S-(X X)0000-0 ZETA FUNCTIONS FOR ANALYTIC MAPPINGS, LOG-PRINCIPALIZATION OF IDEALS, AND NEW

合集下载

and

and

-2-
order to obtain their results. Our first result shows that if F ( x ) is H-admissible, then exp ( F ( x ) ) is HS-admissible, and so one can obtain complete asymptotic expansions for the coefficients of G ( x ) = exp ( F ( x ) ) − 1. This result follows quickly from some results of Hayman [10]. We give some applications of this result, namely to the estimation of the Bell numbers and of the number of idempotent elements in the symmetric semigroup on n elements [9]. In these examples F ( x ) is entire. Our main application is to the estimation of t h , n , the number of rooted unlabelled trees with height h and n vertices, where h is held fixed and n varies. (This problem was solved earlier by M. Yamashita [14].) We also state the analogous results for other tree-counting problems solved by Po ´lya enumeration theory. In these applications, with generating functions related by (1.2), the circle of convergence is typically a natural boundary. Our results are largely of methodological interest. We do not state many general results since we cannot hope to cover all the cases of interest. Instead, we present several examples which show how the powerful machinery developed by Hayman and by Harris and Schoenfeld can be used to quickly obtain very precise enumeration results in many interesting combinatorial problems. Results that are somewhat related to those of this paper are contained in [6], which obtains asymptotic estimates for coefficients of sequences of polynomials defined by nonlinear recurrences such as B h + 1 ( x ) = 1 + xB h ( x ) 2 for h ≥ 0 , B 0 (x) = 1 .

系统概述测试题

系统概述测试题

第1章V isual FoxPro系统概述测试题一、选择题2.数据库系统的组成包括()。

A.计算机硬件系统,数据集合,数据库系统,相关软件,数据管理员(用户)B.计算机软件系统,数据库集合,数据库管理系统,相关软件,数据管理员(用户C.计算机硬件系统,数据库集合,数据系统,相关软件,数据管理员(用户)D.计算机硬件系统,数据库集合,数据库管理系统,相关软件,数据管理员(用户[答案]D3.数据库系统的核心是()。

A.文件B.数据库管理系统C.操作系统D.数据库[答案]B4.数据库系统(DBS),数据库(DB)、数据库管理系统(DBMS)之间的关系是(:A. DBS包括DB和DBMS B.DB包括DBS和DBMSC.DBMS包括DBS和DB D.三者等级,没有包括关系[答案】A5.存储在计算机存储设备上的、结构化的相关数据集合称为()。

A.数据库B.数据库系统C.数据库应用系统D.数据库管理系统【答案】A6.计算机数据管理依次经历了()几个阶段。

A.文件系统、人工管理、数据库系统、分布式数据库系统和面向对象数据库系统B.文件管理、数据库系统、人工系统、分布式数据库系统和面向对象数据库系统C.数据库系统、文件系统、人工系统、分布式数据库系统和面向对象数据库系统D.人工管理、文件系统、数据库系统、分布式数据库系统和面向对象数据库系统[答案]D8.与文件管理系统相比,下列()不是数据库系统的特点。

A.数据结构化B.访问速度快C.数据独立性D.冗余度可控[答案]B9.在数据管理技术的发展过程中,可实现数据完全共享的阶段是()。

A.人工管理阶段B.文件系统阶段C.数据库阶段D.系统管理阶段[答案]C10.在数据库系统阶段,数据()。

A.具有物理独立性,没有逻辑独立性B.具有逻辑独立性,没有物理独立性C.物理独立性和逻辑独立性较差D.具有较高的物理独立性和逻辑独立性[答案]D12.关系数据库管理系统所管理的关系是()。

高斯赛德尔算法程序说明

高斯赛德尔算法程序说明

高斯赛德尔算法程序说明高斯赛德尔算法(Gauss-Seidel algorithm)是一种用于求解线性方程组的迭代方法。

它通过逐次修正线性方程组的解来逼近方程组的精确解。

算法步骤如下:1. 初始化解向量的初值,可以是全零向量或者任意向量。

2. 对于每个方程,使用当前的解来计算该方程中除未知数所在位置以外的其他已知数和常数项的值,然后用这些值代入方程中求解未知数的值,更新当前解向量的对应分量。

3. 重复步骤2,直到当前解向量的变化足够小或达到迭代次数的上限。

通过迭代计算,可以逼近线性方程组的精确解。

高斯赛德尔算法的程序示例:```pythondef gauss_seidel(A, b, x0, tol=1e-6, max_iter=100):n = len(A)x = x0.copy()# 迭代计算for k in range(max_iter):for i in range(n):sum1 = sum(A[i][j] * x[j] for j in range(i))sum2 = sum(A[i][j] * x[j] for j in range(i + 1, n))x[i] = (b[i] - sum1 - sum2) / A[i][i]# 检查当前解的变化是否足够小error = max(abs(A[i][i] * x[i] - b[i]) for i in range(n))if error < tol:breakreturn x```输入参数说明:- A: 系数矩阵(n × n)- b: 常数向量(长度为n)- x0: 初始解向量(长度为n)- tol: 解的变化容许度,默认为1e-6- max_iter: 最大迭代次数,默认为100输出为线性方程组的近似解向量。

需要注意的是,在使用高斯赛德尔算法求解线性方程组时,有时候可能会出现算法发散的情况,即迭代无法收敛到方程组的解。

生物进化算法

生物进化算法
生物进化算法
信实101 薛子朦 丁明 洪莹
引言
• 遗传算法作为一门新兴学科,在信息 学竞赛中还未普及,但由于遗传算法对许 多用传统数学难以解决或明显失效的复杂 问题,特别是优化问题,提供了一个行之 有效的新途径,且能够较好地解决信息学 竞赛中的NP难题,因此值得我们进行深入 的讨论。
关键词
• • • • • • • • • • 遗传算法(Genetic Algorithm) GA通常为简单遗传算法(SGA) 遗传(Genetic) 基因(Gene) 染色体(Chromosome) 选择(option) 交叉(cross) 变异(metamorphossis) 种群(population) 个体(individual)
• • • • • •
变异
• 变异运算用来模拟生物在自然界的遗传环境中由于各 种偶然因素引起的基因突变,它以很小概率随机地改变遗 传基因(表示染色体的符号串的某一位)的值。在染色体 以二进制编码的系统中,它随机地将染色体的某一个基因 由1变成0,或由0变成1。若只有选择和交换,而没有变异 操作,则无法在初始基因组合以外的空间进行搜索,使进 化过程在早期就陷入局部解而终止进化过程,从而使解的 质量受到很大限制。通过变异操作,可确保群体中遗传基 因类型的多样性,以使搜索能在尽可能大的空间中进行, 避免丢失在搜索中有用的遗传信息而陷入局部解,获得质 量较高的优化解答。
• 上述各种算子的实现是多种多样的,而且许多新 的算子正在不断地提出,以改进GA的某些性能。 系统参数(个体数n,基因链长度l,交叉概率Pc,变异 概率Pm等)对算法的收敛速度及结果有很大的影响, 应视具体问题选取不同的值。 • GA的程序设计应考虑到通用性,而且要有较强的 适应新的算子的能力。OOP中的类的继承为我们 提供了这一可能。

matlab中s-function用法

matlab中s-function用法

matlab中s-function用法创建一个有1输入(2维),2输出(1维),3个参数,还有全局变量的s-function。

1.新建sfunction的C语言文件打开simulink,点击User-Defined Functions里面的S-Function Examples。

这个里面有多个语言版本的模板,有C,C++,Ada,Fortran和M语言的版本,其实都大同小异,只要了解几个函数就很容易使用了。

选择C语言的版本:从S-function模块中选择C-file S-functions里面的Basic C-MEX template。

打开后,另存为自己的模块名字,如test.c。

下面我们来分析代码:#define S_FUNCTION_NAME test//这里把文件名sfuntmpl_basic修改为test #define S_FUNCTION_LEVEL 2#include "simstruc.h"//程序里面要用到的头文件在这里引用,如“math.h”等。

float global_var; //定义全局变量static void mdlInitializeSizes(SimStruct *S){//这个函数用来设置输入、输出和参数的。

ssSetNumSFcnParams(S, 3); /*设置参数个数,这里为3 */if (ssGetNumSFcnParams(S) != ssGetSFcnParamsCount(S)) {return;}; ssSetNumContStates(S, 0);//设置连续状态的个数,缺省为0ssSetNumDiscStates(S, 0);//设置离散状态的个数,缺省为0;if (!ssSetNumInputPorts(S, 1)) return;//设置输入变量的个数,这里为1 ssSetInputPortWidth(S, 0, 2); //设置输入变量0的维数为2ssSetInputPortRequiredContiguous(S, 0, true); //设置input0的访问方式,true就是临近访问,这样指针的增量后就可以直接访问下个input端口了。

