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The main advantage of inverse iteration over the QR algorithm and Divide & Conquer for the symmetric tridiagonal eigenproblem is that subsets of eigenpairs can be computed at reduced cost. The MRRR algorithm (MRRR = multiple relatively robust representations) is a clever variant of inverse iteration without the need for reorthogonalization. STEGR, the current version of MRRR in LAPACK 3.0, does not allow for subset computations. The next release of STEGR is designed to compute a (sub-)set of k eigenpairs with O (kn) operations. Because of the special way in which eigenvectors are computed, MRRR subset computations are more complicated than when using inverse iteration. Unlike the latter, MRRR sometimes cannot ignore the unwanted part of the spectrum. We describe the problems with what we call 'false singletons'. These are eigenvalues that appear to be isolated with respect to the wanted eigenvalues but in fact belong to a tight cluster of unwanted eigenvalues. This paper analyses these complications and ways to deal with them. Published in 2006 by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study the convergence of the generalized accelerated overrelaxation (GAOR) iterative method. That is an extension of the classical convergence result of the generalized successive overrelaxation (GSOR) iterative method. We proposed some theorems, which they obtain better results than similar works. By some numerical examples we show the goodness of our results. [Copyright &y& Elsevier]

This paper considers constrained and unconstrained parametric global optimization problems in a real Hilbert space. We assume that the gradient of the cost functional is Lipschitz continuous but not smooth. A suitable choice of parameters implies the linear or superlinear (supergeometric) convergence of the iterative method. From the numerical experiments, we conclude that our algorithm is faster than other existing algorithms for continuous but nonsmooth problems, when applied to unconstrained global optimization problems. However, because we solve 2n + 1 subproblems for a large number n of independent variables, our algorithm is somewhat slower than other algorithms, when applied to constrained global optimization. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a globally convergent first-order training algorithm is proposed that uses sign-based information of the batch error measure in the framework of the nonlinear Jacobi process. This approach allows us to equip the recently proposed Jacobi–Rprop method with the global convergence property, i.e. convergence to a local minimizer from any initial starting point. We also propose a strategy that ensures the search direction of the globally convergent Jacobi–Rprop is a descent one. The behaviour of the algorithm is empirically investigated in eight benchmark problems. Simulation results verify that there are indeed improvements on the convergence success of the algorithm. [Copyright &y& Elsevier]

A new circuit analysis method, named SPICE-accurate iterative linear-centric analysis (SILCA), is proposed for the efficient and accurate time-domain simulation of deep submicron very large scale integrated (VLSI) circuits with strong parasitic couplings. SILCA consists of two key linear-centric techniques applied to time-domain nonlinear circuit simulation. For numerical integration, explicit-formula substitution and iterative-formula transformation are presented to convert implicit variable time-step integration to fixed leading coefficient (FLC) variable time-step integration. This paper characterizes both convergence and stability properties of the resulting FLC integration formulae. For nonlinear iteration, a successive variable chord (SVC) method is used as an alternative to the Newton-Raphson method. Further, the low-rank update technique is implemented for fast LU factorization. With these techniques, the number and cost of required LU factorizations are reduced dramatically. Experimental results on nonlinear circuits coupled with substrate and power/ground networks have demonstrated that SILCA achieves more than an order of magnitude speedup over SPICE3 in terms of both the cost of LU factarization and the overall CPU time. SILCA is suitable for efficient SPICE-like time-domain simulation of parasitic-coupled VLSI circuits, where the number of linear parasitic elements dominates the number of nonlinear devices. [ABSTRACT FROM AUTHOR]

Abstract: In this paper two algorithms for computing the principal square root of a centrosymmetric H-matrix with positive diagonal entries are proposed. It is showed that our algorithms ensure significant savings in computational costs, as compared to the case of an arbitrary H-matrix A with positive diagonal entries. [Copyright &y& Elsevier]

Abstract: The ill-conditioned subproblems appeared in constrained optimization can often impede the preformance of optimization algorithms. In this paper we introduce a class of efficient iterative semi-refinement technique to solve the ill-conditioned trust-region subproblems. The nonmonotone technique is also used to increase the efficiency of the algorithm. The numerical results show that the presented method is competitive. [Copyright &y& Elsevier]

