MATH2061_Linear Mathematics and Vector Calculus_201_tut02s
数学专业英语词汇
数学专业英语词汇相信自己比依赖别人重要。
用尽心机不如静心做事数学 mathematics, maths(BrE), math(AmE) 公理 axiom 定理 theorem 计算calculation 算 operation 证明 prove 假设 hypothesis, hypotheses(pl.) 命题 proposition 算术 arithmetic 加 plus(prep.), add(v.), addition(n.) 被加数 augend, summand 加数 addend 和 sum 减 minus(prep.), subtract(v.), subtraction(n.) 被减数 minuend 减数 subtrahend 差 remainder 乘times(prep.), multiply(v.), multiplication(n.) 被乘数 multiplicand, faciend 乘数 multiplicator 积 product 除 divided by(prep.), divide(v.), division(n.) 被除数 dividend 除数 divisor 商 quotient 等于 equals, is equal to, is equivalent to 大于 is greater than 小于 is lesser than 大于等于 is equal or greater than 小于等于 is equal or lesser than 运算符operator 平均数mean 算术平均数arithmatic mean 几何平均数geometric mean n个数之积的n次方根倒数(reciprocal) x的倒数为1/x 有理数 rational number 无理数 irrational number 实数 real number 虚数 imaginary number 数字 digit 数 number 自然数 natural number 整数 integer 小数 decimal 小数点 decimal point 分数 fraction 分子 numerator 分母 denominator 比ratio 正 positive 负 negative 零 null, zero, nought, nil 十进制 decimal system 二进制 binary system 十六进制 hexadecimal system 权 weight, significance 进位 carry 截尾 truncation 四舍五入 round 下舍入 round down 上舍入 round up 有效数字 significant digit 无效数字 insignificant digit 代数 algebra 公式 formula, formulae(pl.) 单项式 monomial 多项式polynomial, multinomial 系数 coefficient 未知数 unknown, x-factor, y-factor, z-factor 等式,方程式 equation 一次方程 simple equation 二次方程quadratic equation 三次方程 cubic equation 四次方程 quartic equation 不等式 inequation 阶乘 factorial 对数 logarithm 指数,幂 exponent 乘方power 二次方,平方 square 三次方,立方 cube 四次方 the power of four, the fourth power n次方 the power of n, the nth power 开方 evolution, extraction 二次方根,平方根 square root 三次方根,立方根 cube root 四次方根 the root of four, the fourth root n次方根 the root of n, the nth root sqrt(2)=1.414 sqrt(3)=1.732sqrt(5)=2.236 常量 constant 变量 variable 坐标系 coordinates 坐标轴x-axis, y-axis, z-axis 横坐标 x-coordinate 纵坐标 y-coordinate 原点origin 象限quadrant 截距(有正负之分)intercede (方程的)解solution 几何geometry 点 point 线 line 面 plane 体 solid 线段 segment 射线 radial 平行 parallel 相交 intersect 角 angle 角度 degree 弧度 radian锐角 acute angle 直角 right angle 钝角 obtuse angle 平角 straight angle 周角perigon 底 base 边 side 高 height 三角形 triangle 锐角三角形 acute triangle 直角三角形 right triangle 直角边 leg 边 hypotenuse 勾股定理Pythagorean theorem 钝角三角形 obtuse triangle 不等边三角形 scalene triangle 等腰三角形 isosceles triangle 等边三角形 equilateral triangle 四边形 quadrilateral 平行四边形 parallelogram 矩形 rectangle 长 length 宽 width 周长 perimeter 面积 area 相似 similar 全等 congruent 三角trigonometry 正弦 sine 余弦 cosine 正切 tangent 余切 cotangent 正割secant 余割 cosecant 反正弦 arc sine 反余弦 arc cosine 反正切 arc tangent 反余切 arc cotangent 反正割 arc secant 反余割 arc cosecant集合aggregate 元素 element 空集 void 子集 subset 交集 intersection 并集union 补集 complement 映射 mapping 函数 function 定义域 domain, field of definition 值域 range 单调性 monotonicity 奇偶性 parity 周期性periodicity 图象 image 数列,级数 series 微积分 calculus 微分differential 导数 derivative 极限 limit 无穷大 infinite(a.) infinity(n.) 