Test Case Design

Test Case Design
• Systems that run forever in some non-deterministic way, e.g. control systems and communication systems • Systems with huge input domains
Strategy : • Identify input equivalence classes • Based on conditions on inputs / outputs in specification / description • Both valid and invalid input equivalence classes • Based on heuristics and experience
Equivalence Partitioning
Divide all possible inputs into classes (partitions) such that • There is a finite number of input equivalence classes • You may reasonably assume that
Test case design techniques: Blackbox testing
Definition
• Black-box testing is a method of software testing that examines the functionality of an application without peering into its internal structures or workings. This method of test can be applied to virtually every level of software testing: unit, integration, system and acceptance. It typically comprises most if not all higher level testing, but can also dominate unit testing as well.

matlab里function函数的用法

matlab里function函数的用法

matlab里function函数的用法Matlab是一种功能强大的数值计算和科学计算软件,可以进行数据分析、可视化、算法开发等各种科学计算任务。

在Matlab中,function函数是一种非常重要的特性,用于定义自己的函数。

本文将介绍function函数的用法及其相关知识。

在Matlab中,function函数用于定义一个独立的函数,可以在需要的地方调用并执行该函数。

使用function函数可以提高代码的可读性和可维护性,将代码模块化,使得程序结构更加清晰。

下面是一个简单的示例:function result = myFunction(input)% 函数主体部分result = input * 2;end上述代码定义了一个名为myFunction的函数,该函数有一个输入参数input,返回值为result。

函数主体部分定义了result的计算方法,即将input乘以2,并将结果赋值给result。

函数的定义以关键字function开始,后面是函数名和参数列表,参数列表用括号括起来,并用逗号分隔。

函数主体部分用关键字end结束。

在调用函数时,需要提供参数的值,并将返回值赋给一个变量。

例如,可以使用以下代码调用上述示例函数:x = 5;y = myFunction(x);上述代码将变量x的值赋给函数的输入参数input,并将返回值赋给变量y。

这样,变量y的值将为10,因为5乘以2等于10。

除了输入参数和返回值,函数还可以有局部变量。

局部变量只在函数内部有效,外部无法访问。

可以使用关键字persistent或者global声明一个局部变量。

例如,下面的代码定义了一个带有局部变量的函数:function result = myFunction(input)persistent count;if isempty(count)count = 0;endcount = count + 1;result = input * count;end上述代码中,变量count是一个局部变量,用于记录函数被调用的次数。

如何处理未定义的变量和函数

如何处理未定义的变量和函数

如何处理未定义的变量和函数在编程中,每当使用一个变量或函数时,都需要确保其已经定义了。

如果某个变量或函数没有被定义,程序将无法正常执行,会抛出错误并终止。

要解决这个问题,我们需要使用一些技巧来处理未定义的变量和函数。

1.检查变量和函数是否已经定义最基本的方法是在使用变量或调用函数之前检查其是否已经定义。

可以通过使用`if`语句或类似的条件判断语句来实现。

例如,在使用一个变量之前,可以使用`if (typeof variable !== 'undefined')`来检查变量是否已经定义。

类似地,在调用一个函数之前,可以使用`if (typeof functionName !== 'undefined' && typeof functionName === 'function')`来检查函数是否已经定义。

2.给变量和函数设置默认值如果一个变量或函数未定义,你可以为其设置一个默认值。

这样,即使未定义,程序仍然可以继续执行,并使用默认值代替。

例如,可以使用条件运算符来为一个变量设置默认值:`variable = (typeofvariable !== 'undefined') ? variable : defaultValue;`类似地,可以使用逻辑或运算符来为一个函数设置默认值:`functionName = (typeof functionName !== 'undefined' && typeof functionName=== 'function') ? functionName : defaultFunction;`3.引入外部模块或库如果一个变量或函数未定义,可能是因为没有正确引入所需的外部模块或库。

在使用它们之前,首先需要确保已经正确引入了这些模块或库。

可以使用`import`或`require`关键字来引入外部模块或库。

matlab简介2008

matlab简介2008

14
2 用MATLAB处理矩阵——容易
矩阵在MATLAB中是基本的数据单元, MATLAB中矩阵 变量名必须是以字母开头的,由字母和数字组成的字符串。 2.1 形成矩阵 在MATLAB中形成矩阵的办法有多种。生成 小型矩阵的常用办法是直接从键盘输入。 >> A=[1 3 2;3 1 0;2 1 5]
C= 5 8 5 >> 6 2 5 8 4 11 D= -3 -2 -1 0 -4 0 -4 -3 -1
>> E=A*B E= 25 14 30 17 10 22 28 27 46 >>
>> F=A' F=
1 3 2
3 1 0
2 1 5
转置
A=
1 3 2 3 1 1 2 0 5
>> G=inv(A) G= -0.1316 0.3421 0.0526 0.3947 -0.0263 -0.1579 -0.0263 -0.1316 0.2105 >>
MATLAB启动图标:
1.MATLAB的主窗口:包括10个按钮、六个下拉菜单。 其它的几个窗口都包含在这个大的主窗口中。
2.命令窗口 “>>”为运算提示符,表示MATLAB正处在准备状态。当在提 示符后输入一段运算式并按回车键后, MATLAB将给出计算结 果,然后在进入准备状态。
3.历史窗口 在默认设置下,历史窗口中会保留自安装起所有命令的历史记 录,并标明使用时间,这方便了使用者的查询。双击某一行命 令,即在命令窗口中执行该行命令。
2
命令窗口
当前目录窗口
历史窗口
发行说明书窗口
3
4.当前目录窗口 在当前目录窗口中可显示或改变当前目录,还可以显示 当前目录下的文件。 5.发行说明书窗口 用来说明用户所拥有的产品的工具包、演示及帮助信息。 6.工作间管理窗口 在工作间管理窗口中将显示目前内存中所有的MATLAB变 量的变量名、字节数以及类型等信息。

R统计软件及应用期末试题及答案

R统计软件及应用期末试题及答案

R统计软件及应用期末试题及答案一、选择题(每题2分,共20分)1. R语言中,用于将数据按照某一列排序的函数是()。

A. sort()B. order()C. arrange()D. rank()2. 在R中,用于提取矩阵第2行第3列元素的代码是()。

A. matrix[2, 3]B. matrix[3, 2]C. matrix[2][3]D. matrix[c(2, 3)]3. R语言中,以下哪个函数可以对数据进行离散化处理?A. cut()B. merge()C. subset()D. aggregate()4. 在R中,以下哪个函数可以计算两个变量之间的相关系数?A. mean()B. var()C. cor()D. sd()5. 下列哪项操作可以将一个数值向量转换为因子类型?A. as.character()B. as.numeric()C. as.factor()D. as.logical()6. R语言中,用于进行多项式回归分析的函数是()。

A. lm()B. glm()C. nls()D. polr()7. 在R语言中,执行一次循环操作的控制结构是()。

A. if-elseB. whileC. forD. repeat8. R语言中,用于创建一个空的数据框的函数是()。

A. matrix()B. data.frame()C. list()D. cbind()9. 在R语言中,以下哪个函数可以对数据进行缺失值的处理?A. na.omit()B. na.fail()C. na.exclude()D. na.pass()10. R语言中,以下哪个函数用于计算数据的标准差?A. sqrt()B. mean()C. var()D. sd()二、简答题(每题10分,共30分)1. 简述R语言的优点以及应用领域。

R语言是一种功能强大且广泛应用于统计分析和数据可视化的开源编程语言。

其主要优点包括:1)免费开源:R语言是一个开源软件,用户可以自由使用、复制、分发和修改,并且开源社区提供了丰富的扩展包和文档资源。

math.addexact用法 -回复

math.addexact用法 -回复

math.addexact用法-回复Title: Understanding the Usage of math.addexact()Introduction:In modern-day computer programming, mathematical operations are an indispensable aspect of most applications. One such operation is the addition of two numbers, widely used across different programming languages. Python, a popular language among developers, offers numerous built-in functions to simplify mathematical calculations. One such function is math.addexact(). This article aims to provide a comprehensive understanding of math.addexact(), its significance, and step-by-step guidance on its usage.1. What is math.addexact()?The math.addexact() function is part of the math module in Python and is used to precisely add two integer numbers. It ensures accuracy by handling overflow conditions and returning exact results, without any loss of information.2. Syntax and Parameters:The syntax for math.addexact() is as follows:math.addexact(x, y)Here, x and y are the two integer numbers to be added.3. Return Value:The math.addexact() function returns an integer value representing the exact sum of x and y.4. Step-by-Step Usage:To demonstrate the usage of math.addexact(), let's consider a simple example:pythonimport math# Define two integer numbersx = 10y = 20# Add the two numbers using math.addexact()result = math.addexact(x, y)# Print the resultprint("The sum of {} and {} is: {}".format(x, y, result))5. Importing the math module:Before using math.addexact(), it is important to import the math module into your Python script using the import statement: pythonimport math6. Defining integer values:Next, define two integer numbers, x and y, to be added together. The values can be assigned explicitly or obtained from user input, or any other source, based on the requirements of your program.pythonx = 10y = 207. Using math.addexact():To perform the addition operation, call the math.addexact()function with the two integers, x and y, as parameters.pythonresult = math.addexact(x, y)The function will internally handle cases such as overflow and return the exact sum as an integer value.8. Displaying the result:To display the result, use the print() function along with the formatted string:pythonprint("The sum of {} and {} is: {}".format(x, y, result))This will showcase the sum of the two numbers, ensuring clarity and readability.Conclusion:In conclusion, the math.addexact() function offers a precise way of adding two integer numbers while ensuring that no information or accuracy is lost in the process. By understanding its syntax, parameters, and usage, developers can effectively employ thisfunction in Python applications for accurate mathematical calculations.。

