An efficient scheme for the implementation of implicit Runge-Kutta methods

A scheme is proposed for solving nonlinear algebraic equations arising in the implementation of the implicit Runge-Kutta methods. In contrast to the available schemes, not only the starting values of the variables but also those of the derivatives are predicted. This makes it possible to reduce the number of evaluations of the function (the right-hand side) at each implicit stage without significantly reducing the accuracy of integration.     

An efficient step size control for continuation methods

In solving a nonlinear equation by the use of a continuation method one of the crucial problems is the choice of the step sizes. We present a model for the total computational cost of a standard numerical continuation process and solve the problem of optimal step size control for this model. Using the theoretical results as a basis, we develop an adaptive step size algorithm for Newton's method. This procedure is computationally inexpensive and it gives quite satisfactory results compared to some other numerical experiments found in the literature.     

An efficient heuristic based on machine workload for the flowshop scheduling problem with setup and removal

We are concerned in this paper with solving ann jobs,M machines flowshop scheduling problem where the objective function is the minimization of the makespan. We take into account setup, processing and removal times separately. After drawing up a synthesis of existing work which addresses this type of problems, we propose a new heuristic algorithm which is based on the machine workload to find an efficient permutation schedule. Computational experiences show that our algorithm yields excellent results, particularly when bottleneck machines are present.     

An efficient time-step-based self-adaptive algorithm for predictor-corrector methods of Runge-Kutta type

Finding an efficient implementation variant for the numerical solution of problems from computational science and engineering involves many implementation decisions that are strongly influenced by the specific hardware architecture. The complexity of these architectures makes it difficult to find the best implementation variant by manual tuning. For numerical solution methods from linear algebra, auto-tuning techniques based on a global search engine as they are used for ATLAS or FFTW can be used successfully. These techniques generate different implementation variants at installation time and select one of these implementation variants either at installation time or at runtime, before the computation starts. For some numerical methods, auto-tuning at installation time cannot be applied directly, since the best implementation variant may strongly depend on the specific numerical problem to be solved. An example is solution methods for initial value problems (IVPs) of ordinary differential equations (ODEs), where the coupling structure of the ODE system to be solved has a large influence on the efficient use of the memory hierarchy of the hardware architecture. In this context, it is important to use auto-tuning techniques at runtime, which is possible because of the time-stepping nature of ODE solvers.In this article, we present a sequential self-adaptive ODE solver that selects the best implementation variant from a candidate pool at runtime during the first time steps, i.e., the auto-tuning phase already contributes to the progress of the computation. The implementation variants differ in the loop structure and the data structures used to realize the numerical algorithm, a predictor-corrector (PC) iteration scheme with Runge-Kutta (RK) corrector considered here as an example. For those implementation variants in the candidate pool that use loop tiling to exploit the memory hierarchy of a given hardware platform we investigate the selection of tile sizes. The self-adaptive ODE solver combines empirical search with a model-based approach in order to reduce the search space of possible tile sizes. Runtime experiments demonstrate the efficiency of the self-adaptive solver for different IVPs across a range of problem sizes and on different hardware architectures.     

An efficient solution method for rank two quasiconcave minimization problems

The technique of dimension reduction earlier developed by the first author is applied to the class of nonconvex minimization problems having the so called rank two property. This class includes in particular the problem of minimizing the product of two affine functions over a polytope. An efficient method for solving this class of problems is presented. Also some results of computational experiments with this method are discussed     

An efficient simulation algorithm for the generalized von Mises distribution of order two

In this article we propose an exact efficient simulation algorithm for the generalized von Mises circular distribution of order two. It is an acceptance-rejection algorithm with a piecewise linear envelope based on the local extrema and the inflexion points of the generalized von Mises density of order two. We show that these points can be obtained from the roots of polynomials and degrees four and eight, which can be easily obtained by the methods of Ferrari and Weierstrass. A comparative study with the von Neumann acceptance-rejection, with the ratio-of-uniforms and with a Markov chain Monte Carlo algorithms shows that this new method is generally the most efficient.     