无穷小 infinitesimal 积分 integral 定积分 definite integral 不定积分indefinite integral 复数 complex number 矩阵 matrix 行列式 determinant 圆 circle 圆心 centre(BrE), center(AmE) 半径 radius 直径 diameter 圆周率 pi 弧 arc 半圆 semicircle 扇形 sector 环 ring 椭圆 ellipse 圆周 circumference 轨迹 locus, loca(pl.) 平行六面体parallelepiped 立方体 cube 七面体 heptahedron 八面体 octahedron 九面体enneahedron 十面体 decahedron 十一面体 hendecahedron 十二面体dodecahedron 二十面体 icosahedron 多面体 polyhedron 旋转 rotation 轴axis 球 sphere 半球 hemisphere 底面 undersurface 表面积 surface area 体积 volume 空间 space 双曲线 hyperbola 抛物线 parabola 四面体 tetrahedron 五面体 pentahedron 六面体 hexahedron菱形 rhomb, rhombus, rhombi(pl.), diamond 正方形 square 梯形 trapezoid 直角梯形 right trapezoid 等腰梯形isosceles trapezoid 五边形 pentagon 六边形 hexagon 七边形 heptagon 八边形 octagon 九边形 enneagon 十边形 decagon 十一边形 hendecagon 十二边形dodecagon 多边形 polygon 正多边形 equilateral polygon 相位 phase 周期period 振幅 amplitude 内心 incentre(BrE), incenter(AmE) 外心excentre(BrE), excenter(AmE) 旁心 escentre(BrE), escenter(AmE) 垂心orthocentre(BrE), orthocenter(AmE) 重心 barycentre(BrE), barycenter(AmE) 内切圆 inscribed circle 外切圆 circumcircle 统计 statistics 平均数average 加权平均数 weighted average 方差 variance 标准差 root-mean-square deviation, standard deviation 比例 propotion 百分比 percent 百分点 percentage 百分位数 percentile 排列 permutation 组合 combination 概率,或然率 probability 分布 distribution 正态分布 normal distribution 非正态分布 abnormal distribution 图表 graph 条形统计图 bar graph 柱形统计图 histogram 折线统计图 broken line graph 曲线统计图 curve diagram 扇形统计图 pie diagram abscissa 横坐标 absolute value 绝对值 acute angle 锐角 adjacent angle 邻角 addition 加 algebra 代数 altitude 高 angle bisector 角平分线 arc 弧 area 面积 arithmetic mean 算术平均值(总和除以总数) arithmetic progression 等差数列(等差级数) arm 直角三角形的股 at 总计(乘法) average 平均值 base 底 be contained in 位于...上 bisect 平分center 圆心 chord 弦 circle 圆形 circumference 圆周长 circumscribe 外切,外接 clockwise 顺时针方向 closest approximation 最相近似的combination 组合 common divisor 公约数,公因子 common factor 公因子complementary angles 余角(二角和为90度) composite number 合数(可被除1及本身以外其它的数整除) concentric circle 同心圆 cone 圆锥(体积,1/3*pi*r*r*h) congruent 全等的 consecutive integer 连续的整数coordinate 坐标的 cost 成本 counterclockwise 逆时针方向 cube 1.立方数 2.立方体(体积,a*a*a 表面积,6*a*a) cylinder 圆柱体 decagon 十边形 decimal 小数 decimal point 小数点 decreased 减少 decrease to 减少到 decrease by 减少了 degree 角度 define 1.定义 2.化简 denominator 分母 denote 代表,表示 depreciation 折旧 distance 距离 distinct 不同的 dividend 1. 