数值分析英文版课件 3

合集下载

数值分析全册完整课件

数值分析全册完整课件
0
解: 将 ex2 作Taylor展开后再积分
1 eБайду номын сангаас x2 dx
1
(1
x2
x4
x6
x8
... ) dx
0
0
2 ! 3! 4!
1 1 1 1 1 1 1 1 ... 3 2! 5 3! 7 4! 9
S4
R4
取 1 e
x
2
dx
0
S4
,

R4
1 1 4! 9
1 1 5! 11
...
值班军官对连长: 根据营长的命令,明晚8点哈雷彗星将 在操场上空出现。如果下雨的话,就让士兵穿着野战服列 队前往礼堂,这一罕见的现象将在那里出现。
连长对排长: 根据营长的命令,明晚8点,非凡的哈雷彗 星将身穿野战服在礼堂中出现。如果操场上下雨,营长将 下达另一个命令,这种命令每隔76年才会出现一次。
1.由实际问题应用有关知识和数学理论建立模型, -----应用数学任务
2.由数学模型提出求解的数值计算方法直到编程出结果, -----计算数学任务
计算方法是计算数学的一个主要部分,研究的即是后半 部分,将理论与计算相结合。
特点:
面向计算机,提供切实可行的算法; 有可靠的理论分析,能达到精度要求,保证近
计算方法
数值分析全册完整课件
教材和参考书
教材:
数值分析,电子科技大学应用数学学院,钟尔杰, 黄廷祝主编,高等教育出版社
参考书:
数值方法(MATLAB版)(第三版),John H. Mathews,Kurtis D. Fink 著,电子工业出版社;
数值分析(第四版),李庆扬,王能超,易大义编,清华 大学出版社;

数值分析学习课件

数值分析学习课件

§2.正交多项式
性质3. n次多项式 P (x)有n个互异实根,且全部(a, b)内。 n 性质4.设 P (x)的n个实根为x1 , x2 ,..., xn P + 1 (x) 的n+1 ,n n 个实根为 x1 , x2 ,..., xn1 ,则有
a x1 x1 x 2 x2 ...
{ j(x) = e kj x , ki kj } 对应指数多项式 /* exponential
polynomial */
§1.函数逼近的基本概念
定义 权函数:

离散型 /*discrete type */
根据一系列离散点 ( xi , yi ) (i 1, ... , n) 拟合时,在每一误
Pk(x)
kl kl
由 P0 1, P1 x 有递推 (k 1) Pk 1 (2k 1) xP kPk 1 k
k
0
1
2 3
P0 ( x) 1 P ( x) x 1
P2 ( x ) =
4
1 P3 ( x ) = (5 x3 - 3x) 2 1 P4 ( x ) = (35 x 4 - 30 x 2 + 3) 8
第三章
函数逼近
/* Approximation Theory */
第一讲
§1.函数逼近的基本概念
§2.正交多项式
§1.函数逼近的基本概念
已知 x1 … xm ; y1 … ym, 求一个简单易算的近 m 似函数 P(x) f(x) 使得 | P ( xi ) yi |2 最小。
i 1
已知 [a, b]上定义的 f(x),求一个简单易算的 b 近似函数 P(x) 使得 a [ P( x) f ( x)]2 dx 最小。

数值分析英文课件

数值分析英文课件
ˆ ∆y = y − y = 1.4 − 1.41421L ≈ 0.0142
or relative forward error of about 1 percent. Since 1.96 = 1.4 , the absolute backward error is
ˆ ∆x = x − x = 1.96 − 2 = 0.04
Computational error = Truncation error + rounding error
• Propagated (传播) vs. computational error 传播)
– x = exact value, – f = exact function,
ˆ x = approx. value ˆ f = its approximation
Backward vs. forward errors
Suppose we want to compute y = f ( x ) , where f : ℜ → ℜ ˆ but obtain approximate value y
Forward Error:
ˆ ˆ ∆y = y − y = f ( x ) − f ( x )
Example of Ill-Posed Problem
x 1 x 1 x 11 1 + 2 + 3 = 2 3 6 1 1 1 13 x1 + x2 + x3 = 3 4 12 2 1 x1 + 1 x2 + 1 x3 = 47 3 4 5 60
2 significant digits rounding
• Problems that are not well-posed are ill-posed.

