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数学与应用数学专业论文英文文献翻译

数学与应用数学专业论文英文文献翻译
数学与应用数学专业论文英文文献翻译

数学与应用数学专业论文英文文献翻译

Chapter 3

Interpolation

Interpolation is the process of defining a function that takes on specified values at specified points. This chapter concentrates on two closely related interpolants, the piecewise cubic spline and the shape-preserving piecewise cubic named “pchip”.

3.1 The Interpolating Polynomial

We all know that two points determine a straight line. More precisely, any two points in the plane, ),(11y x and ),(11y x , with )(21x x ≠ , determine a unique first degree polynomial in x whose graph passes through the two points. There are many different formulas for the polynomial, but they all lead to the same straight line graph.

This generalizes to more than two points. Given n points in the plane, ),(k k y x ,n k ,,2,1 =, with distinct k x ’s, there is a

unique polynomial in x of degree less than n whose graph passes through the points. It is easiest to remember that n , the number of data points, is also the number of coefficients, although some of the leading coefficients might be zero, so the degree might actually be less than 1-n . Again, there are many different formulas for the polynomial, but they all define the same function.

This polynomial is called the interpolating polynomial because it exactly re- produces the given data.

n k y x P k k ,,2,1,)( ==,

Later, we examine other polynomials, of lower degree, that only approximate the data. They are not interpolating polynomials.

The most compact representation of the interpolating polynomial is the La- grange form.

∑∏???

? ??--=≠k k k j j k j y x x x x x P )( There are n terms in the sum and 1-n terms in each product, so this expression defines a polynomial of degree at most 1-n . If )(x P is evaluated at k x x = , all the products except the k th are zero. Furthermore, the k th product is equal to one, so the sum is equal to k y and the

interpolation conditions are satisfied.

For example, consider the following data set:

x=0:3;

y=[-5 -6 -1 16];

The command

disp([x;y])

displays

0 1 2 3

-5 -6 -1 16 The Lagrangian form of the polynomial interpolating this data is

)16()

6()2)(1()1()2()3)(1()6()2()3)(2()5()6()3)(2)(1()(--+----+---+-----=x x x x x x x x x x x x x P We can see that each term is of degree three, so the entire sum has degree at most three. Because the leading term does not vanish, the degree is actually three. Moreover, if we plug in 2,1,0=x or 3, three of the terms vanish and the fourth produces the corresponding value from the data set.

Polynomials are usually not represented in their Lagrangian form. More fre- quently, they are written as something like

523--x x

The simple powers of x are called monomials and this form of a polynomial is said to be using the power form.

The coefficients of an interpolating polynomial using its power form,

n n n n c x c x c x c x P ++++=---12211)(

can, in principle, be computed by solving a system of simultaneous linear equations

?????

???????=??????????????????????????------n n n n n n n n n n n y y y c c c x x x x x x x x x 21212122212

121111111 The matrix V of this linear system is known as a Vandermonde matrix. Its elements are

j n k

j k x v -=, The columns of a Vandermonde matrix are sometimes written in the opposite order, but polynomial coefficient vectors in Matlab always have the highest power first.

The Matlab function vander generates Vandermonde matrices. For our ex- ample data set,

V = vander(x)

generates

V =

0 0 0 1

1 1 1 1

8 4 2 1

27 9 3 1

Then

c = V\y’

computes the coefficients

c =

1.0000

0.0000

-2.0000

-5.0000

In fact, the example data was generated from the polynomial 523--x x .

One of the exercises asks you to show that Vandermonde matrices are nonsin- gular if the points k x are distinct. But another one of the

exercises asks you to show that a Vandermonde matrix can be very badly conditioned. Consequently, using the power form and the Vandermonde matrix is a satisfactory technique for problems involving a few well-spaced and well-scaled data points. But as a general-purpose approach, it is dangerous.

In this chapter, we describe several Matlab functions that implement various interpolation algorithms. All of them have the calling sequence

v = interp(x,y,u)

The first two input arguments, x and y, are vectors of the same length that define the interpolating points. The third input argument, u, is a vector of points where the function is to be evaluated. The output, v, is the same length as u and has elements ))

x

terp

y

v

u

in

k

(k

(

,

,

)

(

Our first such interpolation function, polyinterp, is based on the Lagrange form. The code uses Matlab array operations to evaluate the polynomial at all the components of u simultaneously.

function v = polyinterp(x,y,u)

n = length(x);

v = zeros(size(u));

for k = 1:n

w = ones(size(u));

for j = [1:k-1 k+1:n]

w = (u-x(j))./(x(k)-x(j)).*w;

end

end

v = v + w*y(k);

To illustrate polyinterp, create a vector of densely spaced evaluation points.

论文外文文献翻译3000字左右

南京航空航天大学金城学院 毕业设计(论文)外文文献翻译 系部经济系 专业国际经济与贸易 学生姓名陈雅琼学号2011051115 指导教师邓晶职称副教授 2015年5月

Economic policy,tourism trade and productive diversification (Excerpt) Iza Lejárraga,Peter Walkenhorst The broad lesson that can be inferred from the analysis is that promoting tourism linkages with the productive capabilities of a host country is a multi-faceted approach influenced by a variety of country conditions.Among these,fixed or semi-fixed factors of production,such as land,labor,or capital,seem to have a relatively minor influence.Within the domain of natural endowments,only agricultural capital emerged as significant.This is a result that corresponds to expectations,given that foods and beverages are the primary source of demand in the tourism economy.Hence,investments in agricultural technology may foment linkages with the tourism market.It is also worth mentioning that for significant backward linkages to emerge with local agriculture,a larger scale of tourism may be important. According to the regression results,a strong tourism–agriculture nexus will not necessarily develop at a small scale of tourism demand. It appears that variables related to the entrepreneurial capital of the host economy are of notable explanatory significance.The human development index(HDI), which is used to measure a country's general level of development,is significantly and positively associated with tourism linkages.One plausible explanation for this is that international tourists,who often originate in high-income countries,may feel more comfortable and thus be inclined to consume more in a host country that has a life-style to which they can relate easily.Moreover,it is important to remember that the HDI also captures the relative achievements of countries in the level of health and education of the population.Therefore,a higher HDI reflects a healthier and more educated workforce,and thus,the quality of local entrepreneurship.Related to this point,it is important to underscore that the level of participation of women in the host economy also has a significantly positive effect on linkages.In sum, enhancing local entrepreneurial capital may expand the linkages between tourism and other sectors of the host country.

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Inventory management Inventory Control On the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion. The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility. Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored: First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments . Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field of

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吉林化工学院理学院 毕业论文外文翻译English Title(Times New Roman ,三号) 学生学号:08810219 学生姓名:袁庚文 专业班级:信息与计算科学0802 指导教师:赵瑛 职称副教授 起止日期:2012.2.27~2012.3.14 吉林化工学院 Jilin Institute of Chemical Technology

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