被除数 2.红利 divided evenly 被除数 divisible 可整除的 division 1.除 2.部分divisor 除数 down payment 预付款,定金 equation 方程 equilateral triangle 等边三角形 even number 偶数 expression 表达 exterior angle 外角face (立体图形的)某一面 factor 因子 fraction 1.分数 2.比例 geometric mean 几何平均值(N个数的乘积再开N次方) geometric progression 等比数列(等比级数) have left 剩余 height 高 hexagon 六边形 hypotenuse 斜边improper fraction 假分数 increase 增加 increase by 增加了 increase to 增加到 inscribe 内切,内接 intercept 截距 integer 整数 interest rate 利率in terms of... 用...表达 interior angle 内角 intersect 相交 irrational无理数 isosceles triangle 等腰三角形 least common multiple 最小公倍数least possible value 最小可能的值 leg 直角三角形的股 length 长 listprice 标价 margin 利润 mark up 涨价 mark down 降价 maximum 最大值 median, medium 中数(把数字按大小排列,若为奇数项,则中间那项就为中数,若为偶数项,则中间两项的算术平均值为中数。
向量四元数和矩阵参考书籍
向量四元数和矩阵参考书籍
以下是一些关于向量、四元数和矩阵的参考书籍:
《3D数学基础》,这本书从坐标系到矩阵变换,讲述了向量、矩阵、四元数、几何变换等内容,并给出了相应的C++代码实现。
《DirectX 3D游戏开发编程基础》,这本书也介绍了向量、矩阵和四元数等数学知识,对于学习图形学的人来说很有用。
《矩阵分析》,这本书系统地介绍了矩阵理论,包括矩阵的QR分解、SVD分解、高斯-赛德尔迭代、特征值求解等内容。
以上书籍仅供参考,建议根据自身需求选择合适的书籍进行阅读。
一个最小二乘问题的三种解法
一个最小二乘问题的三种解法发布时间:2022-08-30T01:58:08.890Z 来源:《教学与研究》2022年第4月第8期作者:肖燏[导读] 线性非齐次方程组无解时,寻找,使得达到极小,此处,实矩阵、向量均已给定. 这是一个高等数学和线肖燏湖南中医药大学信息科学与工程学院湖南长沙 410208)摘要线性非齐次方程组无解时,寻找,使得达到极小,此处,实矩阵、向量均已给定. 这是一个高等数学和线性代数的综合性问题.本文分别从多元函数、向量函数、和向量射影出发,得出这个最小二乘问题的三种解法。
关键词最小二乘多元函数极值向量函数极值向量射影Abstract When there is no solution to the linear non-homogeneous equation system , find , so that reaches the minimum. Here, the real matrix and vector have been given. This is a comprehensive problem of advanced mathematics and linear algebra. Starting from multivariate function, vector function and vector projection, this paper obtains three solutions to the least square problem.Key words least square; extremum of multivariate function; extremum of vector function; projection of vector参考文献[1] 蔡大用,白峰杉. 高等数值分析[M]. 北京:清华大学出版社,1996.[2] 方保镕,周继东,李医民. 矩阵论[M]. 北京:清华大学出版社,2013.[3] 吴赣昌. 高等数学[M]. 北京:中国人民大学出版社,2017.[4] 同济大学数学系. 工程数学.线性代数[M]. 北京:高等教育出版社,2014.作者简介:肖燏(1974—),女,硕士,讲师,研究方向为计算数学.。
中级计量经济学讲义_第二章第一节数学基础 (Mathematics)第一节 矩阵(Matrix)及其二次型(Quadratic Forms)
上课材料之二:第二章 数学基础 (Mathematics)第一节 矩阵(Matrix)及其二次型(Quadratic Forms)第二节 分布函数(Distribution Function),数学期望(Expectation)及方差(Variance) 第三节 数理统计(Mathematical Statistics ) 第一节 矩阵及其二次型(Matrix and its Quadratic Forms)2.1 矩阵的基本概念与运算 一个m ×n 矩阵可表示为:矩阵的加法较为简单,若C=A +B ,c ij =a ij +b ij但矩阵的乘法的定义比较特殊,若A 是一个m ×n 1的矩阵,B 是一个n 1×n 的矩阵,则C =AB 是一个m ×n 的矩阵,而且∑==nk kj ikij b ac 1,一般来讲,AB ≠BA ,但如下运算是成立的:● 结合律(Associative Law ) (AB )C =A (BC ) ● 分配律(Distributive Law ) A (B +C )=AB +AC 问题:(A+B)2=A 2+2AB+B 2是否成立?向量(Vector )是一个有序的数组,既可以按行,也可以按列排列。
行向量(row ve ctor)是只有一行的向量,列向量(column vector)只有一列的向量。
如果α是一个标量,则αA =[αa ij ]。