数 值 分 析Numerical Analysis

数 值 分 析Numerical  Analysis
Your C or C++ file must be named as “yourID_problem#.c” (or .cpp). For example, “98115001_03.c” is considered to be the program for solving problem 3 and the author is the student with ID 98115001.
Time Limit Exceeded: Your program tried to run during too much time. This error does not allow you to know if your program would reach the correct solution to the problem.
15
Laboratory Grade (30) = Lab ( i ) i 1
Numerical Analysis Laboratory Projects
1. Input and Output Your program must read from a file “in.txt” (if there is
Accepted: OK! Your program is correct! You will obtain 2 points for correctly solving one problem.
Presentation Error: Your program outputs are correct, but are not presented in the correct way. Check for spaces, justify line feeds...

数值分析学习课件

数值分析学习课件

Ak =
∫ ∏
xn x0 i≠k
n 0
=∫
(t − i ) h (b − a )( − 1) n − k ∏ (k − i ) h × h dt = n k !( n − k )! i≠k
( x − xi ) dx ( x k − xi )

x =a+th
∫ ∏ (t − i )dt
n 0 i≠k
注:Cotes 系数仅取决于 n 和 k, , 可查表得到。 可查表得到。与 f (x) 及区 均无关。 间[a, b]均无关。 均无关
2
n
机械求积
∫ f ( x ) dx ≈ ∑ A f ( x )
a k =0 k k
注:机械求积是将积分求值问题归结为函数值的计算。 机械求积是将积分求值问题归结为函数值的计算。
1.2 代数精度
如果某个求积公式对于次数不超过m的多项式均能 如果某个求积公式对于次数不超过 的多项式均能 准确成立,但对于m+1次多项式就不准确成立,则 次多项式就不准确成立, 准确成立,但对于 次多项式就不准确成立 称该求积公式具有m次代数精度 次代数精度。 称该求积公式具有 次代数精度。 例如:梯形公式和矩形公式都具有 次代数精度 次代数精度。 例如:梯形公式和矩形公式都具有1次代数精度。 一般,若要使得求积公式具有m次代数精度,只要令 一般, 次代数精度, 2 m 都能准确成立, 它对于 f ( x ) = 1, x, x ,L , x 都能准确成立,即
∫ f ( x ) dx = f (ξ )( b − a )
b a
1.1 数值积分的基本思想
思 只要对平均高度 提供一种算法, f (ξ ) 提供一种算法,相应地便获 路 得一种数值求积的方法。 得一种数值求积的方法。

数值分析英文版课件 (6)

数值分析英文版课件 (6)

12
4.1 Matrix Algebra (III)
The matrix A is denoted as:
1.1 −0.12 3.0 6.2 0.0 0.15 0.6 −4.0 1.3 2.1 8.2 9.3 then a12 = 1.1, or A( 3,2 ) = −4.0 , or ( A )43 = 8.2 .
10
4.1 Matrix Algebra (I)
A matrix is a rectangular array of numbers such as
1.1 −0.12 3.0 6.2 0.0 0.15 , 0.6 −4.0 1.3 9.3 2.1 8.2 3.2 −17 , −4.7 0.11
18
4.1 Matrix Algebra (IX)
Theorem 1 (on equivalent systems):
If one system of equations is obtained from another by a finite sequence elementary operations, then the two systems are equivalent .
3
INDEX
4.0 Introduction 4.1Matrix Algebra 4.2 LU and Cholesky Factorizations
4
INDEX
4.0 Introduction 4.1Matrix Algebra 4.2 LU and Cholesky Factorizations
p
(1 ≤ i ≤ix Algebra (VII)

数值分析课件-num_3.4超松弛迭代法

数值分析课件-num_3.4超松弛迭代法

第三章 线性方程组迭代解法§ 3.4 超松弛迭代法(SOR)一、SOR法迭代公式设线性方程组AX=b≠ 0(i=1,2,⋅⋅⋅⋅⋅⋅,n )。

其中A非奇异,且aii如果已经得到第k次迭代量x (k)及第k+1次迭代量x (k+1)的前i-1个分量(x(k+1),x2 (k+1) ,⋅⋅⋅⋅⋅⋅,x i-1 (k+1) ),1在计算x(k+1)时,先用Gauss-Seidel迭代法得到i(1)选择参数ω,取(2)把 式(1)代入式(2)可以综合写成:即得超松弛法或逐次超松弛迭代法(Successive Over-Relaxation Method),简称SOR法。

或可表示成增量的形式:其中,参数ω叫做松弛因子;若ω=1,它就是Gauss-Seidel迭代法。

令A=D-L-U, SOR 法(2)式可写成:(1)1()1)[(1)]()k k XL D U XD L bωωωωω+--=-++-(D -再整理成:于是可导出SOR 法的矩阵形式:(1)()k k XB Xfω+=+其中,迭代矩阵和f 为:11)[(1)]()B L D U f D L bωωωωωω--⎧=-+⎪⎨=-⎪⎩(D -例3.6 用SOR法求解线性方程组解方程组的精确解为x=(3,4,-5)T,为了进行比较,利用同一初值x(0)=(1,1,1)T,分别取ω=1 (即Gauss-Seidel迭代法)和ω=1.25两组算式同时求解方程组。