Abstract: The goal of this work is to present new numerical approaches to obtain computationally efficient adaptive procedures to solve contact problems associated with Maxwell–Norton materials. We will describe in detail the numerical methods used to solve such problems and derive iterative algorithms. To validate the proposed algorithms we will present some test examples with known analytical solutions. Finally, we will compare the efficiency of the algorithms presented in this paper with others previously proposed by the authors. [Copyright &y& Elsevier]

Algebraic multigrid (AMG) is a very efficient iterative solver and preconditioner for large unstructured sparse linear systems. Traditional coarsening schemes for AMG can, however, lead to computational complexity growth as problem size increases, resulting in increased memory use and execution time, and diminished scalability. Two new parallel AMG coarsening schemes are proposed that are based solely on enforcing a maximum independent set property, resulting in sparser coarse grids. The new coarsening techniques remedy memory and execution time complexity growth for various large three-dimensional (3D) problems. If used within AMG as a preconditioner for Krylov subspace methods, the resulting iterative methods tend to converge fast. This paper discusses complexity issues that can arise in AMG, describes the new coarsening schemes, and examines the performance of the new preconditioners for various large 3D problems. [ABSTRACT FROM AUTHOR]

In this paper we consider the Cramer-Rao lower bound (CRB) for the joint estimation of the carrier phase and the frequency offset from a noisy linearly modulated burst signal containing random data symbols (DSs) as well as known pilot symbols (PSs). We point out that the CRB depends on the location of the PSs in the burst, the number of PSs, the number of DSs, the signal-to-noise ratio (SNR), and the data modulation scheme. Distributing the PSs symmetrically about the center of the burst and estimating the carrier phase in the center of the burst interval decouples the frequency and phase estimation, making the CRB for phase estimation independent of the specific location of the PSs. At low and moderate SNR, the CRBs for both phase and frequency estimation decrease as the fraction of the PSs in the burst increases. In addition, the CRB for frequency estimation decreases as the PSs are separated with more DSs. Numerical evaluation of the CRB indicates that the carrier phase and frequency of a "hybrid" burst (i.e., containing PSs and DSs) can be estimated more accurately when exploiting both the presence of the DSs and the a priori knowledge about the PSs, instead of using only the knowledge about the PSs (and ignoring the DSs), or considering all the received symbols (PSs and DSs) as unknown (and ignoring the knowledge about the PSs). Comparison of the CRB with the performance of existing carrier synchronizers shows that the iterative soft-decision-directed (sDD) estimator with data-aided (DA) initialization performs very closely to the CRB and provides a large improvement over the classical non-data-aided (NDA) estimator at lower SNR. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new adaptive filter and adaptive nonlinear predictor to implement the nonlinear prediction of speech signals to solve the problem of impulse-shaped residual errors in the linear prediction. While using order statistics processing, we add an improvement that keeps time information about the input signal to the LMS-L filter proposed by Pitas and Venetsanopoulos. Tests using synthesized speech show that the proposed nonlinear predictor has superior prediction performance to the Volterra series predictor and not just the linear predictor. However, even with this proposed nonlinear predictor, the prediction accuracy degrades in experiments using real speech. Therefore, we propose a new iterative method to conquer this problem. The iterative method pays attention to the periodicity of the speech, reuses the speech data, and obtains a prediction accuracy similar to when using many data samples. In experiments on real continuous speech, the proposed predictor improved prediction accuracy by using the iterative method, and the effectiveness of the method was confirmed. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(11): 28–42, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20148 [ABSTRACT FROM AUTHOR]

In this paper two noniterative algorithms for locating small electromagnetic inclusions from the scattering amplitude at a fixed frequency are developed. In particular, a variety of numerical results is presented to highlight their potential and their limitations. [ABSTRACT FROM AUTHOR]

Abstract: Stable and accurate spectral element methods for predicting the flow of branched polymer melts past a confined cylinder are presented. The fluid is modelled using a modification of the pom–pom model known as the extended pom–pom (XPP) model. Steady and transient flows are considered in this paper. The operator integration factor splitting technique is used to discretize the governing equations in time, while the spectral element method is used in space. An iterative solution algorithm that decouples the computation of velocity and pressure from that of stress is used to solve the discrete equations. Appropriate preconditioners are developed for the efficient solution of these problems. Local upwinding factors are used to stabilize the computations. Numerical results are presented demonstrating the performance of the algorithm and the predictions of the model. The influence of the model parameters on the solution is described and, in particular, the dependence of the drag on the cylinder as function of the Weissenberg number. [Copyright &y& Elsevier]