An efficient multigrid-FEM method for the simulation of solid–liquid two phase flows

An efficient multigrid-FEM method for the detailed simulation of solid–liquid two phase flows with large number of moving particles is presented. An explicit fictitious boundary method based on a FEM background grid which covers the whole computational domain and can be chosen independently from the particles of arbitrary shape, size and number is used to deal with the interactions between the fluid and the particles. Since the presented method treats the fluid part, the calculation of forces and the movement of particles in a subsequent manner, it is potentially powerful to efficiently simulate real particulate flows with huge number of particles. The presented method is first validated using a series of simple test cases, and then as an illustration, simulations of three big disks plunging into 2000 small particles, and of sedimentation of 10,000 particles in a cavity are presented.     

An efficient algorithm for sparse null space basis problem using ABS methods

We propose a way to use the Markowitz pivot selection criterion for choosing the parameters of the extended ABS class of algorithms to present an effective algorithm for generating sparse null space bases. We explain in detail an efficient implementation of the algorithm, making use of the special MATLAB 7.0 functions for sparse matrix operations and the inherent efficiency of the ANSI C programming language. We then compare our proposed algorithm with an implementation of an efficient algorithm proposed by Coleman and Pothen with respect to the computing time and the accuracy and the sparsity of the generated null space bases. Our extensive numerical results, using coefficient matrices of linear programming problems from the NETLIB set of test problems show the competitiveness of our implemented algorithm.     

An improved branch-and-bound algorithm for the two machine total completion time flow shop problem

We consider the two machine total completion time flow shop scheduling problem. This problem is known to be NP-Hard in the strong sense. An improved Lagrangean relaxation approach is proposed. Two new dominance criteria are introduced to curtail the enumeration tree. A branch-and-bound algorithm capable of solving to optimality medium size problem instances is presented.     

An improved algorithm for the two machine flow shop problem with several availability constraints

In this paper we deal with the two-machine flow shop scheduling problem with several availability constraints on the first machine, under the resumable scenario. We first develop an improved algorithm with a relative worst-case error bound of 4/3. We then present a polynomially solvable case.     

A note on the efficient implementation of implicit methods for ODEs

The use of implicit methods for ODEs, e.g. implicit Runge-Kutta schemes, requires the solution of nonlinear systems of algebraic equations of dimension s · m, where m is the size of the continuous differential problem to be approximated. Usually, the solution of this system represents the most time-consuming section in the implementation of such methods. Consequently, the efficient solution of this section would improve their performance. In this paper, we propose a new iterative procedure to solve such equations on sequential computers.     

具有前瞻区间的两个工件组单机在线排序问题

研究具有前瞻区间的两个不相容工件组单位工件单机无界平行分批在线排序问题.工件按时在线到达, 目标是最小化最大完工时间. 在无界平行分批排序中, 一台容量无限制机器可将多个工件形成一批同时加工, 每一批的加工时间等于该批中最长工件的加工时间. 具有前瞻区间是指在时刻t, 在线算法能预见到时间区间(t,t+\beta]内到达的所有工件的信息.不可相容的工件组是指属于不同组的工件不能安排在同一批中加工.对该问题提供了一个竞争比为\ 1+\alpha 的最好可能的在线算法,其中\ \alpha 是方程2\alpha^{2}+(\beta +1)\alpha +\beta -2=0的一个正根, 这里0\leq \beta <1.     

The structure of invertible substitutions on a three-letter alphabet

We study the structure of invertible substitutions on three-letter alphabet. We show that there exists a fi-nite set S of invertible substitutions such that any invertible substitution can be written as Iw^oQ1^oQ2^o……^oQk,,where Iw is the inner automorphism associated with w, and ajЕS for l≤j≤k. As a consequence, M is thematrix of an invertible substitution if and only if it is a finite product of non-negative elementary matrices.     

An efficient algorithm for a job shop problem

We study the problem of minimizing makespan in a two-machine job shop with unit processing time operations. An efficient algorithm with respect to a succinct encoding of the problem instances is proposed. The algorithm is an improvement of earlier algorithms proposed for the problem by Brucker [1,2], Hefetz and Adiri [7], and Timkovskiy [15]. The idea behind the algorithm has the potential of extension to job shops with parallel machines.This research is supported in part by NSERC Grants A4619, OGP0105675, OGP0104900, General Motors of Canada, and the Manufacturing Research Corporation of Ontario.