①取ω=1 ,即Gauss-Seidel迭代:②取ω=1.25 ,即SOR迭代法:迭代结果见表3.3。

表3.3 Gauss-Seidel迭代法与SOR迭代法比较Gauss-Seidel迭代法SOR迭代法(ω=1.25)k x1x2x3x1x2x30 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.00000001 5.2500000 3.1825000-5.0468750 6.3125000 3.9195313-6.65014652 3.1406250 3.8828125-5.0292969 2.6223145 3.9585266-4.60042383 3.0878906 3.9267587-5.0183105 3.1333027 4.0402646-5.09668634 3.0549316 3.9542236-5.0114410 2.9570512 4.0074838-4.97348975 3.0343323 3.9713898-5.0071526 3.0037211 4.0029250-5.00571356 3.0214577 3.9821186-5.0044703 2.9963276 4.0009262-4.99828227 3.0134110 3.9888241-5.0027940 3.0000498 4.0002586-5.0003486迭代法若要精确到七位小数,◆ Gauss-Seidel迭代法需要34次迭代;◆而用SOR迭代法(ω=1.25),只需要14次迭代。

数值分析(浙江大学)全套课件

数值分析(浙江大学)全套课件
➢ Numerical Analysis (Seventh Edition)
数值分析 (第七版 影印版)
Richard L. Burden & J. Douglas Faires (高等教育出版社)
ห้องสมุดไป่ตู้ 学习方法
1.注意掌握各种方法的基本原理 2.注意各种方法的构造手法 3.重视各种方法的误差分析 4.做一定量的习题 5.注意与实际问题相联系
教材 (Text Book) 数值计算方法 郑慧娆等 编著 (武汉大学出版社)
参考书目 (Reference)
➢ Numerical Analysis:Mathematics of Scientific Computing (Third Edition)
数值分析 (英文版 第3版 )
David Kincaid & Ward Cheney(机械工业出版社)
10
n
0
1
102
0
10 1 101 0
2。与计算机不能分离:上机实习(掌握一 门语言:C语言,会用Matlab)
1.2 误差 ( Error )
§1 误差的背景介绍 ( Introduction ) 1. 来源与分类 ( Source & Classification ) 模型误差 ( Modeling Error ): 从实际问题中抽象出数 学模型
1 e x2 dx 0
(第七章的内容:数值积分)
数值分析的特点
1。近似: 由此产生“误差”
在计算数学和应用数学中一个有趣的问题: 什么是零?
1 10 1 10
原点附近
1
在纯数学中,认为此矩阵为满秩矩阵
10 1
但在计算数学中,它却是降秩矩阵 ?

数值分析学习课件

数值分析学习课件
t 0 = cos
n= 4
3π 5π 7π 9π , t 2 = cos , t 3 = cos , t 4 = cos 10 10 10 10 10 a+b b−a 1 x= t = ( t + 1) + 2 2 2 1 π 1 3π x0 = (cos + 1) ≈ 0.98 , x1 = (cos + 1) ≈ 0.79 2 10 2 10 1 5π 1 7π x2 = (cos + 1) ≈ 0.50 , x3 = (cos + 1) ≈ 0.21 2 10 2 10 1 9π x4 = (cos + 1) ≈ 0.02 为节点作L 以 x0, …, x4 为节点作 4(x) 2 10 , t1 = cos
Take it easy. It’s very Didn’t you say it’s anot so difficult if we consider difficult problem? polynomials only.
§1.最佳一致逼近 1.最佳一致逼近
最佳一致逼近多项式 /* optimal uniform approximating polynomial */ 的构造:求 n 阶多项式 Pn(x) 使得 || Pn − y ||∞ 最 的构造: 小。
第二讲
§1.最佳一致逼近 1.最佳一致逼近
§1.最佳一致逼近 1.最佳一致逼近
偏差
最佳一致逼近 最佳一致逼近 /* uniform approximation*/
意义下, 最小。 在 || f ||∞ = max | f ( x ) | 意义下,使得 || P − y ||∞ 最小。也称 为minimax problem。 。 偏差点。 若 P ( x0 ) − y( x0 ) = ± || P − y ||∞ ,则称 x0 为± 偏差点。