矩阵A 的转置矩阵(transpose matrix)记为A ',是通过把A 的行向量变成相应的列向量而得到。
显然(A ')′=A ,而且(A +B )′=A '+B ',● 乘积的转置(Transpose of a production ) A B AB ''=')(,A B C ABC '''=')(。
Introduction to Linear Algebra
»a = 5 a= 5
A vector is a mathematical quantity that is completely described by its magnitude and direction. An example of a three dimensional column vector might be 4 b= 3 5 uld easily assign bT to another variable c, as follows:
»c = b' c= 4 3 5
A matrix is a rectangular array of scalars, or in some instances, algebraic expressions which evaluate to scalars. Matrices are said to be m by n, where m is the number of rows in the matrix and n is the number of columns. A 3 by 4 matrix is shown here 2 A= 7 5 5 3 2 3 2 0 6 1 3 (3)
»a = 5;
Here we have used the semicolon operator to suppress the echo of the result. Without this semicolon MATLAB would display the result of the assignment:
»A(2,4) ans = 1
The transpose operator “flips” a matrix along its diagonal elements, creating a new matrix with the ith row being equal to the jth column of the original matrix, e.g. T A = 2 5 3 6 7 3 2 1 5 2 0 3
线性代数英文专业词汇
92
the Gram-Schmidt process
正交化过程
93
reducing a matrix to the diagonal form
对角化矩阵
94
orthonormal basis
标准正交基
95
orthogonal transformation
正交变换
96linear transformation线性变换
矩阵mxn
38
the determinant of matrix A
方阵A的行列式
39
operations on Matrices
矩阵的运算
40
a transposed matrix
转置矩阵
41
an inverse matrix
逆矩阵
42
an conjugate matrix
共轭矩阵
43
an diagonal matrix
《线性代数》英文专业词汇
序号
英文
中 文
1
Linear Algebra
线性代数
2
determinant
行列式
3
row
行
4
column
列
5
element
元素
6
diagonal
对角线
7
principal diagona
主对角线
8
auxiliary diagonal
次对角线
9
transposed determinant
78
augmented matrix
增广矩阵
79
general solution
introduction to linear algebra 每章开头方框-概述说明以及解释
introduction to linear algebra 每章开头方框-概述说明以及解释1.引言1.1 概述线性代数是数学中的一个重要分支,主要研究向量空间和线性变换的性质及其应用。
它作为一门基础学科,在多个领域如物理学、计算机科学以及工程学等都有广泛的应用。
线性代数的研究对象包括向量、向量空间、矩阵、线性方程组等,通过对其性质和运算法则的研究,可以解决诸如解线性方程组、求特征值与特征向量等问题。
线性代数的基本概念包括向量、向量空间和线性变换。
向量是指在空间中具有大小和方向的量,可以表示为一组有序的实数或复数。
向量空间是一组满足一定条件的向量的集合,对于向量空间中的任意向量,我们可以进行加法和数乘运算,得到的结果仍然属于该向量空间。
线性变换是指将一个向量空间映射到另一个向量空间的运算。
线性方程组与矩阵是线性代数中的重要内容。
在实际问题中,常常需要解决多个线性方程组,而矩阵的运算和性质可以帮助我们有效地解决这些问题。
通过将线性方程组转化为矩阵形式,可以利用矩阵的特殊性质进行求解。
线性方程组的解可以具有唯一解、无解或者有无穷多解等情况,而矩阵的行列式和秩等性质能够帮助我们判断线性方程组的解的情况。
向量空间与线性变换是线性代数的核心内容。
向量空间的性质研究可以帮助我们理解向量的运算和性质,以及解释向量空间的几何意义。
线性变换是一种将一个向量空间映射到另一个向量空间的运算,通过线性变换可以将复杂的向量运算问题转化为简单的矩阵运算问题。
在线性变换中,我们需要关注其核、像以及变换的特征等性质,这些性质可以帮助我们理解线性变换的本质和作用。
综上所述,本章节将逐步介绍线性代数的基本概念、线性方程组与矩阵、向量空间与线性变换的相关内容。
通过深入学习和理解这些内容,我们能够掌握线性代数的基本原理和应用,为进一步研究更高级的线性代数问题打下坚实的基础。
1.2文章结构在文章结构部分,我们将介绍本文的组织结构和各章节的内容概述。
[14],[17],[4],[1],[13],[7],[2],[3],[18],[16],[10],[15],[11],[9],[8],[12],and[6].
