New higher-order methods for the simultaneous inclusion of polynomial zeros

Higher-order methods for the simultaneous inclusion of complex zeros of algebraic polynomials are presented in parallel (total-step) and serial (single-step) versions. If the multiplicities of each zeros are given in advance, the proposed methods can be extended for multiple zeros using appropriate corrections. These methods are constructed on the basis of the zero-relation of Gargantini’s type, the inclusion isotonicity property and suitable corrections that appear in two-point methods of the fourth order for solving nonlinear equations. It is proved that the order of convergence of the proposed methods is at least six. The computational efficiency of the new methods is very high since the acceleration of convergence order from 3 (basic methods) to 6 (new methods) is attained using only n polynomial evaluations per iteration. Computational efficiency of the considered methods is studied in detail and two numerical examples are given to demonstrate the convergence behavior of the proposed methods.     

Proximal minimizations with D-functions and the massively parallel solution of linear network programs

This paper discusses the massively parallel solution of linear network programs. It integrates the general algorithmic framework of proximal minimization with D-functions (PMD) with primal-dual row-action algorithms. Three alternative algorithmic schemes are studied: quadratic proximal point, entropic proximal point, and least 2-norm perturbations. Each is solving a linear network problem by solving a sequence of nonlinear approximations. The nonlinear subproblems decompose for massively parallel computing. The three algorithms are implemented on a Connection Machine CM-2 with up to 32K processing elements, and problems with up to 16 million variables are solved. A comparison of the three algorithms establishes their relative efficiency. Numerical experiments also establish the best internal tactics which can be used when implementing proximal minimization algorithms. Finally, the new algorithms are compared with an implementation of the network simplex algorithm executing on a CRAY Y-MP vector supercomputer.     

Computing the radiating source term in the system of radiation magnetohydrodynamics

In this paper we present results for the extended short-characteristics method (ESC) for computing the radiation source term Q_rad with special focus on using this method in combination with a finite-volume scheme for solving the equations of radiation magnetohydrodynamics (RMHD). Since the approximation of the radiation source term takes up a large amount of the computational cost, the efficiency of this part of the numerical scheme is an essential consideration. We compare this method with other methods from the literature with respect to their computational efficiency. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A new logarithmic-quadratic proximal method for nonlinear complementarity problems

In this paper, we propose a new modified logarithmic-quadratic proximal (LQP) method for solving nonlinear complementarity problems (NCP). We suggest using a prediction-correction method to solve NCP. The predictor is obtained via solving the LQP system approximately under significantly relaxed accuracy criterion and the new iterate is computed by using a new step size α_k. Under suitable conditions, we prove that the new method is globally convergent. We report preliminary computational results to illustrate the efficiency of the proposed method. This new method can be considered as a significant refinement of the previously known methods for solving nonlinear complementarity problems.     

A comparative study of decomposition algorithms for stochastic combinatorial optimization

This paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from two different applications. In order to preserve the commonalities among the algorithms in our experiments, we have designed a testbed which is used to study instances arising in server location under uncertainty and strategic supply chain planning under uncertainty. Insights related to alternative implementation issues leading to more efficient implementations, benchmarks for serial processing, and scalability of the methods are also presented. The computational experience demonstrates the promising potential of the disjunctive decomposition (D ²) approach towards solving several large-scale problem instances from the two application areas. Furthermore, the study shows that convergence of the D ² methods for stochastic combinatorial optimization (SCO) is in fact attainable since the methods scale well with the number of scenarios.     

Efficient fourth orderP-stable formulae

In this paper, a family of fourth orderP-stable methods for solving second order initial value problems is considered. When applied to a nonlinear differential system, all the methods in the family give rise to a nonlinear system which may be solved using a modified Newton method. The classical methods of this type involve at least three (new) function evaluations per iteration (that is, they are 3-stage methods) and most involve using complex arithmetic in factorising their iteration matrix. We derive methods which require only two (new) function evaluations per iteration and for which the iteration matrix is a true real perfect square. This implies that real arithmetic will be used and that at most one real matrix must be factorised at each step. Also we consider various computational aspects such as local error estimation and a strategy for changing the step size.     