数值分析session3

数值分析session3

from second, and then we subtract 1/2 times the first equation from
third. Finally, we subtract 1 times the first equation from the fourth.
The numbers 2, 1/2 and 1 are called the multipliers for the first step
4.2. LU Factorization:
Easy to Solve System, LU Factorization, LDU Factorization, Doolittle Factorization, Crout’s Factorization, Cholesky Factorization.
in a unit triangular matrix L (li j )
1 0 0 0
L
1
2 /2
1 3
0 0 1 0
(5)
1 1 / 2 2 1
Notice that each multiplier is written in the location corresponding
x2
10
(3)
0
0
2
5
x3
9
0 0 4 13 x4 21
The final step consists of subtracting 2 times the third row from
the fourth so that 2 is both the multiplier and the pivot element.
This system is upper triangular and equivalent to the origional

数值分析(英文版)

数值分析(英文版)

341
Note that to find f 6 exactly, we only need the value of the function and all its derivatives at some other point, x4 in this case
2013-12-9 13
h2 h3 h4 h5 f x h f x f x h f x f x f x f x 2! 3! 4 5 h2 h3 h4 h5 f 0 h f 0 f 0h f 0 f 0 f 0 f 0 2! 3! 4 5
h2 h3 f x h f x f x h f x f x 2! 3! x4 h 64 2
2013-12-9
12
Example (cont.)
Solution: (cont.) Since the higher order derivatives are zero,
x2 x4 x6 cos(x) 1 2! 4! 6!
x3 x5 x7 sin(x) x 3! 5! 7!
x2 x3 e 1 x 2! 3!
x
2013-12-9
10
General Taylor Series
The general form of the Taylor series is given by f x 2 f x 3 f x h f x f x h h h
22 23 f 4 2 f 4 f 42 f 4 f 4 2! 3! 2 2 23 f 6 125 742 30 6 2! 3!

数值分析英文版课件 3

数值分析英文版课件 3

6
3的根x*的近似解序列。
1 bn an n1 (b a) 2

而xn是[an,bn]的中点,所以有
1 1 | xn x | (bn an ) n (b a) 2 2
*
n
lim xn x*
7
3.2.1 二分法 (3)

为求解方程 f(x)=0 的根 x*,假设

有一个近似值 xk ≈ x* f ’’存在且连续

因 f (x*)=0, 则:
'' f ( ) ' f ( x ) f ( xk ) f ( xk )( x xk ) ( x x k )2 2 若 f ' (x*) ≠0,
今日主题

第三章:非线性方程的数值解法



3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法
1
今日主题

第三章:非线性方程的数值解法



3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法
( x x* ) g ( x ) m ( x) mg ( x) ( x x* ) g '( x)

所以x*是方程 m(x)=0 的单根
33
3.5.2 Newton 法的重根情形 (5)

应用Newton法,迭代函数为:
m ( x) f ( x) f '( x) ( x) x x ' m '( x) [ f ( x)]2 f ( x) f ''( x)