sfunction 变量循环计算

sfunction 变量循环计算

sfunction变量循环计算1. 概述sfunction变量循环计算是一种在计算机程序中经常用到的方法,它可以提高程序的效率和灵活性。

通过使用变量循环计算,程序可以重复执行一段特定的代码,并在每次执行时使用不同的变量值。

这样可以方便地处理大量数据,完成复杂的计算任务。

2. sfunction变量循环计算的作用sfunction变量循环计算主要用于处理大量数据或进行复杂的计算任务。

它可以帮助程序员简化代码,提高程序的效率,并且使程序更加灵活和可维护。

通过使用循环计算,可以避免重复性的代码,减少代码量,提高代码的可读性和可维护性。

3. sfunction变量循环计算的实现方法sfunction变量循环计算可以使用不同的编程语言来实现,比如C、C++、Java、Python等。

不同的编程语言提供了不同的循环结构,比如for循环、while循环、do-while循环等。

程序员可以根据具体的需求选择合适的循环结构,并结合变量的使用来实现循环计算。

4. sfunction变量循环计算的应用场景sfunction变量循环计算在实际的编程应用中有着广泛的应用场景。

比如在数据分析和处理、图像处理、模拟计算、科学计算等领域,sfunction变量循环计算都扮演着重要的角色。

它可以帮助程序员处理大规模的数据,完成复杂的计算任务,解决实际问题。

5. sfunction变量循环计算的优缺点sfunction变量循环计算的优点是可以提高程序的效率和灵活性,减少代码的重复性。

通过合理地使用循环计算和变量,可以简化程序,提高代码的可读性和可维护性。

但是sfunction变量循环计算也有一定的局限性,如果循环次数非常大或者计算任务非常复杂,可能会导致程序的性能下降,甚至出现死循环的情况。

6. 结论sfunction变量循环计算是一种重要的编程技术,它可以帮助程序员处理大规模的数据和复杂的计算任务。

合理地使用循环计算和变量,可以提高程序的效率和灵活性,简化代码,提高代码的可读性和可维护性。

quadrant在python中的用法

quadrant在python中的用法

quadrant在python中的用法什么是Quadrant?在Python中,Quadrant(象限)是一个代表平面直角坐标系中的四个象限的术语。

该术语用于描述点或物体在二维平面上相对于原点的位置。

每个象限都被两条互相垂直的坐标轴所分割,并被称为第一、第二、第三和第四象限。

第一象限是x和y坐标都为正值的象限,位于坐标系的右上方。

在该象限中,x轴的正方向是向右,y轴的正方向是向上。

因此,第一象限中的点的坐标值都是正数。

第二象限是x坐标为负值,y坐标为正值的象限,位于坐标系的左上方。

在第二象限中,x轴的正方向是向左,y轴的正方向是向上。

因此,第二象限中的点的x坐标值为负数,y坐标值为正数。

第三象限是x和y坐标都为负值的象限,位于坐标系的左下方。

在第三象限中,x轴的正方向是向左,y轴的正方向是向下。

因此,第三象限中的点的坐标值都是负数。

第四象限是x坐标为正值,y坐标为负值的象限,位于坐标系的右下方。

在第四象限中,x轴的正方向是向右,y轴的正方向是向下。

因此,第四象限中的点的x坐标值为正数,y坐标值为负数。

那么,在Python中如何判断一个点位于哪个象限呢?以下是一种简单的方法来实现这一功能:1. 首先,我们需要获取点的坐标,即x和y的值。

2. 接下来,使用条件语句来判断点位于哪个象限。

根据象限的定义,我们可以使用以下判断条件:- 如果x和y都是正数,那么点位于第一象限。

- 如果x是负数,y是正数,那么点位于第二象限。

- 如果x和y都是负数,那么点位于第三象限。

- 如果x是正数,y是负数,那么点位于第四象限。

3. 最后,根据判断结果输出相应的象限信息。

下面是一个示例代码,演示了如何使用Python确定一个点的象限:pythonx = float(input("请输入x坐标值:"))y = float(input("请输入y坐标值:"))if x > 0 and y > 0:print("该点位于第一象限")elif x < 0 and y > 0:print("该点位于第二象限")elif x < 0 and y < 0:print("该点位于第三象限")elif x > 0 and y < 0:print("该点位于第四象限")else:print("该点位于坐标轴上")在上面的代码中,我们首先使用`input()`函数获取用户输入的x和y坐标值。

编程选择题40道:Python语言基础:包括数据类型,变量,运算符,控制结构,函数

编程选择题40道:Python语言基础:包括数据类型,变量,运算符,控制结构,函数

1.在Python中,a = [1, 2, 3] 和 b = a,之后执行 a.append(4),以下哪个描述是正确的?o A. b 也将包含 4o B. b 不会包含 4o C. a 和 b 现在是同一个对象o D. a 和 b 是独立的列表答案: A解析: 当 b = a 时,b 并没有复制 a 的内容,而是指向 a 的同一份内容,因此对 a 的修改也将反映在 b 上。

2.Python 中的哪一个关键字用于定义函数?o A. varo B. functiono C. defo D. define答案: C解析: 使用def 关键字定义函数,var 用于定义变量在某些语言中,function 用于 JavaScript 中定义函数,define 用于 Scheme 或其他 Lisp 方言。

3.在下列代码中,0 < x <= 10 使用了哪种运算符?o B. 逻辑运算符o C. 算术运算符o D. 赋值运算符答案: A解析: 0 < x <= 10 使用了比较运算符 > 和 <= 来比较数值。

4.下列哪个语句是正确的?o A. 关键字参数必须在位置参数之前。

o B. 可以在一个函数定义中混合使用关键字参数和位置参数。

o C. 函数参数不能有默认值。

o D. 位置参数可以被多次指定。

答案: B解析: Python 函数中可以混合使用位置参数和关键字参数,关键字参数通常跟在位置参数之后。

5.以下哪段代码能够正确地创建一个字典并对其键进行检查?o A. d = dict() if 'a' in d: print("a 在字典中")o B. d = {} if 'a' not in d.keys(): print("a 不在字典中")o C. d = {} if 'a' in d.values(): print("a 是字典的值")o D. d = dict() if 'a' not in d: print("a 不是字典的键")答案: B解析: d = {} 创建一个空字典,'a' not in d.keys() 检查 'a' 是否不在字典的键中。