A class of asynchronous multisplitting two-stage iterations for large sparse block systems of weakly nonlinear equations

For the block system of weakly nonlinear equations Ax=G(x), where is a large sparse block matrix and is a block nonlinear mapping having certain smoothness properties, we present a class of asynchronous parallel multisplitting block two-stage iteration methods in this paper. These methods are actually the block variants and generalizations of the asynchronous multisplitting two-stage iteration methods studied by Bai and Huang (Journal of Computational and Applied Mathematics 93(1) (1998) 13–33), and they can achieve high parallel efficiency of the multiprocessor system, especially, when there is load imbalance. Under quite general conditions that is a block H-matrix of different types and is a block P-bounded mapping, we establish convergence theories of these asynchronous multisplitting block two-stage iteration methods. Numerical computations show that these new methods are very efficient for solving the block system of weakly nonlinear equations in the asynchronous parallel computing environment.     

Convergence Aspects of Step-Parallel Iteration of Runge-Kutta Methods for Delay Differential Equations

Implicit Runge-Kutta methods are known as highly accurate and stable methods for solving differential equations. However, the iteration technique used to solve implicit Runge-Kutta methods requires a lot of computational efforts. To lessen the computational effort, one can iterate simultaneously at a number of points along the t-axis. In this paper, we extend the PDIRK (Parallel Diagonal Iterated Runge-Kutta) methods to delay differential equations (DDEs). We give the region of convergence and analyze the speed of convergence in three parts for the P-stability region of the Runge-Kutta corrector. It is proved that PDIRK methods to DDEs are efficient, and the diagonal matrix D of the PDIRK methods for DDES can be selected in the same way as for ordinary differential equations (ODEs).     

A parallel iterative solver for positive-definite systems with hybrid MPI–OpenMP parallelization for multi-core clusters

This article is devoted to the development and study of an algorithm for solving large systems of linear algebraic equations with sparse stiffness matrix on supercomputer by using the preconditioned conjugate gradient method (PCG). An efficient preconditioner is constructed on the basis of the domain decomposition method (the additive Schwarz method) which makes it possible to implement the algorithm on several computing nodes. We describe the parallel algorithm of the action of the stiffness matrix and the preconditioner on a vector. In addition, to increase the computational efficiency we make use of the routines from Intel^®MKL: the direct solver (PARDISO) and the matrix–vector multiplication for sparse matrices (Sparse BLAS). We also study efficiency of using OpenMP directives on each computational node and compare it with pure MPI parallelization. The corresponding performance and scalability charts are presented.     

Interpretation by Implementation for Understanding a Multiagent Organization

This paper stresses the importance of focusing on the modeling process of computational models for precisely understanding a complex organization and for solving given problems in the organization. Based on our claim, we proposes a method of interpretation by implementation (IbI), which explores factors that drastically change simulation results through an investigation on the modeling process of computational models. A careful investigation on the capabilities of the IbI approach, which comprises the three methods of (a) breakdown and representation, (b) assumption or premise modification, and (c) layer change investigation, derives the following conclusions: (1) the IbI approach has the potential of finding underlying factors that determine the characteristics of an organization; (2) the IbI approach can specify points of attention at necessary levels when analyzing an organization; and (3) the IbI approach has suchadvantages as wide applicability, the effective use of employed models, and KISS principle support.     

Asynchronous Multisplitting GAOR Method and Asynchronous Multisplitting SSOR Method for Systems of Weakly Nonlinear Equations

In this article, we introduce two new asynchronous multisplitting methods for solving the system of weakly nonlinear equations Ax = G(x) in which A is an n × n real matrix and G(x) = (g ₁(x), g ₂(x), . . . , g _n (x)) ^T is a P-bounded mapping. First, by generalized accelerated overrelaxation (GAOR) technique, we introduce the asynchronous parallel multisplitting GAOR method (including the synchronous parallel multisplitting AOR method as a special case) for solving the system of weakly nonlinear equations. Second, asynchronous parallel multisplitting method based on symmetric successive overrelaxation (SSOR) multisplitting is introduced, which is called asynchronous parallel multisplitting SSOR method. Then under suitable conditions, we establish the convergence of the two introduced methods. The given results contain synchronous multisplitting iterations as a special case.     