Abstract: One of the main problems dealing with iterative methods for solving polynomial systemsis the initialization of the iteration. This paper provides an algorithm to initialize the search of solutions of polynomial systems. [Copyright &y& Elsevier]

This paper presents an algorithm to design jointly interpolation filters and decision feedback equalizers in the sense of minimum mean-square error such that the joint capacity which is neglected in conventional design is explored to improve the receiver performance. The algorithm comprises an iteration of two alternating simple quadratic minimizing operations and ensures convergence. A simulation example for the raised-cosine channel demonstrates that via this approach an improvement over the conventional design can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, we report on a non-uniform mesh smart alternating group explicit (SMAGE) parallel algorithm for the solution of non-linear singular two-point boundary value (BV) problems. The proposed method requires three non-uniform grid points and is applicable to both singular and non-singular problems. We also discuss the Newton-SMAGE parallel algorithm for the non-linear difference equations. The error analysis of the method is discussed and numerical tests are performed to demonstrate the utility of the proposed SMAGE iterative methods. [ABSTRACT FROM AUTHOR]

In this paper we establish several algorithms of parallel chaotic generalized AOR iterative methods for solving large nonsingular systems based on some given models. Under some different assumptions of coefficient matrix A and its multisplittings we obtain corresponding sufficient conditions of convergence for some relaxed parameters. [Copyright &y& Elsevier]

It has been observed that the residual polynomials resulted from successive restarting cycles of GMRES(m) may differ from one another meaningfully. In this paper, it is further shown that the polynomials can complement one another harmoniously in reducing the iterative residual. This characterization of GMRES(m) is exploited to formulate an efficient hybrid iterative scheme, which can be widely applied to existing hybrid algoritfims for solving large nonsymmetric systems of linear equations. In particular, a variant of the hybrid GMRES algorithm of Nachtigal, Reichel and Trefethen (1992) is presented. It is described how the new algorithm may offer significant performance improvements over the original one. [ABSTRACT FROM AUTHOR]

In this paper, we study a wide range of graph-based message-passing schedules for iterative decoding of low-density paritycheck (LDPC) codes. Using the Tanner graph (TG) of the code and for different nodes and edges of the graph, we relate the first iteration In which the corresponding messages I deviate from their opthnal value (corresponding to a cycle-free graph) to the girths and the lengths of the shortest closed walks in the graph. Using this result, we propose schedules, which are designed based on the distribution of girths and closed walks in the TG of the code, and categorize them as node based versus edge based, unidirectional versus bidirectional, and deterministic versus probabilistic These schedules, in some cases, outperform the previously known schedules, and in other cases, provide less complex alternatives with more or less the same performance. The performance/complex* tradeoff and the best choice of schedule appear to depend not only on the girth and closed-walk distributions of the TG, but also on the iterative decoding algorithm and channel characteristics. We examine the application of schedules to belief propagation (sum-product) over additive white Gaussian noise (AWGN) and Rayleigh fading channels, mm-sum (max-sum) over an AWGN channel, and Gallager's algorithm A over a binary symmetric channel. Index Terms-Coding, decoding algorithms, iterative decoding, low-density parity-check (LDPC) codes, message-passing schedules, Thnner graph (TG). [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an algorithm for solving the obstacle problem. We try tofind the approximated region of the contact in the obstacle problem by iteration. Numerical examples are given for the obstacle problem for a membrane and the elastic-plastic torsion problem. [Copyright &y& Elsevier]

For tridiagonal matrix systems, a simple direct algorithm giving the solution exists, but in the most general case of tridiagonal matrix with fringes, the direct solving algorithms are more complicated. For big systems, direct methods are not well fitted and iterative algorithms are preferable. In this paper a relaxation type iterative algorithm is presented. It is an extension of the backward substitution method used for simple tridiagonal matrix systems. The performances show that this algorithm is a good compromise between a direct method and other iterative methods as block SOR. Its nature suggests its use as inner solver in the solution of problems derived by application of a decomposition domain method. A special emphasis is done on the programming aspect. The solving Fortran subroutines implementing the algorithm have been generated automatically from their specification by using a computer algebra system technique. [Copyright &y& Elsevier]

