Computational Science

chapter

Symmetric Runge-Kutta Methods with Higher Derivatives and Quadratic Extrapolation

Gennady Yu. Kulikov, Ekaterina Yu. Khrustaleva, Arkadi I. Merkulov

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 117-123

In this paper we study the symmetry of Runge-Kutta methods with higher derivatives. We find conditions which provide this property for the above numerical methods. We prove that the family of E-methods constructed earlier consists of symmetric methods only, which lead to the quadratic extrapolation technique in practice.

chapter

A Note on the Simplex Method for 2-Dimensional Second-Order Cone Programming

Yu Xia

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 124-131

We transform a 2-dimensional second-order cone program to a standard linear program in order to pivot a vector based on its three states. Related properties of the transformation are given. Based on the transformation, we interpret the simplex method and its sensitivity analysis for the 2-dimensional second-order cone programming, especially the state changes. Finally, we give some applications of...

chapter

Local Linearization-Runge Kutta (LLRK) Methods for Solving Ordinary Differential Equations

H. Cruz, R. J. Biscay, F. Carbonell, J. C. Jimenez, more

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 132-139

A new class of stable methods for solving ordinary differential equations (ODEs) is introduced. This is based on combining the Local Linearization (LL) integrator with other extant discretization methods. For this, an auxiliary ODE is solved to determine a correction term that is added to the LL approximation. In particular, combining the LL method with (explicit) Runge Kutta integrators yields what...

chapter

The Study on the sEMG Signal Characteristics of Muscular Fatigue Based on the Hilbert-Huang Transform

Bo Peng, Xiaogang Jin, Yong Min, Xianchuang Su

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 140-147

Muscular fatigue refers to temporary decline of maximal power ability or contractive ability for muscle movement system. The signal of surface electromyographic signal (sEMG) can reflect the changes of muscular fatigue at certain extent. In many years, the application of signal of sEMG on evaluation muscular fatigue mainly focus on two aspects of time and frequency respectively. The new method Hilbert-Huang...

chapter

A New Approach for Solving Evolution Problems in Time-Parallel Way

Nabil R. Nassif, Noha Makhoul Karam, Yeran Soukiassian

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 148-155

With the advent of massively parallel computers with thousands of processors, a large amount of work has been done during the last decades in order to enable a more effective use of a higher number of processors, by superposing parallelism in time-domain, even though it is known that time-integration is inherently sequential, to parallelism in the space-domain [8]. Consequently, many families of predictor-corrector...

chapter

Third-Order Spectral Characterization of Termite’s Emission Track

Juan-José González de-la-Rosa, I. Lloret, Carlos G. Puntonet, A. Moreno, more

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 316-323

A higher-order frequency-domain characterization of termite activity (feeding and excavating) has been performed by means of analyzing diagonal slices of the bi-spectrum. Five sets of signals of different qualities were acquired using a high sensitivity probe-accelerometer. We conclude that it is possible to establish a third-order pattern (spectral track) associated to the termite emissions, and...

chapter

Parallel Optimization Methods Based on Direct Search

Rafael A. Trujillo Rasúa, Antonio M. Vidal, Víctor M. García

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 324-331

This paper is focused in the parallelization of Direct Search Optimization methods, which are part of the family of derivative-free methods. These methods are known to be quite slow, but are easily parallelizable, and have the advantage of achieving global convergence in some problems where standard Newton-like methods (based on derivatives) fail. These methods have been tested with the Inverse Additive...

chapter

On the Selection of a Transversal to Solve Nonlinear Systems with Interval Arithmetic

Frédéric Goualard, Christophe Jermann

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 332-339

This paper investigates the impact of the selection of a transversal on the speed of convergence of interval methods based on the nonlinear Gauss-Seidel scheme to solve nonlinear systems of equations. It is shown that, in a marked contrast with the linear case, such a selection does not speed up the computation in the general case; directions for researches on more flexible methods to select projections...

