A general approach to counterexamples in numerical analysis

Summary The purpose of this paper is to indicate a unified approach to quantitative negative results in numerical analysis. This is done via a rather general theorem which in fact subsumes our previous quantitative uniform boundedness principles. The proof is based upon a gliding hump method. The general theorem is exemplarily applied to discuss the sharpness of various direct and inverse approximation results, known for the compound trapezoidal rule and for the approximate solution of the heat equation. The treatment outlines a program which may also be worked out for other procedures.Supported by Deutsche Forschungsgemeinschaft Grant No. Ne 171/5-1     

Characterization of curriculum materials to support NGSS-aligned engineering instruction in chemistry teaching

Curriculum materials can play a major role in shaping teachers’ thinking about instruction and content as well as serve as a support for teachers’ learning. With the inclusion of engineering in NGSS, many teachers may be turning to existing curriculum materials to help them infuse engineering into their science classroom, especially when they do not have the time or opportunity for professional development sessions. In this study, we identified a sample of curriculum materials freely available online to chemistry teachers trying to incorporate engineering in the topics of stoichiometry and/or energy, common topics in secondary chemistry curricula. Using qualitative coding methods, we examined what this sample had to offer the chemistry teachers in the way of developing their understanding of engineering and teaching it. Our findings indicate that within our sample there are limited existing curriculum materials to support teachers’ engineering incorporation into secondary chemistry, and the support for teachers varied in terms of content and usefulness across the materials. The materials provided procedural information for activities but lacked in supports for teacher learning and student development beyond the procedure. Implications for the enactment of NGSS in secondary science along with needs for curriculum development and teacher learning are discussed.     

Lesson study in a blended approach to support isolated teachers in teaching with technology

Lesson study (LS) is a form of professional development, with a strong foundation in mathematics education, based on teachers collaborating to design lesso     

Logical analysis of numerical data

“Logical analysis of data” (LAD) is a methodology developed since the late eighties, aimed at discovering hidden structural information in data sets. LAD was originally developed for analyzing binary data by using the theory of partially defined Boolean functions. An extension of LAD for the analysis of numerical data sets is achieved through the process of “binarization” consisting in the replacement of each numerical variable by binary “indicator” variables, each showing whether the value of the original variable is above or below a certain level. Binarization was successfully applied to the analysis of a variety of real life data sets. This paper develops the theoretical foundations of the binarization process studying the combinatorial optimization problems related to the minimization of the number of binary variables. To provide an algorithmic framework for the practical solution of such problems, we construct compact linear integer programming formulations of them. We develop polynomial time algorithms for some of these minimization problems, and prove NP-hardness of others. The authors gratefully acknowledge the partial support by the Office of Naval Research (grants N00014-92-J1375 and N00014-92-J4083).     

Using Bayesian network analysis to support centre of gravity analysis in military planning

Centre of gravity (COG) analysis is an integral and cognitively demanding aspect of military operational planning. It involves identifying the enemy and friendly COG and subsequently determining the critical vulnerabilities that have to be degraded or negated to influence the COG of each side. This paper describes a modelling framework based on the causal relationships among the critical capabilities and requirements for an operation. The framework is subsequently used as a basis for the construction, population and analysis of Bayesian networks to support a rigorous and systematic approach to COG analysis. The importance of this work is that it uses existing planning process concepts to facilitate the construction of comprehensive models in which uncertainties and subjective judgements are clearly represented, thus enabling future re-use and traceability. The visual representation of the COG causal structure helps to clarify thinking and provides a way to record and impart this thinking. Moreover, it gives planners the capability to perform impact analysis, that is, to determine which actions are most likely to achieve a desirable end-state. The paper discusses the methodology, development and implementation of the COG Network Effects Tool (COGNET) suite for model population and model checking as well as impact analysis.     

Finite dimensional potential theory applied to numerical analysis of linear systems

It is shown how to use finite dimensional potential theory in the numerical analysis of certain iterative methods for solving differential equations.     

Mathematical modelling and the learning trajectory: tools to support the teaching of linear algebra

In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and span. The objective is to evaluate this didactic proposal by determining the level of match between the hypothetical learning trajectory (HLT) designed in this study with the actual learning trajectory in the second experimental cycle of an investigation design-based research more extensive. The results show a high level of match between the trajectories in more than half of the conjectures, which gives evidence that the HLT has supported, in many cases, the achievement of the learning objective, and that additionally mathematical modelling contributes to the construction of these linear algebra concepts.     

Application of half-derivatives in numerical analysis

Generalized concepts of the Lipschitz constant and the divided difference are used to develop a technique for analyzing numerical methods. Based on the technique, new results are obtained concerning error estimation for a nonlinear equation in a Banach space.     

The double-exponential transformation in numerical analysis

The double-exponential transformation was first proposed by Takahasi and Mori in 1974 for the efficient evaluation of integrals of an analytic function with end-point singularity. Afterwards, this transformation was improved for the evaluation of oscillatory functions like Fourier integrals. Recently, it turned out that the double-exponential transformation is useful not only for numerical integration but also for various kinds of Sinc numerical methods. The purpose of the present paper is to review the double-exponential transformation in numerical integration and in a variety of Sinc numerical methods.     