Parallelization Strategies for Rollout Algorithms

Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving NP-hard combinatorial optimization problems. The main advantage of these approaches is related to their capability of magnifying the effectiveness of any given heuristic algorithm. However, one of the main limitations of rollout algorithms in solving large-scale problems is represented by their computational complexity. Innovative versions of rollout algorithms, aimed at reducing the computational complexity in sequential environments, have been proposed in our previous work [9]. In this paper, we show that a further reduction can be accomplished by using parallel technologies. Indeed, rollout algorithms have very appealing characteristics that make them suitable for efficient and effective implementations in parallel environments, thus extending their range of relevant practical applications.We propose two strategies for parallelizing rollout algorithms and we analyze their performance by considering a shared-memory paradigm. The computational experiments have been carried out on a SGI Origin 2000 with 8 processors, by considering two classical combinatorial optimization problems. The numerical results show that a good reduction of the execution time can be obtained by exploiting parallel computing systems.     

Single‐cell discretization of O(kh2 + h4) for ∂u/∂n for three‐space dimensional mildly quasi‐linear parabolic equation

In this article, using a single computational cell, we report some stable two‐level explicit finite difference approximations of O(kh² + h⁴) for ?u/?n for three‐space dimensional quasi‐linear parabolic equation, where h > 0 and k > 0 are mesh sizes in space and time directions, respectively. When grid lines are parallel to x‐, y‐, and z‐coordinate axes, then ?u/?n at an internal grid point becomes ?u/?x, ?u/?y, and ?u/?z, respectively. The proposed methods are also applicable to the polar coordinates problems. The proposed methods have the simplicity in nature and use the same marching type of technique of solution. Stability analysis of a linear difference equation and computational efficiency of the methods are discussed. The results of numerical experiments are compared with exact solutions. © 2003 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 19: 327–342, 2003.     

Parallel multigrid method for finite element simulations of complex flow problems on locally refined meshes

We present a parallel multigrid solver on locally refined meshes for solving very complex three‐dimensional flow problems. Besides describing the parallel implementation in detail, we prove the smoothing property of the suggested iteration for a simple model problem. For demonstration of the efficiency and feasibility of the solver, we show a chemically reactive flow simulation for a Methane burner using detailed chemical reaction modeling. Further, we give the results of an ocean flow simulation. All described methods are implemented in the finite element toolbox Gascoigne. Copyright © 2010 John Wiley & Sons, Ltd.     

Local and parallel finite element method for solving the biharmonic eigenvalue problem of plate vibration

In this paper, we establish a new local and parallel finite element discrete scheme based on the shifted‐inverse power method for solving the biharmonic eigenvalue problem of plate vibration. We prove the local error estimation of finite element solution for the biharmonic equation/eigenvalue problem and prove the error estimation of approximate solution obtained by the local and parallel scheme. When the diameters of three grids satisfy H⁴ = ?(w²) = ?(h), the approximate solutions obtained by our schemes can achieve the asymptotically optimal accuracy. The numerical experiments show that the computational schemes proposed in this paper are effective to solve the biharmonic eigenvalue problem of plate vibration.     

Preconditioning in implicit initial-value problem methods on parallel computers

Implicit step-by-step methods for numerically solving the initial-value problem {y=f(y),y(0)=y ₀} usually lead to implicit relations of which the Jacobian can be approximated by a matrix of the special formK=I–hM J, whereM is a matrix characterizing the step-by-step method andJ is the Jacobian off. Similar implicit relations are encountered in discretizing initial-value problems for other types of functional equations such as VIEs, VIDEs and DDEs. Application of (modified) Newton iteration for solving these implicit relations requires the LU-decomposition ofK. Ifs andd are the dimensions ofM andJ, respectively, then this LU-decomposition is anO(s ³ d ³) process, which is extremely costly for large values ofsd. We shall discuss parallel iteration methods for solving the implicit relations that exploit the special form of Jacobian matrixK. Their main characteristic is that each processor is required to compute LU-decompositions of matrices of dimensiond, so that this part of the computational work is reduced by a factors ³. On the other hand, the number of iterations in these parallel iteration methods is usually much larger than in Newton iteration. In this contribution, we will try to reduce the number of iterations by improving the convergence of such parallel iteration methods by means of preconditioning.This paper is presented as an outcome of the LMS Durham Symposium convened by Professor C.T.H. Baker on 4–14 July 1992, with support from the SERC under grant reference number GR/H03964.     