FORMALIZED MATHEMATICSVolume11,Number4,2003University of BiałystokBanach Space of Absolute SummableReal SequencesYasumasa Suzuki Take,Yokosuka-shiJapanNoboru EndouGifu National College of Technology Yasunari ShidamaShinshu UniversityNaganoSummary.A continuation of[5].As the example of real norm spaces, we introduce the arithmetic addition and multiplication in the set of absolutesummable real sequences and also introduce the norm.This set has the structureof the Banach space.MML Identifier:RSSPACE3.The notation and terminology used here are introduced in the following papers:[14],[17],[4],[1],[13],[7],[2],[3],[18],[16],[10],[15],[11],[9],[8],[12],and[6].1.The Space of Absolute Summable Real SequencesThe subset the set of l1-real sequences of the linear space of real sequences is defined by the condition(Def.1).(Def.1)Let x be a set.Then x∈the set of l1-real sequences if and only if x∈the set of real sequences and id seq(x)is absolutely summable.Let us observe that the set of l1-real sequences is non empty.One can prove the following two propositions:(1)The set of l1-real sequences is linearly closed.(2) the set of l1-real sequences,Zero(the set of l1-real sequences,the linearspace of real sequences),Add(the set of l1-real sequences,the linear space377c 2003University of BiałystokISSN1426–2630378yasumasa suzuki et al.of real sequences),Mult(the set of l1-real sequences,the linear space ofreal sequences) is a subspace of the linear space of real sequences.One can check that the set of l1-real sequences,Zero(the set of l1-real sequences,the linear space of real sequences),Add(the set of l1-real sequences,the linear space of real sequences),Mult(the set of l1-real sequences,the linear space of real sequences) is Abelian,add-associative,ri-ght zeroed,right complementable,and real linear space-like.One can prove the following proposition(3) the set of l1-real sequences,Zero(the set of l1-real sequences,the linearspace of real sequences),Add(the set of l1-real sequences,the linear spaceof real sequences),Mult(the set of l1-real sequences,the linear space ofreal sequences) is a real linear space.The function norm seq from the set of l1-real sequences into R is defined by: (Def.2)For every set x such that x∈the set of l1-real sequences holds norm seq(x)= |id seq(x)|.Let X be a non empty set,let Z be an element of X,let A be a binary operation on X,let M be a function from[:R,X:]into X,and let N be a function from X into R.One can check that X,Z,A,M,N is non empty.Next we state four propositions:(4)Let l be a normed structure.Suppose the carrier of l,the zero of l,theaddition of l,the external multiplication of l is a real linear space.Thenl is a real linear space.(5)Let r1be a sequence of real numbers.Suppose that for every naturalnumber n holds r1(n)=0.Then r1is absolutely summable and |r1|=0.(6)Let r1be a sequence of real numbers.Suppose r1is absolutely summableand |r1|=0.Let n be a natural number.Then r1(n)=0.(7) the set of l1-real sequences,Zero(the set of l1-real sequences,the linearspace of real sequences),Add(the set of l1-real sequences,the linear spaceof real sequences),Mult(the set of l1-real sequences,the linear space ofreal sequences),norm seq is a real linear space.The non empty normed structure l1-Space is defined by the condition (Def.3).