Example code for some copula functions

Example code for some copula functions

% Example code for some copula functions%% Andrew Patton%% 27 February 2006load 'c:\core\teaching\lse_teaching\lecture_notes\ibm_ccola_rets.txt'-ascii;ibm = ibm_ccola_rets(:,1);ccola = ibm_ccola_rets(:,2);% exceedence correlationsinc = 0.025;qq = (0.1:inc:0.9)';qq2 = [(0.1:inc:0.5)';(0.5:inc:0.9)'];temp1 = ang_chen1(ibm,ccola,qq);figure(1),plot(qq2,temp1,'o-')% quantile dependencetemp2 = quantiledep(ibm,ccola,qq);figure(2),plot(qq,temp2,'o-');% obtaining Unif(0,1) data% NOTE: for this example I will just use the empirical cdf to transform the % data, but in practice this is the step where a model for the conditional % (marginal) densities would be used: conditional means, variances and% distributions.u = empiricalcdf(ibm);v = empiricalcdf(ccola);T = length(u);% dropping the first few so we have an even 2500 observationsu = u(end-2499:end);v = v(end-2499:end);T = length(u)% estimating some copula modelsoptions =optimset('Display','iter','TolCon',10^-12,'TolFun',10^-4,'TolX',10^-6); % 1. Normal Copulakappa1 = corrcoef12(norminv(u),norminv(v));LL1 = NormalCopula_CL(kappa1,[u,v]);% 2. Clayton's copulalower = 0.0001;theta0 = 1;[ kappa2 LL2] =fmincon('claytonCL',theta0,[],[],[],[],lower,[],[],options,[u,v]);% 3. Rotated Clayton copula (with tail dep in upper tail instead of lower) lower = 0.0001;theta0 = 1;[ kappa3 LL3] =fmincon('claytonCL',theta0,[],[],[],[],lower,[],[],options,1-[u,v]);% 4. Plackett copulalower = 0.0001;theta0 = 1;[ kappa4 LL4] =fmincon('plackettCL',theta0,[],[],[],[],lower,[],[],options,[u,v]);% LL5 = -3.2721% 5. Frank copulatheta0 = 1;[ kappa5 LL5] =fmincon('frankCL',theta0,[],[],[],[],lower,[],[],options,[u,v]);% 6. Gumbel copulalower = 1.1;theta0 = 2;[ kappa6 LL6] =fmincon('gumbelCL',theta0,[],[],[],[],lower,[],[],options,[u,v]);% 7. Rotated Gumbel copulalower = 1.1;theta0 = 2;[ kappa7 LL7] =fmincon('gumbelCL',theta0,[],[],[],[],lower,[],[],options,1-[u,v]);% 8. Student's t copulalower = [-0.9 , 2.1 ];upper = [ 0.9 , 100 ];theta0 = [kappa1;10];[ kappa8 LL8] =fmincon('tcopulaCL',theta0,[],[],[],[],lower,upper,[],options,[u,v]);% 9. Symmetrised Joe-Clayton copulalower = [0 , 0 ];upper = [ 1 , 1];theta0 = [0.25;0.25];[ kappa9 LL9] =fmincon('sym_jc_CL',theta0,[],[],[],[],lower,upper,[],options,[u,v]);LL = [LL1;LL2;LL3;LL4;LL5;LL6;LL7;LL8;LL9];[(1:length(LL))',LL]sortrows([(1:length(LL))',LL],2)% optimal copula (in terms of log-likelihood) is one with lowest likelihood % (since we minimise the *negative* LL, rather than maximise the positive LL)opt_copula = find(LL==min(LL))% for these assets it is copula 8, the t-copula,% followed by copula 9, the SJC copula% the worst is copula 3, the rotated Clayton% tail dependence implied by each of these copulastauLU = nines(9,2);tauLU(1,:) = [0,0]; % Normal copula has zero tail dependence tauLU(2,:) = [2^(-1/kappa2),0]; % Clayton copula has zero upper tail dependencetauLU(3,:) = [0,2^(-1/kappa3)]; % Rotated Clayton copula has zero lower tail dependencetauLU(4,:) = [0,0]; % Plackett copula has zero tail dependence tauLU(5,:) = [0,0]; % Frank copula has zero tail dependence tauLU(6,:) = [0,2-2^(1/kappa6)]; % Gumbel copula has zero lower tail dependencetauLU(7,:) = [2-2^(1/kappa7),0]; % Rotated Gumbel copula has zero upper tail dependencetauLU(8,:) =ones(1,2)*2*tdis_cdf(-sqrt((kappa8(2)+1)*(1-kappa8(1))/(1+kappa8(1))),k appa8(2)+1); % Student's t copula has symmetric tail dependencetauLU(9,:) = kappa9([2,1])'; % SJC copula parameters are the tail dependence coefficients, but in reverse order.tauLU% the tail dependence values are reasonably similar, when they are allowed % to be non-zerosortrows([(1:9)',LL,tauLU],2)% the 3 best fitting copulas all allow for non-zero lower tail% dependence. however, Clayton's copula does poorly even with lower tail % dependence, suggesting that it is just a poor parameterisation for these % two stocks.% Now taking a look at a couple of time-varying copulas% 10. Time-varying normal Copulalower = -5*ones(3,1); % in theory there are no constraints, but setting loose constraints sometimes helps in the numerical optimisationupper = 5*ones(3,1);theta0 = [log((1+kappa1)/(1-kappa1));0;0];[ kappa10 LL10] =fmincon('bivnorm_tvp1_CL',theta0,[],[],[],[],lower,upper,[],options,[u, v],kappa1);[LL10, rho10] = bivnorm_tvp1_CL(kappa10,[u,v],kappa1);figure(10),plot((1:T)',rho10,(1:T)',kappa1*ones(T,1),'r--'),legend('time-varying','constant'),title('Normal copula');% looks nice% 11. Time-varying rotated Gumbel copulalower = -5*ones(3,1); % in theory there are no constraints, but setting loose constraints sometimes helps in the numerical optimisationupper = 5*ones(3,1);theta0 = [sqrt(kappa7-1);0;0];[ kappa11 LL11] =fmincon('Gumbel_tvp1_CL',theta0,[],[],[],[],lower,upper,[],options,[1-u,1-v],kappa7);[LL11, rho11] = Gumbel_tvp1_CL(kappa11,[1-u,1-v],kappa7);figure(11),plot((1:T)',rho11,(1:T)',kappa7*ones(T,1),'r--'),legend('time-varying','constant'),title('Rotated Gumbel copula');% not so nice: variation in this parameter looks like just noise.% 12. Time-varying SJC copulalower = -25*ones(6,1); % in theory there are no constraints, but setting loose constraints sometimes helps in the numerical optimisationupper = 25*ones(6,1);theta0 =[log(kappa9(1)/(1-kappa9(1)));0;0;log(kappa9(2)/(1-kappa9(2)));0;0];[ kappa12 LL12] =fmincon('sym_jc_tvp_CL',theta0,[],[],[],[],lower,upper,[],options,[u,v],kappa9);[ LL12 tauU12 tauL12] = sym_jc_tvp_CL(kappa12,[u,v],kappa9);figure(12),subplot(2,1,1),plot((1:T)',tauL12,(1:T)',kappa9(2)*ones(T,1),'r--'),legend('time-varying','constant'),title('SJC copula - lowertail'),axis([0,T,0,0.8]);subplot(2,1,2),plot((1:T)',tauU12 ,(1:T)',kappa9(1)*ones(T,1),'r--'),le gend('time-varying','constant'),title('SJC copula - uppertail'),axis([0,T,0,0.8]);% movement in upper tail dependence seems very noisy, whereas some of the movement in lower tail dependence appears informative.LL = [LL1;LL2;LL3;LL4;LL5;LL6;LL7;LL8;LL9;LL10;LL11;LL12];[(1:length(LL))',LL]sortrows([(1:length(LL))',LL],2)% new rankings:% 1 is time-varying SJC copula% 2 is time-varying rotated Gumbel% 3 is constant Student's t% 4 is time-varying Normalparams = [ones(7,1);2;2;3;3;6]; % number of parameters in each modelAIC = 2*LL + 2/T*params;BIC = 2*LL + log(T)/T*params;[(1:length(LL))',LL,AIC,BIC]sortrows([(1:length(LL))',LL,AIC,BIC],2)sortrows([(1:length(LL))',LL,AIC,BIC],3)sortrows([(1:length(LL))',LL,AIC,BIC],4)% rankings by AIC and BIC are the same as by log-likelihood (T is so large % that k=1 vs k=6 does not impose a very large penalty)。

S-Function 使用及应用举例

S-Function 使用及应用举例
setting smaple times and offsets 主要设置采样时间 MATLAB 为了用户方便,已经书写了 S-FUNCTIONS 模板函数 sfuntmpl.m。为了更好的写 S-FUNCTIONS,大家来看一下,该函数 sfuntmpl.m 内容如下:(我 通过在该内容加注释来说明,以$开头)
高范强@IEE 版权所有
3
% actual length for all other flags will be equal to the % length of the input, U. % SYS(5) = Reserved for root finding. Must be zero. % SYS(6) = Direct feedthrough flag (1=yes, 0=no). The s-function % has direct feedthrough if U is used during the FLAG=3 % call. Setting this to 0 is akin to making a promise that % U will not be used during FLAG=3. If you break the promise % then unpredictable results will occur. % SYS(7) = Number of sample times. This is the number of rows in TS. % $需要说明的是 sys 的顺序不能乱 % % X0 = Initial state conditions or [] if no states. % % STR = State ordering strings which is generally specified as []. % % TS = An m-by-2 matrix containing the sample time % (period, offset) information. Where m = number of sample % times. The ordering of the sample times must be: % % TS = [0 0, : Continuous sample time. % 0 1, : Continuous, but fixed in minor step % sample time. % PERIOD OFFSET, : Discrete sample time where % PERIOD > 0 & OFFSET 〈 PERIOD. % -2 0]; : Variable step discrete sample time % where FLAG=4 is used to get time of % next hit. % % There can be more than one sample time providing % they are ordered such that they are monotonically % increasing. Only the needed sample times should be % specified in TS. When specifying than one % sample time, you must check for sample hits explicitly by % seeing if % abs(round((T-OFFSET)/PERIOD) - (T-OFFSET)/PERIOD) % is within a specified tolerance, generally 1e-8. This % tolerance is dependent upon your model's sampling times % and simulation time. % % You can also specify that the sample time of the S-function % is inherited from the driving block. For functions which % change during minor steps, this is done by % specifying SYS(7) = 1 and TS = [-1 0]. For functions which

