基于学情寻突破 优化思路促提升——以极值点偏移问题为例

以极值点偏移问题的解法探索为例,探讨了在高三解题教学中如何基于学情、基于学生错误解法进行聚类分析,并且对解法进行优化比较.通过精选三道试题加强学生对极值点偏移问题的理解,提升解题能力.     

对一类恒成立问题的思考

学生要深入理解课本中极值点、极值的定义.在不等式恒成立问题中,针对“存在区间内某点处或者区间端点处,函数值为零”的一类问题,可以用极值点、极值的知识进行解决,从而找到突破口.     

基于小波变换的高斯函数极值点及拐点的判别

利用小波变换能够表征信号特征的特性 ,选取适当小波函数 ,对 Gauss函数作小波变换 ,根据小波变换零值点和极值点来判别 Gauss函数极值点和拐点 ,根据小波变换的变化情况来判别 Gauss函数的重叠情况 .结果表明是有效的 .     

函数极值点偏移问题的两种解题策略

每年高考中,函数导数问题几乎都是我们的压轴大戏,2016年全国高考也不例外,而今年的第二问再次出现极值点偏移问题,让我们大部分的考生在考场中茫然不知所措,本文试着提供两种关于极值点偏移问题的解决方法,希望能对大家有所帮助．     

一道高考题的多角度思考

<正>近几年来,函数导数成为高考命题专家的新宠,其中对函数极值点的考查更是呈现多样化的趋势,该类题目涵盖的思想方法多,对综合能力要求高,本文试图从一道高考极值点偏移问题的多角度思考揭示出此类问题的求解策略.     

多约束二阶非线性常微分方程极值点的数值求解法

本文对科学研究和工程应用问题中常见的多约束二阶非线性常微分方程的极值点进行了讨论 ,提出了一种数值解法 .以带有化学反应项的热传导方程为例 ,给出了相应的计算结果 .     

三阶WENO-Z格式精度分析及其改进格式

首先通过理论推导给出了三阶WENO格式(WENO-JS3格式)满足收敛精度的充分条件.采用Taylor(泰勒)级数展开的方法,分析发现传统的三阶WENO-Z格式(WENO-Z3格式)在光滑流场极值点处精度降低.为了提高WENO-Z3格式在极值点处的计算精度,根据收敛精度的充分条件构造一种改进的三阶WENO-Z格式(WENO-NZ3格式),并综合权衡计算精度和计算稳定性确定所构造格式的参数.通过两个典型的精度测试,验证了WENO-NZ3格式在光滑流场极值点区域逼近三阶精度.选用Sod激波管、激波与熵波相互作用、Rayleigh-Taylor不稳定性、二维Riemann(黎曼)问题经典算例,进一步证实了本文提出的WENO-NZ3格式相较其他格式(WENO-JS3、WENOZ3、WENO-N3),不仅提高了计算精度,而且提高了对复杂流场结构的分辨率.     

非闭区间连续函数的最大(小)值问题

<正> 关于连续函数的最大值、最小值问题有两种情况是我们所熟悉的,就是闭区间连续函数和非闭区间内连续且只有唯一极值点的函数的最值问题。那么,我们自然要问,在非闭区间内连续而有若干极值点的函数的最大(小)值在什么条件下存在?若存在如何求解呢?本文就有限个极值点(在严格意义下的极值点,下同)的情况给出解决的一般方法,首先证明两个结论。     

对一道含参极值点偏移问题的再思考

本文从四个方面对文[1]中一道含参极值点偏移问题进行再思考,首先给出一种仿照文[1]中加强命题的观点所得到的在最后环节受阻而无法完成证明的解题过程,然后对文[1]中一处加强命题的结果进行纠错,之后给出文[1]中一道含参极值点偏移的变式问题以再次论述加强命题的失效,最后给出该变式问题一种备受困惑的证法,以期引起大家的讨论.     

例谈极值点偏移问题的处理方略

一个函数在某区间内存在一个极值点和两个零点,若该极值点在两个零点的中点的左侧,则称极值点左偏移;若该极值点在两个零点的中点的右侧,则称极值点右偏移.处理极值点偏移问题的常用方法是构造相应的函数,并利用函数的单调性处理.     

Signal Processing in Relaxation Experiments

Signal processing problems arising in the study of the linearly viscoelastic behavior of polymers and composites are considered. It is shown that the great amount of data conversions is associated with integral transforms using kernels which depend on the ratio or product of arguments for monotonic long-time-interval and wide-frequency-band functions (signals). A unified method of carrying out these integral transforms is developed by combining a logarithmic transformation of the signal time scale with digital filtering. For integral transforms leading to ill-conditioned inverse problems, a method of regularization is proposed based on choosing a sampling rate which ensures an acceptable error variance of the output signal. The specific features of the functional filters used for performing the functional (integral) transforms are discussed. Examples of performing the Heaviside-Carson sine transform and an inherently ill-conditioned problem of inverting the integral transform for determining the relaxation spectrum are represented by digital functional filters.     

Local sampling set conditions in weighted shift-invariant signal spaces

How to represent a continuous signal in terms of a discrete sequence is a fundamental problem in sampling theory. Most of the known results concern global sampling in shift-invariant signal spaces. But in fact, the local reconstruction from local samples is one of the most desirable properties for many applications in signal processing, e.g. for implementing real-time reconstruction numerically. However, the local reconstruction problem has not been given much attention. In this article, we find conditions on a finite sampling set X such that at least in principle a continuous signal on a finite interval is uniquely and stably determined by their sampling value on the finite sampling set X in shift-invariant signal spaces.     