(Def.3)l1-Space= the set of l1-real sequences,Zero(the set of l1-real sequences,the linear space of real sequences),Add(the set of l1-realsequences,the linear space of real sequences),Mult(the set of l1-realsequences,the linear space of real sequences),norm seq .banach space of absolute summable (379)2.The Space is Banach SpaceOne can prove the following two propositions:(8)The carrier of l1-Space=the set of l1-real sequences and for every set xholds x is an element of l1-Space iffx is a sequence of real numbers andid seq(x)is absolutely summable and for every set x holds x is a vectorof l1-Space iffx is a sequence of real numbers and id seq(x)is absolutelysummable and0l1-Space=Zeroseq and for every vector u of l1-Space holdsu=id seq(u)and for all vectors u,v of l1-Space holds u+v=id seq(u)+id seq(v)and for every real number r and for every vector u of l1-Spaceholds r·u=r id seq(u)and for every vector u of l1-Space holds−u=−id seq(u)and id seq(−u)=−id seq(u)and for all vectors u,v of l1-Spaceholds u−v=id seq(u)−id seq(v)and for every vector v of l1-Space holdsid seq(v)is absolutely summable and for every vector v of l1-Space holdsv = |id seq(v)|.(9)Let x,y be points of l1-Space and a be a real number.Then x =0iffx=0l1-Space and0 x and x+y x + y and a·x =|a|· x .Let us observe that l1-Space is real normed space-like,real linear space-like, Abelian,add-associative,right zeroed,and right complementable.Let X be a non empty normed structure and let x,y be points of X.The functorρ(x,y)yields a real number and is defined by:(Def.4)ρ(x,y)= x−y .Let N1be a non empty normed structure and let s1be a sequence of N1.We say that s1is CCauchy if and only if the condition(Def.5)is satisfied. (Def.5)Let r2be a real number.Suppose r2>0.Then there exists a natural number k1such that for all natural numbers n1,m1if n1 k1and m1k1,thenρ(s1(n1),s1(m1))<r2.We introduce s1is Cauchy sequence by norm as a synonym of s1is CCauchy.In the sequel N1denotes a non empty real normed space and s2denotes a sequence of N1.We now state two propositions:(10)s2is Cauchy sequence by norm if and only if for every real number rsuch that r>0there exists a natural number k such that for all naturalnumbers n,m such that n k and m k holds s2(n)−s2(m) <r.(11)For every sequence v1of l1-Space such that v1is Cauchy sequence bynorm holds v1is convergent.References[1]Grzegorz Bancerek.The ordinal numbers.Formalized Mathematics,1(1):91–96,1990.[2]Czesław Byliński.Functions and their basic properties.Formalized Mathematics,1(1):55–65,1990.380yasumasa suzuki et al.[3]Czesław Byliński.Functions from a set to a set.Formalized Mathematics,1(1):153–164,1990.[4]Czesław Byliński.Some basic properties of sets.Formalized Mathematics,1(1):47–53,1990.[5]Noboru Endou,Yasumasa Suzuki,and Yasunari Shidama.Hilbert space of real sequences.Formalized Mathematics,11(3):255–257,2003.[6]Noboru Endou,Yasumasa Suzuki,and Yasunari Shidama.Real linear space of real sequ-ences.Formalized Mathematics,11(3):249–253,2003.[7]Krzysztof Hryniewiecki.Basic properties of real numbers.Formalized Mathematics,1(1):35–40,1990.[8]Jarosław Kotowicz.Monotone real sequences.Subsequences.Formalized Mathematics,1(3):471–475,1990.[9]Jarosław Kotowicz.Real sequences and basic operations on them.Formalized Mathema-tics,1(2):269–272,1990.[10]Jan Popiołek.Some properties of functions modul and signum.Formalized Mathematics,1(2):263–264,1990.[11]Jan Popiołek.Real normed space.Formalized Mathematics,2(1):111–115,1991.[12]Konrad Raczkowski and Andrzej Nędzusiak.Series.Formalized Mathematics,2(4):449–452,1991.[13]Andrzej Trybulec.Subsets of complex numbers.To appear in Formalized Mathematics.[14]Andrzej Trybulec.Tarski Grothendieck set theory.Formalized Mathematics,1(1):9–11,1990.[15]Wojciech A.Trybulec.Subspaces and cosets of subspaces in real linear space.FormalizedMathematics,1(2):297–301,1990.[16]Wojciech A.Trybulec.Vectors in real linear space.Formalized Mathematics,1(2):291–296,1990.[17]Zinaida Trybulec.Properties of subsets.Formalized Mathematics,1(1):67–71,1990.[18]Edmund Woronowicz.Relations and their basic properties.Formalized Mathematics,1(1):73–83,1990.Received August8,2003。
MATH2061_Linear Mathematics and Vector Calculus_201_tut06s
Tutorial 6 (week 7) — Solutions
Page 4
If λ = 6 then we solve (A − 6I )v = 0:
−3 3 4 0 −4 0
− − − − − →
Row reduce
1 0
4 −3 0
0 0
.