提纲

提纲

VF练习题基本概念对表进行垂直方向的分割用的运算是:投影对表进行水平方向的分割用的运算是:选择Visual FoxPro关系数据库管理系统能够实现的三种基本关系运算是:选择、投影、连接Visual FoxPro是一种:数据库管理系统关系数据库系统中所使用的数据结构是:二维表用二维表形式表示的数据模型是:关系数据模型把二维表表头中的项目称为:属性名如果“姓名”是属性名,某个学生叫“张三”,则张三是:属性值关系理论中的“元组”概念,在关系数据库中称为:记录表的关联关系类型包括:一对一关联关系,一对多关联关系关系型表达式的运算结果是:逻辑型常量数据库文件的扩展名是:.dbc常量、变量、运算符什么是变量:在命令操作和程序运行过程中,其值允许变化的量称为变量。

7E-6是一个:数值常量姓名= = “张三”表示精确相等日期型常量的定界符是:花括号日期型常量{^2000/05/07}与日期型常量{^1999/06/08}哪一个大:前者字符串'It is a'string' 是否合法否已知字符串M=”12□34□”,N=”□56□78”,则连接运算M-N的运算结果为:”12□34□56□78□”要存储员工上下班打卡的日期和时间,应采用哪种数据类型的字段:日期时间类型表示逻辑值为真的符号有:.T. .t. .Y. .y.表示逻辑值为假的符号有:.F. .f. .N. .n.在Visual FoxPro中,逻辑非可以表示为:!NOT在Visual FoxPro中,逻辑运算优先级最高的是:NOT AND OR关于FoxPro中的运算符的优先级是:算术运算符关系运算逻辑运算VisualFoxPro中的合法变量名是:字母开始,后面为字母或数字或符号,但是:许多符号不许使用不加任何参数的? 输出将产生一个换行的动作不加任何参数的??输出将不产生换行动作表达式设X=”AB”,Y=”ABC”,则X$Y的值是:.T. X=Y的值是:.F. Y=X的值是:.T.1.取子串函数SUBSTR(,,)SUBSTR(字符串表达式,起始位置,长度)2. 求字符串长度函数LEN( )3. 搜索子串位置函数AT(子串,原字符串)4. “$”判断第一个字符串是否是第二个字符串的子字符串。