Optimally Computing the Shortest Weakly Visible Subedge of a Simple Polygon

Given ann-vertex simple polygonP, the problem of computing the shortest weakly visible subedge ofPis that of finding a shortest line segmentson the boundary ofPsuch thatPis weakly visible froms(ifsexists). In this paper, we present new geometric observations that are useful for solving this problem. Based on these geometric observations, we obtain optimal sequential and parallel algorithms for solving this problem. Our sequential algorithm runs inO(n) time, and our parallel algorithm runs inO(log n) time usingO(n/log n) processors in the CREW PRAM computational model. Using the previously best known sequential algorithms to solve this problem would takeO(n²) time. We also give geometric observations that lead to extremely simple and optimal algorithms for solving, both sequentially and in parallel, the case of this problem where the polygons are rectilinear.     

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

During the past decades, explicit finite element approximate inverse preconditioning methods have been extensively used for efficiently solving sparse linear systems on multiprocessor systems. The effectiveness of explicit approximate inverse preconditioning schemes relies on the use of efficient preconditioners that are close approximants to the coefficient matrix and are fast to compute in parallel. New parallel computational techniques are proposed for the parallelization of the Optimized Banded Generalized Approximate Inverse Finite Element Matrix (OBGAIFEM) algorithm, based on the concept of the “fish bone” computational approach, and for the Explicit Preconditioned Conjugate Gradient type methods on a General Purpose Graphics Processing Unit (GPGPU). The proposed parallel methods have been implemented using Compute Unified Device Architecture (CUDA) developed by NVIDIA. Finally, numerical results for the performance of the finite element explicit approximate inverse preconditioning for solving characteristic two dimensional boundary value problems on a massive multiprocessor interface on a GPU are presented. The CUDA implementation issues of the proposed methods are also discussed.     

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

During the past decades, explicit finite element approximate inverse preconditioning methods have been extensively used for efficiently solving sparse linear systems on multiprocessor systems. The effectiveness of explicit approximate inverse preconditioning schemes relies on the use of efficient preconditioners that are close approximants to the coefficient matrix and are fast to compute in parallel. New parallel computational techniques are proposed for the parallelization of the Optimized Banded Generalized Approximate Inverse Finite Element Matrix (OBGAIFEM) algorithm, based on the concept of the “fish bone” computational approach, and for the Explicit Preconditioned Conjugate Gradient type methods on a General Purpose Graphics Processing Unit (GPGPU). The proposed parallel methods have been implemented using Compute Unified Device Architecture (CUDA) developed by NVIDIA. Finally, numerical results for the performance of the finite element explicit approximate inverse preconditioning for solving characteristic two dimensional boundary value problems on a massive multiprocessor interface on a GPU are presented. The CUDA implementation issues of the proposed methods are also discussed.     

Single‐cell fourth‐order difference approximations for $${\font\twelvesl=cmsl10\partial {\sl\bf u}\over\partial {\sl\bf x}},{\partial \sl\bf u\over\partial\sl\bf y}$$ , and $${\font\twelvesl=cmsl10\partial {\sl\bf u}\over\partial{\sl\bf z}}$$ of the three‐dimensional quasi‐linear elliptic equation

Finite difference methods of O(h⁴) are proposed for obtaining estimates of first‐order partial derivatives of the solution of three‐dimensional quasi‐linear elliptic equation with mixed derivative terms subject to Dirichlet boundary conditions on a uniform cubic grid. In all the cases, we use a single computational cell and the methods are applicable to the problems both in cartesian and polar coordinates. The utility of the new methods is shown by testing the methods on three‐dimensional poisson solvers in polar coordinates. Some numerical examples are provided to demonstrate the accuracy and efficiency of the methods discussed. © 2000 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 16: 417–425, 2000