So, a basis for the 6-eigenspace of A is ( 4 3) . Therefore, the general solution of the differential equation X′ = AX is
− − − − − →
Row reduce
1 0
1 0 0 0
.
1 Hence, a basis for the −1–eigenspace of A is ( − 1) .
Math 2061: Tutorial 6 (week 7) — Solutions Page 3
Linear Mathematics
xn+2 xn+1 xn
= a · (−1)n
1 −1 1
+ b · 1n
1 1 1
+ c · 2n4 Nhomakorabea 1,
for some constants a, b, c. So xn = a · (−1)n + b + c · 2n . Now, x0 = 0, x1 = 1 and x2 = 1 so we have the following equations: a+b+c=0 − a + b + 2c = 1 a + b + 4c = 1
线性代数(linearalgebra)
线性代数(linear algebra)Linear algebra (Linear Algebra) is a branch of mathematics. Its research objects are vectors, vector spaces (or linear spaces), linear transformations and finite dimensional linear equations. Vector space is an important subject in modern mathematics. Therefore, linear algebra is widely used in abstract algebra and functional analysis. Linear algebra can be expressed concretely by analytic geometry. The theory of linear algebra has been generalized to operator theory. Since nonlinear models in scientific research can often be approximated as linear models, linear algebra has been widely applied to natural and social sciences.The development of linear algebraBecause the work of Descartes and Fermat, linear algebra basically appeared in seventeenth Century. Until the late eighteenth Century, the field of linear algebra was confined to planes and spaces. The first half of nineteenth Century to complete the transition matrix to the n-dimensional vector space theory begins with Kailai in the second half of nineteenth Century, because if when work reached its culmination in.1888, Peano axiomatically defined finite or infinite dimensional vector space. Toeplitz will be the main theorem is generalized to arbitrary body linear algebra on the general vector space. The concept of linear mapping can in most cases get rid of matrix computation directed to the inherent reasoning, that is not dependent on the selection of the base. Do not exchange and exchange or not with the ring as the operator domain, this concept to die, this concept very significantly extended vector space theory and re organize the nineteenth Century Instituteof the.The word "algebra" appeared relatively late in China, in the Qing Dynasty when the incoming China, it was translated into "Alj Bala", until 1859, the Qing Dynasty famous mathematician, translator Li Shanlan translated it as "algebra", still in use.The status of linear algebraLinear algebra is a subject that discusses matrix theory and finite dimensional vector spaces combined with matrices and their linear transformation theory.The main theory is mature in nineteenth Century, and the first cornerstone (the solution of two or three Yuan linear equations) appeared as early as two thousand years ago (see in our ancient mathematical masterpiece "nine chapters arithmetic").The linear algebra has many important applications in mathematics, mechanics, physics and technology, so it has important place in various branches of algebra;In the computer today, computer graphics, computer aided design, cryptography, virtual reality and so on are all part of the theory and algorithm of linear algebra;.Between geometric and algebraic methods embodied in the concept of the subject of the connection from the axiomatic method on the abstract concept and rigorous logic reasoning, cleverly summed up, to strengthen people's training in mathematics, science and intelligent gain is very useful;And with the development of science, we should not only study the relationship between the individual variables, but also further study the relationship between multiple variables, all kinds of practical problems in most cases can be linearized, and because of the development of the computer, the linearized problem can be calculated, linear algebra is a powerful tool to solve these problems.Basic introduction to linear algebraLinear algebra originated from the study of two-dimensional and three-dimensional Cartesian coordinate systems. Here, a vector is a line segment with a direction that is represented by both length and direction. Thus vectors can be used to represent physical quantities, such as force, or to add and multiply scalar quantities. This is the first example of a real vector space.Modern linear algebra has been extended to study arbitrary or infinite