spss函数大全解读

spss函数大全解读

Spss 算术函数孙中友江苏ABS(numexpr 数值。

返回 numexpr (必须为数值的绝对值。

ARSIN(numexpr 数值。

返回 numexpr 的反正弦(以弧度为单位 ,求出的值必须为 -1 和 +1 之间的数字值。

ARTAN(numexpr 数值。

返回 numexpr 的反正切(以弧度为单位 , numexpr 必须为数字值。

COS(radians 数值。

返回 radians 的余弦(以弧度为单位 , radians 必须为数字值。

EXP(numexpr 数值。

返回 e 的 numexpr 次幂, 其中 e 是自然对数的底数, 而numexpr 是数值。

较大的 numexpr 值可能会产生超过机器性能的结果。

LN(numexpr 数值。

返回以 e 为底数的 numexpr 的对数, numexpr 必须为大于 0 的数值。

LNGAMMA(numexpr 数值。

返回 numexpr 的完全 Gamma 函数的对数, numexpr 必须为大于 0 的数值。

LG10(numexpr 数值。

返回以 10 为底数的 numexpr 的对数, numexpr 必须为大于 0 的数值。

MOD(numexpr,modulus 数值。

返回 numexpr 除以 modulus 所得到的余数。

两个参数都必须为数值,且 modulus 不得为 0。

RND(numexpr 数值。

返回对 numexpr 舍入后产生的整数, numexpr 必须为数值。

刚好以 .5 结尾的数值将舍去 0 以后的数值。

SIN(radians 数值。

返回 radians 的正弦(以弧度为单位 , radians 必须为数字值。

SQRT(numexpr 数值。

返回 numexpr 的正平方根, numexpr 必须为非负数。

TRUNC(numexpr 数值。

返回 numexpr 被截断为整数(向 0 的方向的值。

统计函数后缀 .n 可在所有统计函数中使用以指定有效参数的数目。

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U n sp eci…ed Jou rn alVolu m e00,N u m b er0,Pages000–000S????-????(X X)0000-0ZETA FUNCTIONS FOR ANALYTIC MAPPINGS, LOG-PRINCIPALIZATION OF IDEALS,AND NEWTONPOLYHEDRAWILLEM VEYS AND W.A.ZUNIGA-GALINDOAbstract.In this paper we provide a geometric description of the possi-ble poles of the Igusa local zeta function Z (s;f)associated to an analyticmapping f=(f1;:::;f l):U( K n)!K l,and a locally constant function,with support in U,in terms of a log-principalizaton of the K[x] idealI f=(f1;:::;f l).Typically our new method provides a much shorter list ofpossible poles compared with the previous methods.We determine the largestreal part of the poles of the Igusa zeta function,and then as a corollary,we ob-tain an asymptotic estimation for the number of solutions of an arbitrary sys-tem of polynomial congruences in terms of the log-canonical threshold of thesubscheme given by I f.We associate to an analytic mapping f=(f1;:::;f l)a Newton polyhedron (f)and a new notion of non-degeneracy with respectto (f).The novelty of this notion resides in the fact that it depends on oneNewton polyhedron,and Khovanskii’s non-degeneracy notion depends on theNewton polyhedra of f1;:::;f l.By constructing a log-principalization,wegive an explicit list for the possible poles of Z (s;f),l 1,in the case inwhich f is non-degenerate with respect to (f).1.IntroductionLet K be a p adic…eld,i.e.[K:Q p]<1.Let R K be the valuation ring of K,P K the maximal ideal of R K,and K=R K=P K the residue…eld of K. The cardinality of the residue…eld of K is denoted by q,thus K=F q.Forz2K,ord(z)2Z[f+1g denotes the valuation of z,and j z jK =q ord(z)its absolute value.The absolute value j jK can be extended to K l by de…ningk z k K=max1 i l j z i j K,for z=(z1;:::;z l)2K l.Let f1;:::;f l be polynomials in K[x1;:::;x n],or,more generally,K analytic functions on an open set U K n.We consider the mapping f=(f1;:::;f l): K n!K l,respectively,U!K l.Let :K n!C be a Schwartz-Bruhat function (with support in U in the second case).The Igusa local zeta function associated to2000Mathematics Subject Classi…cation.Primary11S40,11D79,14M25;Secondary32S45.Key words and phrases.Igusa zeta functions,congruences in many variables,topological zeta functions,motivic zeta functions,Newton polyhedra,toric varieties,log-principalization of ideals.The…rst author was partially supported by the Fund of Scienti…c Research-Flanders (G.0318.06).The second author thanks the…nancial support of the NSA.Project sponsored by the Na-tional Security Agency under Grant Number H98230-06-1-0040.The United States Government is authorized to reproduce and distribute reprints notwithstanding any copyright notation herein.c 2001enter n am e of copyright h old er12W ILLEM VEYS AND W.A.ZUNIGA-GALINDOthe above data is de…ned asZ (s;f)=Z (s;f;K)=Z K n (x)k f(x)k s K j dx j;for s2C with Re(s)>0,where j dx j is the Haar measure on K n normalized in such a way that R n K has measure1.We write Z(s;f),Z0(s;f)and Z W(s;f)when is the characteristic function of R n K,P n K,and an open compact subset W of K n, respectively.The function Z (s;f)admits a meromorphic continuation to the complex plane as a rational function of q s.Igusa established this result in the hypersurface case using Hironaka’s resolution theorem[16,Theorem8.2.1].In the case l 1the rationality of Z (s;f)was established by Meuser in[24],however,as mentioned in the review MR83g:12015of[24],a trick by Serre allows to deduce the general case from the hypersurface case.Denef gave a completely di¤erent proof of the rationality of Z (s;f),l 1,using p-adic cell decomposition[4].The mentioned results do not give any information about the poles of Z (s;f)in the case l>1:In [37]the second author showed that a list of possible poles of Z (s;f),l 1,can be computed from an embedded resolution of singularities of the divisor[l i=1f 1i(0) by using toroidal geometry.In the special case in which f is a non-degenerate homogeneous polynomial mapping the possible poles of Z (s;f)are given in[38].In this paper we provide a geometric description of the possible poles of Z (s;f), l 1,in terms of a log-principalization of the K[x] ideal I f=(f1;:::;f l)(see Theorem2.4).At this point it is important to mention that the main result in[37] gives an algorithm to compute a list of possible poles of Z (s;f),l 1,in terms of an embedded resolution of singularities of the divisor[l i=1f 1i(0),while Theorem2.4gives a list of candidates to poles in terms of a log-principalization of the idealI f.Typically our new method provides a much shorter list of possible poles(see Example2.5).It is important to mention that in the case l=1the problem of determining the poles of the meromorphic continuation of Z (s;f)in Re(s)<0 has been studied extensively(see e.g.[3],[14],[28],[23],[32],[34]).The relevance of this problem is due to the existence of several conjectures relating the poles of Z (s;f)with the structure of the singular locus of f.In the case of polynomials in two variables,as a consequence of the works of Igusa,Strauss,Meuser and the…rst author,there is a complete solution of this problem[14],[27],[23],[33].For general polynomials the problem of determination of the poles of Z (s;f)is still open. There exists a generic class of polynomials named non-degenerate with respect to its Newton polyhedron for which it is possible to give a small set of candidates for the poles of Z (s;f).The poles of the local zeta functions attached to non-degenerate polynomials can be described in terms of Newton polyhedra.The case of two variables was studied by Lichtin and Meuser[21].In[5],Denef gave a procedure based on monomial changes of variables to determine a small set of candidates for the poles of Z (s;f)in terms of the Newton polyhedron of f.This result was obtained by the second author,using an approach based on the p adic stationary phase formula and Néron p desingularization,for polynomials with coe¢cients in a non-archimedean local…eld of arbitrary characteristic[36],(see also[7],[29]).In the case l=1,among the conjectures relating the poles of Igusa’s zeta function with topology and singularity theory,we mention here a conjecture of Igusa that proposes that the real parts of the poles of the Igusa zeta function of f are rootsZETA FUNCTIONS AND NEW TON POLYHEDRA3 of the Bernstein polynomial of f(see e.g.[3],[16],and references therein).It seems reasonable to believe that such relations between poles and singularity theory extend to the case l>1.Indeed,recently it was proved that the above-mentioned conjecture of Igusa is valid in the case in which I f is a monomial ideal[13].In the case l=1,the largest real part of the poles of the Igusa zeta function has been extensively studied both in the archimedean and non-archimedean cases[7], [21],[31],[36].In the case l 1we show that the largest real part (I f)of the poles of the Igusa zeta function attached to f can easily be determined from a log-principalization of the ideal I f(see Theorem2.7).As a consequence of this result we obtain an asymptotic estimation for the number of solutions of an arbitrary system of polynomial congruences in terms of the log-canonical threshold of a log-principalization(see Corollary2.9,and the comments that follow).At this point we have to mention that in the case l=1Loeser found lower and upper bounds for(I f)in terms of certain geometric invariants introduced by Teissier[22,Theorem2.6and Proposition3.1.1],[30].In this form he derived a geometric bound for the number of solutions of a polynomial congruence involving one polynomial.If f is a polynomial mapping with coe¢cients in a number…eld F,then for every maximal ideal P of the ring of algebraic integers of F,we can consider Z(s;f;K), l 1,where K is the completion of F with respect to P.We give an explicit formula for Z(s;f;K),l 1,that is valid for almost all P(see Theorem2.10). The proof of this formula follows by adapting the argument given by Denef for the case l=1[6].One can also associate to a sheaf of ideals I on a smooth algebraic variety(over a …eld of characteristic zero)a motivic zeta function(see De…nition2.16).By usinga log-principalization of I we give a similar explicit formula for it(see Theorem2.17).The proof is a reasonably straightforward generalization of the one given by Denef and Loeser in[8].By specializing to Euler characteristics one obtains the topological zeta function associated to I.We attach to an analytic mapping f=(f1;:::;f l)a Newton polyhedron (f) and a new notion of non-degeneracy with respect to (f).The novelty of this notion resides in the fact that it depends on one Newton polyhedron,and Khovan-skii’s non-degeneracy notion depends on the Newton polyhedra of f1;:::;f l(see [18],[26]).By constructing a log-principalization,we give an explicit list for the possible poles of Z (s;f),l 1,in the case in which f is non degenerate with respect to (f)(see Theorem3.11).This theorem provides a generalization to the case l 1of a