It is well known that at low-bit-rate block discrete cosine transform compressed image exhibits visually annoying blocking and ringing artifacts. In this paper, we propose a noniterative, wavelet-based deblocking algorithm to reduce both types of artifacts. The algorithm exploits the fact that block discontinuities are constrained by the dc quantization interval of the quantization table, as well as the behavior of wavelet modulus maxima evolution across wavelet scales to derive appropriate threshold maps at different wavelet scales. Since ringing artifacts occur near strong edges, which can be located either along block boundaries or within blocks, suppression of block discontinuities does not always reduce ringing artifacts. By exploiting the behavior of ringing artifacts in the wavelet domain, we propose a simple yet effective method for the suppression of such artifacts. The proposed algorithm can suppress both block discontinuities and ringing artifacts effectively while preserving true edges and textural information. Simulation results and extensive comparative study with both iterative and noniterative methods reported in the literature have shown the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes asymptotic convergence properties of policy iteration in a class of stationary, infinite-horizon Markovian decision problems that arise in optimal growth theory. These problems have continuous state and control variables and must therefore be discretized in order to compute an approximate solution. The discretization may render inapplicable known convergence results for policy iteration such as those of Puterman and Brumelle [Math. Oper. Res., 4 (1979), pp. 60-69]. Under certain regularity conditions, we prove that for piecewise linear interpolation, policy iteration converges quadratically. Also, under more general conditions we establish that convergence is superlinear. We show how the constants involved in these convergence orders depend on the grid size of the discretization. These theoretical results are illustrated with numerical experiments that compare the performance of policy iteration and the method of successive approximations. [ABSTRACT FROM AUTHOR]

This paper describes a numerical procedure for solving two-dimensional elastostatics problems with multiple circular holes and elastic inclusions in a finite domain with a circular boundary. The inclusions may have arbitrary elastic properties, different from those of the matrix, and the holes may be traction free or loaded with uniform normal pressure. The loading can be applied on all or part of the finite external boundary. Complex potentials are expressed in the form of integrals of the tractions and displacements on the boundaries. The unknown boundary tractions and displacements are approximated by truncated complex Fourier series. A linear algebraic system is obtained by using Taylor series expansion without boundary discretization. The matrix of the linear system has diagonal submatrices on its diagonal, which allows the system to be effectively solved by using a block Gauss-Seidel iterative algorithm. [ABSTRACT FROM AUTHOR]

We study some continuous dynamical systems associated with constrained optimization problems. For that purpose, we introduce the concept of elliptic barrier operators and develop a unified framework to derive and analyze the associated class of gradient-like dynamical systems, called A-driven descent method (A-DM). Prominent methods belonging to this class include several continuous descent methods studied earlier in the literature such as steepest descent method, continuous gradient projection methods and Newton-type methods as well as continuous interior descent methods such as Lotka-Volterra-type differential equations and Riemannian gradient methods. Related discrete iterative methods such as proximal interior point algorithms based on Bregman functions and second order homogeneous kernels can also be recovered within our framework and allow for deriving some new and interesting dynamics. We prove global existence and strong viability results of the corresponding trajectories of (A-DM) for a smooth objective function. When the objective function is convex, we analyze the asymptotic behavior at infinity of the trajectory produced by the proposed class of dynamical systems (A -DM). In particular, we derive a general criterion ensuring the global convergence of the trajectory of (A-DM) to a minimizer of a convex function over a closed convex set. This result is then applied to several dynamics built upon specific elliptic barrier operators. Throughout the paper, our results are illustrated with many examples. [ABSTRACT FROM AUTHOR]