chapter

Landscape Properties and Hybrid Evolutionary Algorithm for Optimum Multiuser Detection Problem

Shaowei Wang, Qiuping Zhu, Lishan Kang

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 340-347

Optimum multiuser detection (OMD) for CDMA systems is an NP-complete problem. Fitness landscape has been proven to be very useful for understanding the behavior of combinatorial optimization algorithms and can help in predicting their performance. This paper analyzes the statistic properties of the fitness landscape of the OMD problem by performing autocorrelation analysis, fitness distance correlation...

chapter

A Parallel Solution of Hermitian Toeplitz Linear Systems,

Pedro Alonso, Miguel O. Bernabeu, Antonio M. Vidal

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms > 348-355

A parallel algorithm for solving complex hermitian Toeplitz linear systems is presented. The parallel algorithm exploits the special structure of Toeplitz matrices to obtain the solution in a quadratic asymptotical cost. Our parallel algorithm transfors the Toeplitz matrix into a Cauchy–like matrix. Working on a Cauchy–like system lets to work with real arithmetic. The parallel algorithm for the solution...

chapter

Performance Improvement of Sparse Matrix Vector Product on Vector Machines

Sunil R. Tiyyagura, Uwe Küster, Stefan Borowski

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms and New Algorithmic Approaches to Computational Kernels and Applications > 196-203

Many applications based on finite element and finite difference methods include the solution of large sparse linear systems using preconditioned iterative methods. Matrix vector multiplication is one of the key operations that has a significant impact on the performance of any iterative solver. In this paper, recent developments in sparse storage formats on vector machines are reviewed. Then, several...

chapter

A New Reconstruction Algorithm in Spline Signal Spaces

Chen Zhao, Yueting Zhuang, Honghua Gan

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms and New Algorithmic Approaches to Computational Kernels and Applications > 204-209

In this research letter, we introduce a reconstruction formula in spline signal spaces which is a generalization of former results in [11]. A general improved A-P iterative algorithm is presented. We use the algorithm to show reconstruction of signals from weighted samples and also show that the new algorithm shows better convergence than the old one. The explicit convergence rate of the algorithm...

chapter

An Implicit Riemannian Trust-Region Method for the Symmetric Generalized Eigenproblem

C. G. Baker, P. -A. Absil, K. A. Gallivan

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms and New Algorithmic Approaches to Computational Kernels and Applications > 210-217

The recently proposed Riemannian Trust-Region method can be applied to the problem of computing extreme eigenpairs of a matrix pencil, with strong global convergence and local convergence properties. This paper addresses inherent inefficiencies of an explicit trust-region mechanism. We propose a new algorithm, the Implicit Riemannian Trust-Region method for extreme eigenpair computation, which seeks...

chapter

Interval Arithmetic and Computational Science: Performance Considerations

Alistair P. Rendell, Bill Clarke, Josh Milthorpe

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms and New Algorithmic Approaches to Computational Kernels and Applications > 218-225

Interval analysis is an alternative to conventional floating-point computations that offers guaranteed error bounds. Despite this advantage, interval methods have not gained widespread use in large scale computational science applications. This paper addresses this issue from a performance perspective, comparing the performance of floating point and interval operations for some small computational...

chapter

Floating-Point Computation with Just Enough Accuracy

Hank Dietz, Bill Dieter, Randy Fisher, Kungyen Chang

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advanced Numerical Algorithms and New Algorithmic Approaches to Computational Kernels and Applications > 226-233

Most mathematical formulae are defined in terms of operations on real numbers, but computers can only operate on numeric values with finite precision and range. Using floating-point values as real numbers does not clearly identify the precision with which each value must be represented. Too little precision yields inaccurate results; too much wastes computational resources. The popularity of...

chapter

Quasi-Gaussian Particle Filtering

Yuanxin Wu, Dewen Hu, Meiping Wu, Xiaoping Hu

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advances in Parameter Estimation in Computational-Science: Strategies, Concepts, and Applications > 689-696