well-known result that describes the poles of the local zeta function associated to a non-degenerate polynomial in terms of the corresponding Newton polyhedron[5],[21],[36].This result was originally established by Varchenko[31] for local zeta functions over R.If f is non-degenerate with respect to (f),then (I f)can be computed from (f)in the classical way(see Corollary3.12).By using our notion of non-degeneracy and toroidal geometry we give an explicit formula for Z(s;f)and Z0(s;f),l 1.This formula generalizes one given by Denef and Hoornaert in the case l=1[7,Theorem4.2],and one given by the second author for the local zeta function of a monomial mapping[36,Theorem 6.1].The authors wish to thank the referee for his/her constructive remarks about the paper.The…rst author would like to thank Robert Lazarsfeld for suggestions and4W ILLEM VEYS AND W.A.ZUNIGA-GALINDOseveral inspiring conversations on poles of zeta functions,and Orlando Villamayor for very useful information concerning principalization in the analytic setting.2.The Igusa local zeta function of a polynomial mapping2.1.Log-principalization and poles of the Igusa local zeta function.We state the two versions of log-principalization of ideals that we will use in this paper. The…rst is the‘classical’algebraic formulation,see for example[11],[12],[35].The second is in the context of p adic analytic functions.It follows from the results in [11],see5.11in that paper(noticing that‘Property D’there is valid in the p adic analytic setting).Theorem2.1(Hironaka).Let X0be a smooth algebraic variety over a…eld of characteristic zero,and I a sheaf of ideals on X0.There exists a log-principalization of I,that is a sequenceX0 1 X1 2 X2::: i X i ::: r X r=Xof blow-ups i:X i 1 X i in smooth centers C i 1 X i 1such that(1)the exceptional divisor E i of the induced morphism i= 1 ::: i:X i !X0 has only simple normal crossings and C i has simple normal crossings with E i,and (2)the total transform( r) (I)is the ideal of a simple normal crossings divisor E#.If the subscheme determined by I has no components of codimension one, then E#is a natural combination of the irreducible components of the divisor E r. Remark2.2.We use notations like( r) (I)as in[35].However,other authors use the notation IO X for the same object,for example in[11].As many other authors we use the term‘log-principalization’.The terms‘principalization’and ‘monomialization’are also used for the same purpose by other authors. Theorem2.3([11]).Let K be a p adic…eld and U an open submanifold of K n. Let f1;:::;f l be K analytic functions on U such that the ideal I f=(f1;:::;f l)is not trivial.Then there exists a log-principalization :X K!U of I f,that is, (1)X K is an n dimensional K analytic manifold, is a proper K analytic map which is a composition of a…nite number of blow-ups in closed submanifolds,and which is an isomorphism outside of the common zero set Z K of f1;:::;f l;(2) 1(Z K)=[i2T E i,where the E i are closed submanifolds of X K of codi-mension one,each equipped with a pair of positive integers(N i;v i)satisfying the following.At every point b of X K there exist local coordinates(y1;:::;y n)on X K around b such that,if E1;:::;E p are the E i containing b,we have on some neighborhood of b that E i is given by y i=0for i=1;:::;p,(I f)is generated by"(y)p Y i=1y N i i;and(dx1^:::^dx n)= (y)pY i=1y v i 1i!dy1^:::^dy n;where"(y), (y)are units in the local ring of X K at b.The(N i;v i),i2T,are called the numerical data of .ZETA FUNCTIONS AND NEW TON POLYHEDRA 5Let K be a p adic …eld.Let f 1;:::;f l be polynomials over K or K analytic functions on U K n .We set I f to be the K analytic ideal generated by the f i ;we suppose it is not trivial.Let :K n !C or U !C be a Schwartz-Bruhat function,that is,a locally constant function with compact support.We associate to f =(f 1;:::;f l )and the Igusa zeta function Z (s;f )as in the introduction.The following theorem yields a new proof of its meromorphic continuation,but especially it gives a list of its possible poles in terms of the numerical data of a log-principalization.Theorem 2.4.The local zeta function Z (s;f )admits a meromorphic continu-ation to the complex plane as a rational function of q s .Furthermore,the poles have the form s = v i N i 2 p 1log q k N i;k 2Z ,where the (N i ;v i )are the numerical data of a log-principalization :X K !U of the ideal I f =(f 1;:::;f l ).Proof.We pick a log-principalization of I f as in Theorem 2.3and we use all notations that were introduced there.At every point b 2X K we can take a chart (V; V )with coordinates (y 1;:::;y n ),which may be schrinked later when necessary.Let g (y )be a generator of (I f )= (f 1;:::;f l )in V .Theng (y )="(y )p Y i =1y N i i ;(dx 1^:::^dx n )= (y ) p Y i =1y v i 1i !dy 1^:::^dy n ;where "(y )and (y )are units of the local ring of X K at b .Furthermore,since (I f )is locally generated by g (y )we havef i (y )=g (y )e f i (y );for i =1;:::;l ,y 2V ,where each e f i (y )is an analytic function on V .And,since g (y )2 (I f ),we also have g (y )=X l i =1a i (y )f i (y ),with a i (y )an analytic function on V for each i ;therefore1=l X i =1a i (y )e f i (y ),for y 2V .Then there exists at least one index i 0such that e f i 0(b )=0,hence we may assume that e f i 0(y )=0on V and that k (f 1(y );:::;f l (y ))k s K = e f i (y ) i =2H ; e f i (b ) i 2H s K j g (y )j s K ;for y 2V .Here H f 1;:::;n g such that e f i (b )=0,i 2H .We may furthersuppose that e f i (y ) i =2H ; e f i (b ) i 2H s K = e f i (b ) i 2H s K6W ILLEM VEYS AND W.A.ZUNIGA-GALINDOon V.Since is proper, 1(supp( ))is compact open in X K,hence we can express it as a…nite disjoint union of compact open sets B such that each B is contained in some V above.Since is locally constant we may assume(after sub-dividing B )that( )j B=( )(b),j"jK j B =j"(b)j K,j j K j B =j (b)j K,and V(B )=c+ e0R n K.Denote by D K=(div( (I f)))K.Since :X K n D K !U n (D K)is biana-lytic,and D K has measure zero,we haveZ (s;f)=Z U n (D K) (x)k f(x)k s K j dx j=X ( )(b)j"(b)j s K j (b)j K e f i(b) i2H s K Z c+ e0R n K Y1 i p j y i j N i s+v i 1j dy j:The conclusion is now obtained by computing the integral in the previous expression like in the case l=1(see[16,Lemma8.2.1]). Example2.5.Let K be a p adic…eld,and let f1(x;y)=y a x b,f2(x;y)= x a y b,with a<b,and for j=3;:::;M,M 3,f j(x;y)=x n j y m j h j(x;y), with n j,m j a,and h j(x;y)2K[x;y].Set f=(f1;f2;f3;:::;f M),and I f=(f1;f2;f3;:::;f M).Let be a Schwartz-Bruhat function whose support is contained in a su¢ciently small neighborhood of the origin.A log-principalization of the ideal I f(over a neighborhood of the origin)is obtained by blowing-up the origin of K2.There is only one exceptional curve E=P1(K)whose numerical da-tum is(a;2),and therefore the possible poles of Z (s;f)have real part 2a.In[37] an algorithm for computing a list of candidates for the poles of Z (s;f)in terms of the numerical data of an embedded resolution of the divisor[M j=1f 1j(0)was given.Since the f j(x;y)are arbitrary polynomials for3 j M,the mentioned algorithm gives in general a very long list of possible poles.2.2.The largest real part of the poles of the Igusa zeta function.Let U bea compact open subset of K n and let f=(f1;:::;f l):U !K l be an analytic mapping.Recall that Z U(s;f)=R U k f(x)k s K j dx j.The following lemma is known by the experts,however we did not…nd a suitable reference for it;for the sake of completeness we include its proof here.Lemma2.6.(1)Z U(s;f)has no pole in s,i.e.Z U(s;f)is a Laurent polynomial in q s if and only if there is no x2U such that f1(x)=:::=f l(x)=0.(2)If02U and f(0)=0,i.e.f1(0)=:::=f l(0)=0,then Z U(s;f)has at least one pole in s.Proof.(1)We…rst note that rationality of Z U(s;f)implies the equivalence of the conditions“Z U(s;f)has no pole in s”and“Z U(s;f)is a Laurent polynomial inq s.”(()Since f:U !K l is continuous,also k f kK :U !q Z[f0g is continuous.If0does not belong to the image of k f kK ,then there are only…nitely many valuesin the image because U is compact.So R U k f(x)k s K j dx j is a Laurent polynomial in q s.())If x02U with f1(x0)=:::=f l(x0)=0,by using the continuity of k f k K, there exist in…nitely many i such that there exists x i2U with k f(x i)k K=q i.ZETA FUNCTIONS AND NEW TON POLYHEDRA 7Since U is open we have for all those i that the Haar measure of the set x 2U j k f (x )k K =q iis positive.ThereforeZ U (s;f )=P j volx 2U j k f (x )k K =q j q sj is not a Laurent polynomial in q s .(2)The second part follows directly from the …rst one. Theorem 2.7.Let f =(f 1;:::;f l ):U !K l be an analytic mapping de…ned on a compact open neighborhood of the origin U such that f (0)=0.We take a log-principalization :X K !U as in Theorem 2.3with numerical data (N i ;v i ),i 2T .Let := (I f )=min i v i N i .Then (I f )is the real part of a pole of Z U (s;f ).In particular, (I f )depends only on I f .Proof.The proof will be achieved by establishing that q is the radius of conver-gence R of Z U (s;f )considered as a function in q s .Certainly R q ,since (by Theorem 2.4)the candidate poles closest to the origin have modulus q .We shall show that R q by proving a lower bound for the coe¢cients of Z U (q s ;f ),considered as power series in q s :Z U q s ;f =P jvol x 2U j k f (x )k K =q j q sj :Take a generic point b on a component E r with v r N r = ,and a small enough chart B ( X K )around b with coordinates (y 1;:::;y n )such that(I f )is generated by "(y )y N r 1;and(dx 1^:::^dx n )= (y )y v r 11dy 1^:::^dy n ;on B ,where j "j K and j j K are constant (and nonzero)on B .After an eventual K analytic coordinate change,we may assume furthermore that B =R n K .Claim.For j big enough and divisible by N r we have vol x 2U j k f (x )k K =q j Cq j ;where C is a positive constant.By the above claim we havelim sup i !1 vol x 2U j k f (x )k K =q i 1=i q and henceR =1lim sup i !1[vol (f x 2U j k f (x )k K =q i g )]1=i q :Therefore,since Z U (q s ;f )is a rational function of q s ,we conclude that uq is a pole of Z U (q s ;f ),for some complex N r -th root of the unity u .Proof of the claim.By the p adic change of variables formula [16,Proposition7.4.1]we have (B 1(U )):vol x 2U j k f (x )k K =q j (2.1)vol y 2B j k f (y )k K =q j j (Jac )(y )j K ;8W ILLEM VEYS AND W.A.ZUNIGA-GALINDOwhere Jac is the Jacobian determinant of .With the same reasoning as inthe proof of Theorem2.4we have that k f (y)kK =C1j"j K j y1j N r K on B,whereC1is a positive constant.So on B we have k f (y)kK =q j if and only ifj y1j K=C2q j=N r,where C2is a positive constant.Hence (2.2)vol y2B j k f (y)k K=q j = 1 q 1 C2q j=N r: Note that on this subset of B we have(2.3)j(Jac )(y)jK =j jK j y1j v r 1K=j j K C v r 12q j(v r 1)=N r:Combining(2.1),(2.2)and(2.3)yieldsvol x2U j k f(x)k K=q j Cq j;for some positive constant C.Remark2.8.