The paper describes a domain decomposition strategy which allows high efficiency parallel simulations of turbomachinery flows. The implicit ADI parallel solver is based on the scalar approximate factorisation. The Navier-Stokes (NS) and turbulence model equations are discretised by centred finite differences. The results prove that the parallel calculations with domain decomposition, in which each sub-domain explicitly communicates with the adjacent ones at the end of each implicit iteration, may suffer from a considerable deterioration of the convergence rate. A simple sub-iterative domain recoupling strategy allows recovering the convergence rate of a single processor computation. The strategy is carefully analysed and optimised in terms of inter-processor data communication rate and algorithm memory requirements. The span-wise domain decomposition is particularly suited for turbomachinery flows and is applied to a radial impeller and to an axial turbine stator and stage to prove the validity and the accuracy of the proposed approach. The results indicate that the parallel recoupled algorithm usually reach efficiencies of 0.8, with peaks over 0.9 with up to 16 processors, thereby allowing a considerable speed-up of design and verification calculations. [ABSTRACT FROM AUTHOR]

In this paper, we show why the modified Gram--Schmidt algorithm generates a well- conditioned set of vectors. This result holds under the assumption that the initial matrix is not 'too ill-conditioned' in a way that is quantified. As a consequence we show that if two iterations of the algorithm are performed, the resulting algorithm produces a matrix whose columns are orthogonal up to machine precision. Finally, we illustrate through a numerical experiment the sharpness of our result. [ABSTRACT FROM AUTHOR]

Many problems in image restoration can be formulated as either an unconstrained non-linear minimization problem, usually with a Tikhonov-like regularization, where the regularization parameter has to be determined; or as a fully constrained problem, where an estimate of the noise level, either the variance or the signal-to-noise ratio, is available. The formulations are mathematically equivalent. However, in practice, it is much easier to develop algorithms for the unconstrained problem, and not always obvious how to adapt such methods to solve the corresponding constrained problem. In this paper, we present a new method which can make use of any existing convergent method for the unconstrained problem to solve the constrained one. The new method is based on a Newton iteration applied to an extended system of non-linear equations, which couples the constraint and the regularized problem, but it does not require knowledge of the Jacobian of the irregularity functional. The existing solver is only used as a black box solver, which for a fixed regularization parameter returns an improved solution to the unconstrained minimization problem given an initial guess. The new modular solver enables us to easily solve the constrained image restoration problem; the solver automatically identifies the regularization parameter, during the iterative solution process. We present some numerical results. The results indicate that even in the worst case the constrained solver requires only about twice as much work as the unconstrained one, and in some instances the constrained solver can be even faster. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper is devoted to developing a complete algorithm for solving a class of 3D elliptic equations with discontinuous coefficients (so‐called interface problems). The algorithm is based on a more accurate discretization of the problem, as well as on an efficient solution of the discretized equations. A new seven‐point finite volume discretization on cell‐centred grids is derived. It is proved that this discretization is second‐order accurate in the discrete W12 norm. A multigrid algorithm exploiting pointwise a Jacobi smoother is used to solve the ill‐conditioned system of linear algebraic equations arising after the discretization of the above problem. It is demonstrated that the choice of the stopping criterion plays a significant role for the efficiency of the iterative solver. The discretization and the iterative solver are tested in solving an eight‐corner problem (i.e. with different diffusivity coefficients in eight subregions). Second‐order convergence for both the solution and the flux is observed in numerical experiments. Numerical experiments also demonstrate that the algorithm developed for solving 3D interface problems is robust and fast. [ABSTRACT FROM PUBLISHER]

Large linear systems arising in CFD problems are often solved using iterative methods which typically combine an accelerator such as GMRES and a preconditioner. This paper presents results using several preconditioning techniques, which are not standard in CFD. Numerical tests are carried out for solving three-dimensional incompressible, compressible and magnetohydrodynamic (MHD) problems. A selection of numerical results is presented showing in particular that the flexible GMRES algorithm preconditioned with ILUT factorization provides a fairly robust iterative solver. [ABSTRACT FROM AUTHOR]

Dispersed generators (DGs) such as fuel cells and solar cells will be installed on the demand side of power systems. Such dispersed facilities can reduce distribution system loss by appropriate allocation. Planning and operation of distribution systems with the dispersed facilities have already been discussed. In this paper, the authors discuss the extent to which distribution system loss can be reduced if DGs are optimally allocated on the demand side of the distribution system. In order to determine the optimal allocation and size of DGs so as to minimize the distribution system loss, an algorithm based on tabu search is employed. The proposed algorithm consists of iterated nested use of the tabu search algorithm. Namely, in the proposed algorithm, in one computational iteration, after the location of the DG is temporarily determined by tabu search, the size of the DG is also determined by tabu search so as to minimize the distribution system loss for the temporarily determined allocation. Numerical simulations are carried out for two system models in order to examine the validity of the algorithm. © 2001 Scripta Technica, Electr Eng Jpn, 136(2): 1–8, 2001 [ABSTRACT FROM AUTHOR]