时间效益分析法在教学质量评价中的应用

文章在回顾教学质量评价方法的基础上 ,介绍了时间效益分析方法的基本原理并应用此方法 ,利用同一地区两所学校 95年中考成绩和 98年高考成绩对两所学校的教学质量进行了分析比较。文章最后还采用齐次马尔可夫链分析法和协方差分析方法对这两所学校的教学质量进行了评价 ,三种方法得出了相同的结论 ,从而验证了时间效益分析方法的可行性     

Positioning analysis using knowledge-based support

We present knowledge-based support for positioning analysis applications. It is shown how knowledge about user wishes, positioning analysis objectives, and the input/output behavior of methods can be combined in order to provide substantial support for the analysis of survey data. We describe how such knowledge is represented and processed in the knowledge-based marketing data analysis system WIMDAS-PS (WIssensbasiertes Marketing-DatenAnalyse-System zur Positionierungs-und Segmentierungsanalyse) and use a sample consultation session for demonstration purposes.     

Variational differential quadrature: A technique to simplify numerical analysis of structures

A new solution method in the area of computational mechanics is developed in this article, which is called variational differential quadrature (VDQ). The main idea of this method is based on the accurate and direct discretization of the energy functional in the structural mechanics. In the VDQ method, through developing an efficient matrix formulation and using an accurate integral operator, the discretized governing equations are derived directly from the weak form of the equations with no need for the analytical derivation of the strong form. This technique provides an alternative way to discretize the energy functional, which avoids the local interpolation and the assembly process of the methods of this kind. We first implement the VDQ method for the nonlinear elasticity theory considering the Green-St. Venant strain tensor; then we simplify the formulation further for the first-order shear deformable beam and plate theories. The final formulation of these cases demonstrates the simplicity of the implementation for the VDQ method in the numerical analysis of the structures, which is a major goal for this article. Using these examples, one can easily learn and apply this technique to other structures. To assess the performance of the VDQ method, we compare it with the generalized differential quadrature (GDQ) method and finite element method (FEM) in the case of bending analysis of Mindlin plates. It is indicated that computational cost of VDQ is less than that of GDQ, and the convergence rate of VDQ is faster than that of FEM.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

On average case errors in numerical analysis

The error of a numerical method may be much smaller for most instances than for the worst case. Also, two numerical methods may have the same maximal error although one of them usually is much better than the other. Such statements can be made precise by concepts from average case analysis. We give some examples where such an average case analysis seems to be more sensible than a worst case analysis.     

Homotopy analysis method to obtain numerical solutions of the Painlevé equations

In this paper, the homotopy analysis method (HAM) is presented to obtain the numerical solutions for the two kinds of the Painlevé equations with a number of initial conditions. Then, a numerical evaluation and comparison with the results obtained via the HAM are included. It illustrates the validity and the great potential of the HAM in solving Painlevé equations. Although the HAM contains the auxiliary parameter, the convergence region of the series solution can be controlled in a simple way. Copyright © 2012 John Wiley & Sons, Ltd.     

Non-uniform robust global asymptotic stability for discrete-time systems and applications to numerical analysis

The notion of non-uniform Robust Global Asymptotic Stability(RGAS) presented in this paper generalizes the notion of non-uniformin time RGAS for finite- or infinite-dimensional discrete-timesystems. Lyapunov characterizations for this stability notionare provided. The results are applied to finite-dimensionaldiscrete-time systems obtained by time discretization of continuous-timesystems by the explicit Euler method.     

Some continuous programming problems in numerical analysis

Many problems in the design and implementation of computational schemes may be studied using the theory and methods of mathematical programming. One seeks to minimize bounds for the errors in the calculated results obtained from a given set of input data, exploiting analytical relations. We describe optimal quadrature rules and give an application to the evaluation of the sums of power series, belonging to an important class. We present results which are based on the theory of linear and semi-infinite programming. We also study the associated complexity issues and obtain simple qualitative results for the computational work required.     

Stochastic preference analysis in numerical preference relations

Numerical preference relations (NPRs) consisting of numerical judgments can be considered as a general form of the existing preference relations, such as multiplicative preference relations (MPRs), fuzzy preference relations (FPRs), interval MPRs (IV-MPRs) and interval FPRs (IV-FPRs). On the basis of NPRs, we develop a stochastic preference analysis (SPA) method to aid the decision makers (DMs) in decision making. The numerical judgments in NPRs can also be characterized by different probability distributions in accordance with practice. By exploring the judgment space of NPRs, SPA produces several outcomes including the rank acceptability index, the expected priority vector, the expected rank and the confidence factor. The outcomes are obtained by Monte Carlo simulation with at least 95% confidence degree. Based on the outcomes, the DMs can choose some of them which they find most useful to make reliable decisions.