(1)In[15]Igusa showed in the case l=1that (I f)is a pole of Z U(s;f)for a suitable compact open set U containing the origin.The argument uses Langlands’description of residues in terms of principal value integrals[20]. Furthermore,this argument is valid for archimedean and non-archimedean local zeta functions(see also[2,Théorème5,part3a,page186],[31]).(2)We note that (I f) lct(I f),where lct(I f)is the‘log-canonical threshold’ofI f.This well-known important invariant(see e.g.[19],[25])is de…ned analogously as (I f)but in a geometric setting,i.e.working over an algebraic closure of K. In order to obtain a log-principalization in this context maybe more exceptional components are needed,and then the inequality above could be strict.2.2.1.Number of solutions of polynomial congruences.Suppose that f i(x),i= 1;:::;l,are polynomials with coe¢cients in R K.Let N j(f)be the number of so-lutions of f i(x) 0mod P jK;i=1;:::;l,in R K=P j K n,and let P(t;f)be the series P1j=0N j(f)(q n t)j.The Poincaréseries P(t;f)is related to Z(s;f)by theformula P(t;f)=1 tZ(s;f)1 t;t=q s;(cf.[24,Theorem2]).In the proof of the previous theorem was established that q is the radius of convergence R of Z(s;f) considered as a function in q s.By using this fact,and the above-mentioned relation between P(t;f)and Z(s;f),we obtain the following corollary.Corollary2.9.With the above notation,lim supj!1 N j(f)q nj 1j=q (I f); where (I f)=min n v i N i o,where(N i;v i)runs through the numerical data of a log-principalization :X K !R n K of the ideal I f=(f1;:::;f l).Let d be the maximal order of the poles of P(t;f)with modulus q (I f).As a consequence of the above corollary and of the rationality of P(t;f)we have that N j(f) Cj d 1q(n (I f))j for j big enough,where C is a positive constant.And by Remark2.8(2),we have then that N j(f) Cj d 1q(n lct(I f))j for j big enough.ZETA FUNCTIONS AND NEW TON POLYHEDRA9 2.3.Denef’s explicit formula.For polynomials f1;:::;f l over a number…eld F,we can consider local zeta functions Z W(s;f;K)for all(non-archimedean) completions K of F.When l=1,Denef presented in[6,Theorem3.1]an explicit formula,which is valid simultaneously for almost all these zeta functions.His arguments extend to the several polynomials case,by replacing resolution by log-principalization(as in Theorem2.1).Theorem2.10.Let F be a number…eld and f i(x)2F[x1;:::;x n]for i=1;:::;l. Let :X!A n be a log-principalization of I f=(f1;:::;f l)over F as in Theorem2.1.Denote div( (I f))=P i2T N i E i,and div( (dx1^:::^dx n))= P i2T(v i 1)E i;where E i,i2T,are the irreducible components of the simple normal crossings divisor given by the principal ideal (I f).For every maximal ideal P of the ring of integers of F,we consider the completion K of F with respect to P.Denote the valuation ring and the residue…eld of K by R and K=F q re-spectively.Then for almost all completions K(i.e.for all except a…nite number) we haveZ W(s;f;K)=q n X I T c I Y i2I(q 1)q N i s v i1 q N i s v i;where W R n is a union of cosets mod(P)n,andc I=card a2X K j a2E i K ,i2I;and (a)2W :Here denotes the reduction mod P,for which we refer to[6,Sect.2].Example2.11.Take f1;f2;f3;:::;f M as in Example2.5as being de…ned over a number…eld F.Then the formula of Theorem2.10for W=(P)2yieldsZ0(s;f;K)=q 2(q+1)(q 1)q as 21 q as 2= 1 q 2 q as 21 q as 2:Example2.12.Let K=Q p,f1(x;y)=x,f2(x;y)=x+p0y,where p0is a…xed prime number,and let f=(f1;f2).A direct calculation shows thatZ(s;f;K)=8><>:1 p 21 p 2 s;p=p0;(1 p 1)(1+p 1 s)1 p2 s;p=p0:A log-principalization for the ideal I f is attained by blowing-up the origin.One easily veri…es that the expression for p=p0is the one given by Theorem2.10.As a consequence of Theorem2.4(or[4],[24])Z W(s;f)can be written asZ W(s;f)=P(T);where P(T)and Q(T)are polynomials in T=q s with rational coe¢cients.We de…ne deg Z W(s;f)=deg P(T) deg Q(T),where deg means‘degree’. Corollary2.13.Let f i(x)2F[x1;:::;x n]for i=1;:::;l.For almost all comple-tions K of F we have deg Z(s;f;K) 0and deg Z0(s;f;K)=0.Moreover if all f i are homogeneous of degree d,then deg Z(s;f;K)= d.The proof follows from the explicit formula(Theorem2.10)by analogous argu-ments as in[6](or[16])where the case l=1is treated.We should mention that by using model-theoretic arguments Denef already showed the above result(see10W ILLEM VEYS AND W.A.ZUNIGA-GALINDO[6,Theorem5.2,and Example5.4]).So in this paper we give a geometric proof of this fact.Note that for the case p=p0in Example2.12it is not true that deg Z(s;f;Q p)= 1,though f1,f2are homogeneous of degree1. Example2.14.Let f=(f1;f2)= x3 xy;y .One easily constructs a log-principalization of the ideal I f= x3 xy;y as a composition of three blow-ups.The numerical data of the three exceptional components in 1(supp I f)= 1(0)are(1;2),(2;3),(3;4)respectively.So Theorem2.4yields 2, 3=2, 4=3 as possible(real parts of)candidate poles of Z(s;f).However,in the formula of Theorem2.10the…rst two candidate poles cancel:Z(s;f)=q 2f q2 1 +q(q 1)q 2 s1 q +(q 1)(q 1)q 3 2s1 q+q(q 1)q 4 3s1 q+(q 1)2q 5 3s(1 q 2 s)(1 q 3 2s)+(q 1)2q 7 5s(1 q 3 2s)(1 q 4 3s)g =q 2q 11 q 4 3s q+1+q 1 s+q2 2s :We shall present an alternative formula to compute this example in Section4,where only one candidate pole will appear.Example2.15.Let f=(f1;f2)= y2 x3;y2 z2 .We shall compute Z0(s;f) by means of a log-principalization of I f= y2 x3;y2 z2 .Note that the support of I f has two1 dimensional components C and C0with a singularity at the origin of K3.We…rst blow up the origin yielding the exceptional surface E1 =P2 with (N1;v1)=(2;3).The strict transform of C and C0and E1have one common point.Next we blow up this point obtaining the new exceptional surface E2 =P2 with(N2;v2)=(3;5).At this stage(the strict transforms of)C and C0are disjoint and both meet E2in one point of the intersection of E2with(the strict transform of)E1.Now we blow up the curve E1\E2;the new exceptional component E3is a ruled surface over that curve and(N3;v3)=(6;8).We have that E3\E1and E3\E2are disjoint sections of E3,and C and C0intersect E3transversely outside E3\E1and E3\E2.Finally we blow up C and C0,yielding the last two exceptional surfaces E4and E04with numerical data(1;2).The formula of Theorem2.10yieldsZ0(s;f)=q 3 (q2+q)(q 1)q 3 2s1 q 3 2s +q2(q 1)q 5 3s1 q 5 3s+(q2 3)(q 1)q 8 6s+(q+1)(q 1)2q 11 8s+(q+1)(q 1)2q 13 9s(1 q 5 3s)(1 q 8 6s)+2(q+1)(q 1)2q 10 7s(1 q 2 s)(1 q 8 6s)=q 3(q 1)N(q s)(1 q 2 s)(1 q 8 6s);whereN q s =(q2 q 1)q 10 7s+(q2+q 1)q 8 6s (q+1)q 7 5s+q 4 4s q 4 3s+(q+1)q 2 2s:ZETA FUNCTIONS AND NEW TON POLYHEDRA11 Note that the candidate poles 3=2and 5=3cancel.2.4.Motivic and topological zeta functions.The analogue of the original ex-plicit formula of Denef plays an important role in the study of the motivic zeta function associated to one regular function[8].One can associate more generally a motivic zeta function to any sheaf of ideals on a smooth variety,and obtain a similar formula for it in terms of a log-principalization using the argument of[8]. We just formulate the more general de…nition and formula,referring to e.g.[9], [32]for the notion of jets and Grothendieck ring.De…nition2.16.Let Y be a smooth algebraic variety of dimension n over over a …eld F of characteristic zero,and I a sheaf of ideals on Y.Let W be a subvariety of Y.Denote for i2N by X i;W the variety of i jets on Y with origin in W for which ord t( I)=i.The motivic zeta function associated to I(and W)is the formal power seriesZ W(I;T)=P i 0[X i;W] L n T i;where[ ]denotes the class of a variety in the Grothendieck ring of algebraic varieties over F,and L= A1 .Theorem2.17.Let :X!Y be a log-principalization of I.With the analogous notation E i,N i,v i,(i2T)as before,and also E I:=(\i2I E i)n([k=2I E k)for I T,we haveZ W(I;T)=P I T E I\ 1W Q i2I(L 1)T N iL v i T N i:In particular Z W(I;T)is rational in T.Specializing to topological Euler characteristics,denoted by ( ),as in[8,(2.3)] or[32,(6.6)]we obtain the expressionZ top;W(I;s):=P I T E I\ 1W Q i2I1v i+N i s2Q(s);which is then independent of the chosen log-principalization.(When the base…eld is not the complex numbers,we consider ( )inétale Q-cohomology as in[8].)It can be taken as a de…nition for the topological zeta function associated to I(and W),generalizing the original one of Denef and Loeser associated to one polynomial [10].3.Newton polyhedra and non-degeneracy conditions3.1.Newton polyhedra.We set R+:=f x2R j x>0g.Let G be a nonempty subset of N n.The Newton polyhedron = (G)asso-ciated to G is the convex hull in R n+of the set[m2G m+R n+ .For instance classically one associates a Newton polyhedron(at the origin)to g(x)=P m c m x m (x=(x1;:::;x n),g(0)=0),being a nonconstant polynomial function over K or K analytic function in a neighborhood of the origin,where G=supp(g):= f m2N n j c m=0g.Further we will associate more generally a Newton polyhedron to an analytic mapping.We…x a Newton polyhedron as above.We…rst collect some notions and results about Newton polyhedra that will be used in the next sections.Let h ; i。

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