The translation matrix for the multilevel fast multipole algorithm (MLFMA) in an FISC (fast Illinois solver code) is calculated directly, and the complexity is O(N3/2), where N is the number of unknowns. For a problem with a small electrical size, the CPU time for calculating the translation matrix can be negligible. But for large problems, the calculation time increases significantly. In this paper, we use interpolation to calculate the translation matrix, and the complexity is reduced to O(N). Different interpolation techniques are tested, and it is found that the Lagrange polynomial interpolation with high sampling rates is the best. The saving factor is 10 for the VFY218 at 4 GHz. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 30: 109–114, 2001. [ABSTRACT FROM AUTHOR]

In this paper, we study some efficient numerical methods for parameter identifications in elliptic systems. The proposed numerical methods are conducted iteratively and each iteration involves only solving positive definite linear algebraic systems, although the original inverse problems are ill-posed and highly nonlinear. The positive definite systems can be naturally preconditioned with their corresponding block diagonal matrices. Numerical experiments are presented to illustrate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The DEFLATED-GMRES(m, k) method is one of the major iterative solvers for the large sparse linear systems of equations, x = b. This algorithm assembles a preconditioner adaptively for the GMRES(m) method based on eigencomponents gathered from the Arnoldi process during iterations. It is usually known that if a restarted GMRES(m) method is used to solve linear systems of equations, the information of the smallest eigencomponents is lost at each restart and the super-linear convergence may also be lost. In this paper, we propose an adaptive procedure that combines the DEFLATED-GMRES(m, k) algorithm and the determination of a restart frequency automatically. It is shown that a new algorithm combining elements of both will reduce the negative effects of the restarted procedure. The numerical experiments are presented on three test problems by using the MIMD parallel machine AP3000. From these numerical results, we show that the proposed algorithm leads to faster convergence than the conventional DEFLATED-GMRES(m, k) method. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents the results of a scalability study for a three-dimensional semi- coarsening multigrid solver on a distributed memory computer. In particular, we are interested in the scalability of the solver-how the solution time varies as both problem size and number of processors are increased. For an iterative linear solver, scalability involves both algorithmic issues and implementation issues. We examine the scalability of the solver theoretically by constructing a simple parallel model and experimentally by results obtained on an IBM SP. The results are compared with those obtained for other solvers on the same computer. [ABSTRACT FROM AUTHOR]

In a previous work, the generalized forward–backward (GFB) method was proposed to compute the scattering from targets on rough ocean-like surfaces. In this paper, we develop an acceleration of the GFB method based on the fast multipole method (FMM). The FMM is adapted to reduce the operational cost associated with the iterative computations in the target regions. The proposed method is shown to converge in a low number of iterations, and allows a significant reduction in the computational and storage costs with respect to the conventional GFB formulation. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 78–83, 2000. [ABSTRACT FROM AUTHOR]

Given convex scattered data in R3 we consider the constrained interpolation problem of finding a smooth, minimal Lp-norm (1 < p < ∞) interpolation network that is convex along the edges of an associated triangulation. In previous work the problem has been reduced to the solution of a nonlinear system of equations. In this paper we formulate and analyse a Newton-type algorithm for solving the corresponding type of systems. The correctness of the application of the proposed method is proved and its superlinear (in some cases quadratic) convergence is shown. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to derive new computable convergence bounds for GMRES. The new bounds depend on the initial guess and are thus conceptually different from standard “worst-case” bounds. Most importantly, approximations to the new bounds can be computed from information generated during the run of a certain GMRES implementation. The approximations allow predictions of how the algorithm will perform. Heuristics for such predictions are given. Numerical experiments illustrate the behavior of the new bounds as well as the use of the heuristics. [ABSTRACT FROM AUTHOR]