A Modified DIviding RECTangles Algorithm for a Problem in Astrophysics

We present a modification of the DIRECT (DIviding RECTangles) algorithm, called DIRECT-G, to solve a box-constrained global optimization problem arising in the detection of gravitational waves emitted by coalescing binary systems of compact objects. This is a hard problem, since the objective function is highly nonlinear and expensive to evaluate, has a huge number of local extrema and unavailable derivatives. DIRECT performs a sampling of the feasible domain over a set of points that becomes dense in the limit, thus ensuring the everywhere dense convergence; however, it becomes ineffective on significant instances of the problem under consideration, because it tends to produce a uniform coverage of the feasible domain, by oversampling regions that are far from the optimal solution. DIRECT has been modified by embodying information provided by a suitable discretization of the feasible domain, based on the signal theory, which takes into account the variability of the objective function. Numerical experiments show that DIRECT-G largely outperforms DIRECT and the grid search, the latter being the reference algorithm in the astrophysics community. Furthermore, DIRECT-G is comparable with a genetic algorithm specifically developed for the problem. However, DIRECT-G inherits the convergence properties of DIRECT, whereas the genetic algorithm has no guarantee of convergence.     

Relation between total variation and persistence distance and its application in signal processing

In this paper we establish the new notion of persistence distance for discrete signals and study its main properties. The idea of persistence distance is based on recent developments in topological persistence for assessment and simplification of topological features of data sets. Particularly, we establish a close relationship between persistence distance and discrete total variation for finite signals. This relationship allows us to propose a new adaptive denoising method based on persistence that can also be regarded as a nonlinear weighted ROF model. Numerical experiments illustrate the ability of the new persistence based denoising method to preserve significant extrema of the original signal.     

Local Sampling and Reconstruction in Shift-Invariant Spaces and Their Applications in Spline Subspaces

The local reconstruction from samples is one of the most desirable properties for many applications in signal processing. Local sampling is practically useful since we need only to consider a signal on a bounded interval and computer can only process finite samples. However, the local sampling and reconstruction problem has not been given as much attention. Most of known results concern global sampling and reconstruction. There are only a few results about local sampling and reconstruction in spline subspaces. In this article, we study local sampling and reconstruction in general shift-invariant spaces and multiple generated shift-invariant spaces with compactly supported generators. Then we give several applications in spline subspaces and multiple generated spline subspaces.     

Convolution theorem for fractional cosine‐sine transform and its application

Fractional cosine transform (FRCT) and fractional sine transform (FRST), which are closely related to the fractional Fourier transform (FRFT), are useful mathematical and optical tool for signal processing. Many properties for these transforms are well investigated, but the convolution theorems are still to be determined. In this paper, we derive convolution theorems for the fractional cosine transform (FRCT) and fractional sine transform (FRST) based on the four novel convolution operations. And then, a potential application for these two transforms on designing multiplicative filter is presented. Copyright © 2016 John Wiley & Sons, Ltd.     

Saddlepoint approximations to P-values for comparison of density estimates

Summary  This article is concerned with computing approximate p-values for the maximum of the absolute difference between kernel density estimates. The approximations are based on treating the process of local extrema of the differences as a nonhomogeneous Poisson Process and estimating the corresponding local intensity function. The process of local extrema is characterized by the intensity function, which determines the rate of local extrema above a given threshold. A key idea of this article is to provide methods for more accurate estimation of the intensity function by using saddlepoint approximations for the joint density of the difference between kernel density estimates and using the first and second derivative of the difference. In this article, saddlepoint approximations are compared to gaussian approximations. Simulation results from saddlepoint approximations show consistently better agreement between empirical p-value and predetermined value with various bandwidths of kernel density estimates.     

Towards the Analysis of the simulated annealing method in the multiextremal case

There are many applied problems in which it is necessary to calculate global extrema whose number is large or even infinite. These problems include, for example, some experimental design problems, and the problem of solving large systems of equations. For a single extremum of a function of several variables, one of the commonly used numerical algorithms is the simulated annealing, which is also successfully used in high volume discrete problems (travelling salesman problem). In discrete problems, it is known that the simulated annealing method searches equal global extrema with an equal probability. The continuous case has not been investigated yet. It was assumed that equal extrema are to be found consistently, sharing their neighborhood during the computation. This method is not always effective, especially in the case when multiple extrema fill up a certain region in R ⁿ . The results obtained in this study outline a general approach to the problem. We give computational examples showing the effectiveness of the approach. It can be used to create programs, algorithms indicating the localization of the roots of large equation systems. It can also be noted that many problems of design for regression experiments have an infinite number of solutions.     

Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces

Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a high-dimensional shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. The determination of a signal in a shift-invariant space, up to a sign, by its magnitude measurements on the whole Euclidean space has been shown in the literature to be equivalent to its nonseparability. In this paper, we introduce an undirected graph associated with the signal in a shift-invariant space and use connectivity of the graph to characterize nonseparability of the signal. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that nonseparable signals in the shift-invariant space can be reconstructed in a stable way from their phaseless samples taken on that set. In this paper, we also propose a reconstruction algorithm which provides an approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robustness of the proposed algorithm to reconstruct box spline signals from their noisy phaseless samples.

     

On the limit of sampled signal extrapolation with a wavelet signal model ∗

The extrapolation of sampled signals from a given interval using a wavelet model with various sampling rates is examined in this research. We present sufficient conditions on signals and wavelet bases so that the discrete-time extrapolated signal converges to its continuous-time counterpart when the sampling rate goes to infinity. Thus, this work provides a practical procedure to implement continuous-time signal extrapolation, which is important in wideband radar and sonar signal processing, with a discrete one via carefully choosing the sampling rate and the wavelet basis. A numerical example is given to illustrate our theoretical result.