数值分析

数值分析

1.1 Introduction to numerical analysisNumerical analysis is the main part of Computional Methods. It involves the study, development, and analysis of algorithms for obtaining numerical solutions to various mathematical problems.For example:Growth of a Population An Exponential Model is as follows:Let denote the number at time t and denote the constant birth rate of thepopulation.Suppose that immigration is permitted at a constant rate , then the population satisfiesthe differentialequationWhose solution iswhere denotes the initial population.Suppose a certain population contains 1,000,000 individuals initially, that 435,000 individuals immigrate into the community in the first year, and that 1,564,000 individuals are present at the end of the one year.To determine the birth rate of this population, we must solve the equationof which the exact solution cannot be obtained by algebraic methods.This problem is equivalent to finding a solution of an equation of the form .We will discuss how to solve this problem in Chapter 2. Textbook (not necessarily to have):Numerical Analysis (Seventh Edition) 数值分析 (第七版 影印版)Richard L. Burden & J. Douglas Faires (高等教育出版社) 1.2 Roundoff Errors and Computer ArithmeticThe arithmetic performed by a calculator or computer is different from the arithmetic in our algebra and calculus courses.In our traditional math world, we permit numbers with an infinite number of digits. For exampleComputers use floating point numbers which have 3 parts: ● Sign● Characteristic● MantissaFor the 64-bit (binary digit) representation, a machine number looks like0 10000000011 10111001000100000 (00000000000)The first bit is a sign indicator, denoted s.Followed by an 11-bit component, c , called the characteristic.And a 52-bit binary fraction, f , called the mantissa. υλ+=)(d )(d t N t t N )(t N λv )1()(0-+=t t e e N t N λλλν0N )1(000,435000,000,1000,564,1-+=λλλe e 0)(=x f ()3332844222==⋅=+)76.4,93.1(),(),24.3,31.1(),(1100==y x y xUsing this system,we give a floating-point number of the formThe smallest positive number that can be represented hasNumbers occurring in calculations that have a magnitude less than this value results in underflow and are generally set to zero.The largest positive number that can be represented hasNumbers occurring in calculations that have a magnitude greater than this value results overflow and typically cause the computation to stop.Round-off ErrorsThe error is produced when a calculator or computer is used to perform real-number calculations. k-digit decimal machine numbers: machine numbers that are in the following normalized decimal floating point formAny positive real number within the numerical range of the machine can be normalized to theformThe floating-point form of y, denoted fl(y ), is obtained by terminating the mantissa of y at kdecimal digits ,There are two ways of performing this termination.• Chopping• RoundingIf is an approximation to , then• The absolute error is• The relative error is provided thatFor exampleConclusions:• The example shows that the same relative error)1(2)1(1023f c s +--4096156212311618121 2112112112112112111027121024 212120202101285431012910+++++=⎪⎭⎫ ⎝⎛⋅+⎪⎭⎫ ⎝⎛⋅+⎪⎭⎫ ⎝⎛⋅+⎪⎭⎫ ⎝⎛⋅+⎪⎭⎫ ⎝⎛⋅+⎪⎭⎫ ⎝⎛⋅==++=⋅+⋅+⋅++⋅+⋅==f c s 0,1,0===f c s 307102310102225.0)01(2)1(--⨯≈+-5221,2047,0--===f c s 309521023204701017977.0)211(2)1(⨯≈-+---.,,3,290 91,10.0121k i d d d d d i n k =≤≤≤≤⨯±,,.10.02121n k k k d d d d d y ⨯=++ .10.0)(fl 21n k d d d y ⨯=1030.333 is error relative and 10 0.1 is error absolute 103100.0,103000.0)c 1030.333 is error relative and 10 0.1 is error absolute 103100.0,103000.0)b 1030.333 is error relative and 0.1 is errorabsolute 103100.0,103000.0)a 1-34*41-4-3*31-1*1⨯⨯⨯=⨯=⨯⨯⨯=⨯=⨯⨯=⨯=--p p p p p p ,103333.01-⨯*p p .*p p -,/*p p p -.0≠poccurs for widely varying absolute errors. • As a measure of accuracy, the absolute error can be misleading and the relative error more meaningful since the relative error takes into consideration the size of the value. Significant Digits (有效数字) Definition: The number is said to approximate p to t significant digits if t is the largest nonnegative integer for whichExamples 1Real value: 10/3Approximate value: 3.3333Absolute error: 1/30000Relative error: 1/100000Number of significant digits = 5Examples 2Approximate values x 1=1.73, x 2=1.7321, x 3=1.7320, determine the digits of significance.Computer ArithmaticIn addition to inaccurate representation of numbers, the arithmetic performed in a computer is not exact.For exampleCalculations which produce the erros are given as follows:1. One of the most common error-producing calculations involves the cancellation of significantdigits due to the substracton of nearly equal numbers.So, we should avoid subtraction of nearly equal numbers!!2. If a finite-digit representation or calculation introduces an error, further enlargement of theerror occurs when divided by a number with small magtitude (or, equivalently, when multiplying by a number with large magnitude).So, we should avoid division by a small number!))()(( ))()(())()(( ))()((y fl x fl fl y x y fl x fl fl y x y fl x fl fl y x y fl x fl fl y x ÷=÷⨯=⊗-=+=⊕52210500005.03-⨯=≈-=x e 433105000051.03-⨯<≈-=x e 3 digits3111050021.03-⨯<≈-=x e 4 digits5 digits t *p tp p p -⨯≤-105 :error relative the *...7320508.13==x 8163972286.0101732.0101414.03211=⨯⨯=How to reduce the roundoff error?1. The loss of accuracy due to roundoff error can ofen be avoided by a reformulation of the problem.2. Accuracy loss dute to roundoff error can also be reduced by rearranging calculations. Oneway to reduce roundoff error is to reduce the number of error-producing computations.Homeworks.⏹ Page 27 Q. 4Perform the following computations(i)exactly,(ii)using three-digit chopping arithmetic,(iii)using thrree-digit rounding arithmetic.(iv)Compute the relative errors in parts(ii)and (iii).⏹ Page 28 Q. 15Use the 64-bit long real format to find the decimalequivalent of the following floating-pint machine number.a. 0 10000001010 10010011000000 (000)⏹ Page 28 Q. 17Suppose two points andare on a straight line with Two formulas are available to find the x-intercept of the line:a. Show that both formulas are algebraically correct.b. Use the data and three-digit rounding arithmetic to compute the x-intercept both ways. Which method is better and why1.3 Algorithm and convergenceAn algorithm is a procedure that describes, in an umambiguous manner, a finite sequence of steps to be performed in a specified order.The object of algorithm is to implement a procedure to solve a problem or approximate a solution to the problem.We use a pseudocode to describe the algorithm.This pseudocode specifies the form of the inpute to be supplied and the form of the desired output. Not all numerical procedures give satisfactory output for arbitrary chosen input.As a consequence, a stopping technique independent of numerical technique is incorporated into each algorithm to avoid infinite loops.The steps in the algorithms follow the rules of structured program consruction. They have been arranged so that there should be minimal difficulty translating pseudocode into any programming language suitable for scientific applications.Since iterative techniques involving sequences are often used, now we concludes with a brief 20311331c.+⎪⎭⎫ ⎝⎛-)76.4,93.1(),(),24.3,31.1(),(1100==y x y x 010010010110)(y y y x x x x and y y y x y x x ---=--=),(00y x ),(11y x 01y y ≠discussion of some terminology used to describe the rate at which convergence occurs when employing a numerical techniques. In general, we would like the technique to converge as rapidly as possible.Although this definiton permites {}∞=1n n α to be compared with an arbitrary seqence {}∞=1n n β, in nearly every situation we use p n n 1=βfor some numberp>0. We are generally interested in the largest value of p with )(n n O βαα+=.The following definitons describes the rate at which functions converge.The functions we use for comparision generally have the form p h h G =)(,where p>0. We are interested in the largest value of p with )(F(h)p h O L +=.{}{}{}).().(,arg ,tan ,111n n n n n n n n n n n O writing by indicated is This O e convergenc of rate with to converges that say we then n e l for K with exitst K t cons positive a If to converges and zero to known sequence a is Suppse Definition βααβααβααααβ+=≤-∞=∞=∞=)).((F(h) write ,,)()(tan )(lim 0)(lim 0h 0h h G O L we then h small tly suffficien for h G K L h F with exitst K t cons positive a If L h F and h G that Suppse Definition +=≤-==→→。