In this paper a recently proposed additive version of the algebraic multilevel iteration method for iterative solution of elliptic boundary value problems is studied. The method constructs a nearly optimal order parameter-free preconditioner, which is robust with respect to anisotropy and discontinuity of the problem coefficients. It uses a new strategy for approximating the blocks corresponding to ‘new’ basis functions on each discretization level. To cope with the difficulties arising from the anisotropy, the problem on the coarsest mesh is solved using a bordering technique with a special choice of bordering vectors. The aim is to find a parameter-free ‘black-box’ robust solver. The results are derived in the framework of a hierarchical basis, linear finite element discretization of an elliptic problem on arbitrary triangular meshes, and a hierarchical basis, bilinear finite element discretization on Cartesian meshes. A comparison of the method with some other iterative solution techniques is presented. Robustness and high efficiency of the proposed algorithm are demonstrated on several model-type problems.

The restarted GMRES algorithm proposed by Saad and Schultz [SIAM J. Sci. Statist. Comput., 7 (1986), pp. 856-869] is one of the most popular iterative methods for the solution of large linear systems of equations Ax = b with a nonsymmetric and sparse matrix. This algorithm is particularly attractive when a good preconditioner is available. The present paper describes two new methods for determining preconditioners from spectral information gathered by the Arnoldi process during iterations by the restarted GMRES algorithm. These methods seek to determine an invariant subspace of the matrix A associated with eigenvalues close to the origin and to move these eigenvalues so that a higher rate of convergence of the iterative methods is achieved. [ABSTRACT FROM AUTHOR]

This paper shows the ability of a domain decomposition method with a Neumann-Neumann preconditioner to solve industrial and biomechanical problems. For a mid-size biomedical simulation involving soft tissues the results obtained by using this algorithm are comapred to those obtained by more classical methods. The present algorithm thus appears to be more reliable than standard iterative methods and more efficient than a direct method. Copyright © 1999 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The BR algorithm, a new method for calculating the eigenvalues of an upper Hes- senberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrow-band, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30–60 in computing time and a factor of over 100 in matrix storage space. [ABSTRACT FROM AUTHOR]

When a formant is modified based on the pole of the AR model, a problem arises due to the interaction between the pole to be modified and other poles such as the apparent elimination of the formant, if the frequency of the considered pole simply is modified. To solve this problem, the method proposed in this paper uses the spectral intensity at the pole frequency as a new future parameter and the spectral intensity at the pole after the frequency modification is corrected to the desired value by iterative processings. To examine the effectiveness of the proposed method, an experiment was conducted in which a phoneme in a word is replaced into the same phoneme in a different environment, and then the format frequency is modified by the proposed method in order to restore the original speech. The speeches before and after the modification are compared in terms of the cepstral distance (CD) from the original speech, and it is verified that the proposed method is effective in the modification of the formant. An attempt is made to solve the formant discontinuity problem at the concatenation point in waveform based speech synthesis-by-rule and to modify the formant frequency accompanying the modification of the fundamental frequency using the proposed method. It was verified as a result of experiment that the speech after formant modification is better than the speech before modification, indicating that the proposed method is useful in improving the quality of the synthesized speech. [ABSTRACT FROM AUTHOR]

This paper presents a general method to associate the operator splitting for the Peaceman-Rachford procedure with a decomposition of the domain in problems arising from finite element discretization of partial differential equations. The algorithm is provably convergent without any symmetry requirement. Moreover, this method possesses the significant advantage of making the linear systems of the Peaceman-Rachford iteration block diagonal and therefore perfectly appropriate for parallel processing. Not only is sparsity not affected but a reduction of the bandwidth occurs. In fact, for appropriate choices of nonconforming finite element spaces, this method makes directly possible elementwise processing. This option remains available in general for higher-dimensional problems by applying the splitting algorithm recursively. Practical implementation requires nothing more than the standard finite element assembly procedure and some bookkeeping to relate a few different orderings of the nodes. In addition to all these attractive features, the method is rapidly convergent and remains highly competitive even when used on a serial machine. [ABSTRACT FROM AUTHOR]

The solution of nonsymmetric systems of linear equations continues to be a difficult problem. A main algorithm for solving nonsymmetric problems is restarted GMRES. The algorithm is based on restarting full GMRES every s iterations, for some integer s > 0. This paper considers the impact of the restart frequency s on the convergence and work requirements of the method. It is shown that a good choice of this parameter can lead to reduced solution time, while an improper choice may hinder or preclude convergence. An adaptive procedure is also presented for determining automatically when to restart. The results of numerical experiments are presented. [ABSTRACT FROM AUTHOR]