The recently-raised Gaussian particle filtering (GPF) introduced the idea of Bayesian sampling into Gaussian filters. This note proposes to generalize the GPF by further relaxing the Gaussian restriction on the prior probability. Allowing the non-Gaussianity of the prior probability, the generalized GPF is provably superior to the original one. Numerical results show that better performance is obtained...

chapter

Improved Sensitivity Estimate for the H ² Estimation Problem

N. D. Christov, M. Najim, E. Grivel

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advances in Parameter Estimation in Computational-Science: Strategies, Concepts, and Applications > 697-703

The paper deals with the local sensitivity analysis of the discrete-time infinite-horizon H ² estimation problem. An improved, nonlinear sensitivity estimate is derived which is less conservative than the existing, condition number based sensitivity estimates.

chapter

Constrained Optimization of the Stress Function for Multidimensional Scaling

Vydunas Saltenis

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advances in Parameter Estimation in Computational-Science: Strategies, Concepts, and Applications > 704-711

Multidimensional Scaling (MDS) requires the multimodal Stress function optimization to estimate the model parameters, i.e. the coordinates of points in a lower-dimensional space. Therefore, finding the global optimum of the Stress function is very important for applications of MDS. The main idea of this paper is replacing the difficult multimodal problem by a simpler unimodal constrained optimization...

chapter

Targeted Observations for Atmospheric Chemistry and Transport Models

Adrian Sandu

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advances in Parameter Estimation in Computational-Science: Strategies, Concepts, and Applications > 712-719

The aim of this paper is to address computational aspects of the targeted observations problem for atmospheric chemical transport models. The fundamental question being asked is where to place the observations such that, after data assimilation, the uncertainty in the resulting state is minimized. Our approach is based on reducing the system along the subspace defined by the dominant singular vectors,...

chapter

Model Optimization and Parameter Estimation with Nimrod/O

David Abramson, Tom Peachey, Andrew Lewis

Lecture Notes in Computer Science > Computational Science – ICCS 2006 > Advances in Parameter Estimation in Computational-Science: Strategies, Concepts, and Applications > 720-727

Optimization problems where the evaluation step is computationally intensive are becoming increasingly common in both engineering design and model parameter estimation. We describe a tool, Nimrod/O, that expedites the solution of such problems by performing evaluations concurrently, utilizing a range of platforms from workstations to widely distributed parallel machines. Nimrod/O offers a range of...

INFONA - science communication portal

Computational Science – ICCS 2006
6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part I

Symmetric Runge-Kutta Methods with Higher Derivatives and Quadratic Extrapolation

A Note on the Simplex Method for 2-Dimensional Second-Order Cone Programming

Local Linearization-Runge Kutta (LLRK) Methods for Solving Ordinary Differential Equations

The Study on the sEMG Signal Characteristics of Muscular Fatigue Based on the Hilbert-Huang Transform

A New Approach for Solving Evolution Problems in Time-Parallel Way

Third-Order Spectral Characterization of Termite’s Emission Track

Parallel Optimization Methods Based on Direct Search

On the Selection of a Transversal to Solve Nonlinear Systems with Interval Arithmetic

Landscape Properties and Hybrid Evolutionary Algorithm for Optimum Multiuser Detection Problem

A Parallel Solution of Hermitian Toeplitz Linear Systems,

Performance Improvement of Sparse Matrix Vector Product on Vector Machines

A New Reconstruction Algorithm in Spline Signal Spaces

An Implicit Riemannian Trust-Region Method for the Symmetric Generalized Eigenproblem

Interval Arithmetic and Computational Science: Performance Considerations

Floating-Point Computation with Just Enough Accuracy

Quasi-Gaussian Particle Filtering

Improved Sensitivity Estimate for the H ² Estimation Problem

Constrained Optimization of the Stress Function for Multidimensional Scaling

Targeted Observations for Atmospheric Chemistry and Transport Models

Model Optimization and Parameter Estimation with Nimrod/O

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Computational Science – ICCS 2006
6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part I