数值分析全套课件

数值分析全套课件

Ln n si n

ˆ L2n (4L2n Ln ) / 3
n L error 192 3.1414524 1.4e-004 384 3.1415576 3.5e-005 3.1415926 4.6e-010
3/16
通信卫星覆盖地球面积
将地球考虑成一 个球体, 设R为地 球半径,h为卫星 高度,D为覆盖面 在切痕平面上的 投影(积分区域)
( x1 x2 ) | x1 | ( x2 ) | x2 | ( x1 )
15/16
例3.二次方程 x2 – 16 x + 1 = 0, 取
求 x1 8 63 使具有4位有效数
63 7.937
解:直接计算 x1≈8 – 7.937 = 0.063
( x1 ) (8) (7.937) 0.0005
5/16
误差的有关概念
假设某一数据的准确值为 x*,其近似值 为 x,则称
e(x)= x - x*
为 x 的绝对误差 而称
e( x) x x er ( x ) , x x
*
( x 0)

为 x 的相对误差
6/16
如果存在一个适当小的正数ε

,使得
e( x) x x
计算出的x1 具有两位有效数
1 0.062747 修改算法 x1 8 63 15.937 4位有效数 (15.937) 0.0005 ( x1 ) 0.000005 2 2 (15.937) (15.937)
16/16
1
参考文献
[1]李庆扬 关治 白峰杉, 数值计算原理(清华) [2]蔡大用 白峰杉, 现代科学计算 [3]蔡大用, 数值分析与实验学习指导 [4]孙志忠,计算方法典型例题分析 [5]车刚明等, 数值分析典型题解析(西北工大) [6]David Kincaid,数值分析(第三版) [7] John H. Mathews,数值方法(MATLAB版)

4.3 4.4 (英文)

4.3  4.4 (英文)
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x→x
(a) Since the iterative function of Newton’s method is f (x) (x − x∗)q(x) ϕ(x) = x − =x− , ∗ )q (x) f (x) mq(x) + (x − x we have q(x) ϕ (x) = 1 − mq(x) + (x − x∗)q (x) −(x − x∗) Then q(x) . mq(x) + (x − x∗)q (x)
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(4.3.6)
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is the iterative function.
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4.3.3 Newton’s Downhill Method
Definition 4.3.4 Newton’s downhill method is an iterative technique of the form f (xk−1) xk = xk−1 − λ , for k = 1, 2, · · · , (4.3.7) f (xk−1) where the constant λ is called the downhill factor, which is selected such that |f (xk )| < |f (xk−1)|, for k = 1, 2, · · · . (4.3.8)
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converges linearly to x∗; but (b) the sequence {xk } defined by the modified Newton’s method f (xk−1) , for k = 1, 2, · · · , (4.3.11) xk = xk−1 − m f (xk−1) converges at least quadratically to x∗.

