonlinear differential equations by blending two recent semi-numerical techniques; the spectral homotopy analysis method and the successive linearisation method. The hybrid method converges rapidly and is an enhancement of the utility of the original spectral homotopy analysis method (Motsa et al., Commun Nonlinear Sci Numer Simul 15:2293?C2302, 2010; Computer & Fluids 39:1219?C1225, 2010) and an improvement on other recent semi-analytical techniques. We illustrate the application of the method by solving a system of nonlinear differential equations that govern the problem of laminar viscous flow in a semi-porous channel subject to a transverse magnetic field. A comparison with the numerical solution confirms the validity and accuracy of the technique and shows that the method converges rapidly and gives very accurate results.     

Distributed optimal control of the viscous Burgers equation via a Legendre pseudo‐spectral approach

This paper presents a computational technique based on the pseudo‐spectral method for the solution of distributed optimal control problem for the viscous Burgers equation. By using pseudo‐spectral method, the problem is converted to a classical optimal control problem governed by a system of ordinary differential equations, which can be solved by well‐developed direct or indirect methods. For solving the resulting optimal control problem, we present an indirect method by deriving and numerically solving the first‐order optimality conditions. Numerical tests involving both unconstrained and constrained control problems are considered. Copyright © 2015 John Wiley & Sons, Ltd.     

Controllability method for acoustic scattering with spectral elements

We formulate the Helmholtz equation as an exact controllability problem for the time-dependent wave equation. The problem is then discretized in time domain with central finite difference scheme and in space domain with spectral elements. This approach leads to high accuracy in spatial discretization. Moreover, the spectral element method results in diagonal mass matrices, which makes the time integration of the wave equation highly efficient. After discretization, the exact controllability problem is reformulated as a least-squares problem, which is solved by the conjugate gradient method. We illustrate the method with some numerical experiments, which demonstrate the significant improvements in efficiency due to the higher order spectral elements. For a given accuracy, the controllability technique with spectral element method requires fewer computational operations than with conventional finite element method. In addition, by using higher order polynomial basis the influence of the pollution effect is reduced.     

Legendre-Laguerre coupled spectral element methods for second- and fourth-order equations on the half line

Some Legendre spectral element/Laguerre spectral coupled methods are proposed to numerically solve second- and fourth-order equations on the half line. The proposed methods are based on splitting the infinite domain into two parts, then using the Legendre spectral element method in the finite subdomain and Laguerre method in the infinite subdomain. C⁰ or C¹-continuity, according to the problem under consideration, is imposed to couple the two methods. Rigorous error analysis is carried out to establish the convergence of the method. More importantly, an efficient computational process is introduced to solve the discrete system. Several numerical examples are provided to confirm the theoretical results and the efficiency of the method.