In the paper the author presents a novel point of view for the refinement and derefinement algorithms of triangular nested meshes using fractal concepts and iterated function systems (IFS). The fractal behaviour can be understood in the sense that these meshes feature a remarkable amplifying invariance under changes of magnification. Here we compare the meshes obtained by the combination of these algorithms with those presented by Bova and Carey (1992). Although both of the meshes are very similar, the current algorithms automatically build and manage sequences of nested irregular discretizations of the domain. The author illustrates here how the application of IFS families is equivalent to the use of an adaptive strategy that combines the refinement procedure with the derefinement one. [ABSTRACT FROM AUTHOR]

In the paper [1], a direct version of the classical Kaczmarz algorithm was proposed, which gives us in only one iteration a solution of an arbitrary consistent system of linear equations. Unfortunately, as any direct method applied to large sparse matrices, this algorithm is based on some modifications of the system matrix sparsity structure such that a big fill-in appears. In order to overcome this difficulty, in the present paper we propose a modified version of this direct Kaczmarz algorithm in which the transformations applied to the system matrix try to conserve the initial sparsity structure. This transformations are done via clustering using Jaccard and Hamming distances. The modified Kaczmarz algorithm is no more a direct method, but we obtain an acceleration of convergence with respect to the classical Kaczmarz algorithm. Numerical experiments which ilustrate the efficiency of our algorithm are also presented. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Inspired by several recent developments in regularization theory, optimization, and signal processing, we present and analyze a numerical approach to multi-penalty regularization in spaces of sparsely represented functions. The sparsity prior is motivated by the largely expected geometrical/structured features of high-dimensional data, which may not be well-represented in the framework of typically more isotropic Hilbert spaces. In this paper, we are particularly interested in regularizers which are able to correctly model and separate the multiple components of additively mixed signals. This situation is rather common as pure signals may be corrupted by additive noise. To this end, we consider a regularization functional composed by a data-fidelity term, where signal and noise are additively mixed, a non-smooth and non-convex sparsity promoting term, and a penalty term to model the noise. We propose and analyze the convergence of an iterative alternating algorithm based on simple iterative thresholding steps to perform the minimization of the functional. By means of this algorithm, we explore the effect of choosing different regularization parameters and penalization norms in terms of the quality of recovering the pure signal and separating it from additive noise. For a given fixed noise level numerical experiments confirm a significant improvement in performance compared to standard one-parameter regularization methods. By using high-dimensional data analysis methods such as principal component analysis, we are able to show the correct geometrical clustering of regularized solutions around the expected solution. Eventually, for the compressive sensing problems considered in our experiments we provide a guideline for a choice of regularization norms and parameters. [ABSTRACT FROM AUTHOR]

Iterative algorithms are widely applied in reliability analysis and design optimization. Nevertheless, phenomena of failed convergence, such as periodic oscillation, bifurcation, and chaos, are oftentimes observed in iterative procedures of solving some nonlinear problems. In the present paper, the essential causes of numerical instabilities including periodic oscillation and chaos of iterative solutions are revealed by the eigenvalue-based stability analysis of iterative schemes. To understand and control these instabilities, the stability transformation method (STM), which is capable of tackling numerical instabilities of iterative algorithms in reliability analysis and design optimization, is proposed. Finally, several benchmark examples of convergence control of PMA (performance measure approach) for probabilistic analysis and the SORA (sequential optimization and reliability assessment) for reliability-based design optimization (RBDO) are presented. The observations from the benchmark examples indicate that the STM is a promising approach to achieve convergence control for iterative algorithms in reliability analysis and design optimization. [ABSTRACT FROM AUTHOR]

In this paper, the Alternating Group Explicit (AGE) method is developed from a judicious splitting of the implicit equations derived from the finite difference discretisation of the partial differential equations. The resulting equations can be reformulated in a (2 × 2) explicit form resulting in a new stable and efficient method. Finally, the AGE method is used to investigate the numerical solution of the Telegraph equation in various 2D forms. [ABSTRACT FROM AUTHOR]