数值分析 CHAPTER3

数值分析 CHAPTER3

▲ 他把严格的论证引进分析学,建立了实数理论,引进了 现今分析学上通用的ε-δ定义,奠基了分析学的算术化。
▲ 在变分法中,给出了带有参数的函数的变分结构,研究
了变分问题的间断解。 ▲ 在微分几何中,研究了测地线和最小曲面; ▲ 在线性代数中,建立了初等因子理论,并用来简化矩阵。 ▲ 魏尔斯特拉斯一生中培养了很多有成就的学生,其中著
L2 ,1 ( x )
( x x 0 )( x x 2 ) ( x1 x 0 )( x1 x 2 )
L2 , 2 ( x )
( x x 0 )( x x1 ) ( x 2 x 0 )( x 2 x1 )
(3) The nth Lagrange interpolating polpolynomial P ( x ) of degree at most n that passes through the n+1 given points ( x0 , f ( x0 )) , ( x1 , f ( x1 )) , , ( xn , f ( xn )) .
The coefficient determinant of the system is
1 1 1 x0 x1 xn x0 x1 xn
2 2
x0 x1 xn
n n


2 n
0.
Vandemonde determinant
3.1 Interpolation and Lagrange Polynomial
f ( 3) P ( 3) 0.325
The truncation error
Ln ,n ( x )
Ln ,1 ( x )
( x x 0 )( x x 2 )( x x n ) ( x1 x 0 )( x1 x 2 )( x1 x n )
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3.5.2 Newton 法的重根情形 (2)
g ( x) ( x x ) g '( x) '( x) 1 mg ( x) ( x x ) g '( x) 1 ( x x ) g ( x)( )' mg ( x) ( x x ) g '( x)
( x x* ) g ( x ) m ( x) mg ( x) ( x x* ) g '( x)

所以x*是方程 m(x)=0 的单根
33
3.5.2 Newton 法的重根情形 (5)

应用Newton法,迭代函数为:
m ( x) f ( x) f '( x) ( x) x x ' m '( x) [ f ( x)]2 f ( x) f ''( x)
即Newton迭代法的迭代公式
22
3.5.1 Newton迭代法公式和收敛性 (5)

Newton迭代法的几何解释:



求 x* 就是求曲线 y=f(x) 与x轴的交点。 在曲线 y=f(x) 上的点(xk,f(xk))上作曲线的切线, 切线方程为 y-f(xk)=f '(xk)(x-xk), 切线与x轴交点的横坐标就是 xk+1, 把它作为 x*新 的近似。 可以证明Newton迭代法是超线性收敛的。

可验证 f (x*) = x* , f (x*)=0
mf ( xk ) xk 1 xk ' , f ( xk )

k 0,1,
此种迭代至少有两阶收敛
32
3.5.2 Newton 法的重根情形 (4)

第三种迭代方法:

令 m (x) = f(x) / f '(x) 若x*是方程 f(x)=0 的m重根,则
6
3.2.1 二分法 (2)

区间中点序列{xn}就是方程的根x*的近似解序列。
1 bn an n1 (b a) 2

而xn是[an,bn]的中点,所以有
1 1 | xn x | (bn an ) n (b a) 2 2
*
n
lim xn x*
7
3.2.1 二分法 (3)

为求解方程 f(x)=0 的根 x*,假设

有一个近似值 xk ≈ x* f ’’存在且连续

因 f (x*)=0, 则:
'' f ( ) ' f ( x ) f ( xk ) f ( xk )( x xk ) ( x x k )2 2 若 f ' (x*) ≠0,
5
3.2.1 二分法 (1)

假设

已找到方程 f(x)=0 的一个有根区间 [a,b]; f(a)f(b)<0; 方程在区间 [a,b] 只有一个根。

二分法步骤:
令 [a1,b1]=[a,b] , 执行以下迭代步骤: 对于区间 [an,bn] , 其中点为 xn=1/2(an+bn); 若 f(an)f(xn)<0 , 则将 [an+1,bn+1] 替换为 [an,xn] ; 若 f(an)f(xn)> 0 , 则将 [an+1,bn+1] 替换为 [xn,bn] 。
13
3.3.1 不动点和不动点的迭代 (2)

可以通过不同的途径将方程 f(x)=0 变换成方 程 x = f (x) 的形式。 例如,


(x)=x - f(x) (x) = x - Af(x), 其中A为常数,
也可以用其他的方法。
14
3.3.1 不动点和不动点的迭代 (3)

设序列 {xk} 收敛到 x*,记误差 ek=xk-x*.
'' 2 f ( )( x x ) k x xk ' f ( xk ) 2 f ' ( xk )
21
f xk
3.5.1 Newton迭代法公式和收敛性 (2)

其中 在x*与xk之间。 略去最后一项,右端为x*的一个新的近似值,记为 xk+1:
f ( xk ) xk 1 xk ' f ( xk )

即 20 次二分可满足要求
8
3.2.2 试位法

假设

已找到方程 f(x)=0 的一个有根区间 [a,b]; f(a)f(b)<0; 方程在区间 [a,b] 只有一个根。

试位法步骤:
取点(an, f(an)) 和(bn, f(bn)) 连线与x轴的交点,即
f (bn )(bn an ) xn bn f (bn ) f (an )
3
3.1 引言 (2)

设f(x)可分解为
f ( x ) ( x x* )m g ( x )

其中m为正整数, 函数g满足g(x*) ≠ 0。


则称x*是 f(x) 的 m重零点, 或x*是方程 f(x)=0 的m重根 。
4
今日主题

第三章:非线性方程的数值解法



3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法

有: f '(x*)=1-1/m 因m>1, 所以 f ' (x*)≠0 ,且 | f ' (x*) |<1 Newton法是收敛的,但只是线性收敛。
31


3.5.2 Newton 法的重根情形 (3)

第二种迭代方法:将迭代函数改取为
mf ( x) ( x) x ' f ( x)

得到迭代法:
f ( xk ) f '( xk ) xk 1 xk , 2 [ f '( xk )] f ( xk ) f ''( xk )

k 0,1,
这种方法也是至少二阶收敛的。
34
3.5.2 Newton 法的重根情形 (6)



方程 x4-4x2+4=0 的根x*= 2 是二重根,用3种方 法求解。

它是x*的一个新的近似值

从序列{xk}, 用上式得到序列 { xk} 的方法, 称为Aitken加速方法。
18
3.4.1 Aitken加速方法 (3)

可以证明,只要{xk}满足 且lim ek 1 , k e k
xk x , k 1, 2,

1

则序列 { x k }是完全确定的,而且有
12
3.3.1 不动点和不动点的迭代 (1)

为解方程
f(x)=0 (4.3.1) (4.3.2)

将其变换为等价的方程
x = f (x)

其中是连续函数。构造迭代公式: x k+1 = f (xk)(4.3.3)如果k
l i m xk = x*
则称为迭代函数, x*是函数 的一个不动点,也就是方 程的根。此迭代法称为不动点迭代法。
如果 f(xn)=0 ,就找到了方程的根,否则: 若 f(an)f(xn)> 0 , 则 [an+1,bn+1] = [xn,bn] ; 若 f(an)f(xn)<0 , 则 [an+1,bn+1] = [an,xn] 。
11
今日主题

第三章:非线性方程的数值解法



3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法

例 4.2.1 X3-15x2+319=0,是否可以用二分法在 区间[5,10] 内求解,要求误差小于0.5∙10-5,需要 用二分法计算多少次?
设 f(x)=X3-15x2+319 => f(5)>0, f(10)<0 。 因此可以用二分法求解。


误差小于 0.5 ∙ 10-5,即
2 N ( 10 5 ) 0.5 10 5 6 N 19.9 lg 2
29
3.5.2 Newton 法的重根情形 (1)

设x*是方程f(x)=0的m重根,m>1,即

f (x) = (x-x*)m g(x) 其中g(x)有二阶导数,g(x*)≠0,重根情况下有 f ' (x*) =0 :
f ( x) ( x x ) g ( x) ( x) x ' x f ( x) mg ( x) ( x x ) g '( x) g ( x) ( x x ) g '( x) '( x) 1 mg ( x) ( x x ) g '( x) 1 ( x x ) g ( x)( )' mg ( x) ( x x ) g '( x)
xk x* lim 0 k x x k

说明 ?
19
今日主题

第三章:非线性方程和方程组的数值解 法

3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法
20
3.5.1 Newton迭代法公式和收敛性 (1)



3.1 引言 3.2 二分法和试位法 3.3 不动点迭代法 3.4 迭代加速收敛的方法 3.5 Newton 迭代法
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