dimensional spaces. A vector space of dimension n is called n-dimensional space. In two-dimensional andthree-dimensional space, most useful conclusions can be extended to these high-dimensional spaces. Although many people do not easily imagine vectors in n-dimensional space, such vectors (i.e., n tuples) are very useful for representing data. Since n is a tuple, and the vector is an ordered list of n elements, most people can effectively generalize and manipulate data in this framework. For example, in economics, 8 dimensional vectors can be used to represent the gross national product (GNP) of 8 countries. When all the nationalorder (such as scheduled, China, the United States, Britain, France, Germany, Spain, India, Australia), you can use the vector (V1, V2, V3, V4, V5, V6, V7, V8) showed that these countries a year each GNP. Here, each country's GNP are in their respective positions.As a purely abstract concept used in proving theorems, vector spaces (linear spaces) are part of abstract algebra and have been well integrated into this field. Some notable examples are: irreversible linear maps or groups of matrices, rings of linear mappings in vector spaces. Linear algebra also plays an important role in mathematical analysis,Especially in vector analysis, higher order derivatives are described, and tensor product and commutative mapping are studied.A vector space is defined on a domain, such as a real or complex domain. Linear operators map the elements of a linear space into another linear space (or in the same linear space), and maintain the consistency of addition and scalar multiplication in the vector space. The set of all such transformations is itself a vector space. If a basis of linear space is determined, all linear transformations can be expressed as a table, called matrix. Further studies of matrix properties and matrix algorithms (including determinants and eigenvectors) are also considered part of linear algebra.We can simply say that the linear problems in Mathematics - those that exhibit linear problems - are most likely to be solved. For example, differential calculus studies the problemof linear approximation of functions. In practice, the difference between a nonlinear problem and a nonlinear one is very important.The linear algebra method refers to the problem of using a linear viewpoint to describe it and to describe it in the language of linear algebra and to solve it (when necessary) by using matrix operations. This is one of the most important applications in mathematics and engineering.Some useful theoremsEvery linear space has a base.The nonzero matrix n for a row of N rows A, if there is a matrix B that makes AB = BA = I (I is the unit matrix), then A is nonsingular matrix.A matrix is nonsingular if and only if its determinant is not zero.A matrix is nonsingular if and only if the linear transformation it represents is a automorphism.A matrix is semi positive if and only if each of its eigenvalues is greater than or equal to zero.A matrix is positive if and only if each of its eigenvalues is greater than zero.Generalizations and related topicsLinear algebra is a successful theory, and its method has been applied to other branches of mathematics.The theory of modulus is to study the substitution of scalar domains in linear algebra by ring substitution.Multilinear algebra transforms the "multivariable" problem of mapping into the problem of each variable, resulting in the concept of tensor.In the spectral theory of operators, by using mathematical analysis, infinite dimensional matrices can be controlled.All of these areas have very large technical difficulties.Basic contents of linear algebra in Chinese UniversitiesFirst, the nature and tasks of the courseThe course of linear algebra is an important basic theory course required by students of science and Engineering in universities and colleges. It is widely used in every field of science and technology. Especially today, with the development and popularization of computer, linear algebra has become the basic theory knowledge and important mathematical tool for engineering students. Linear algebra is to train thehigh-quality specialized personnel needed for the socialist modernization construction of our country. Through the study of this course, we should make students get:1 determinant2, matrix3. The correlation of vectors and the rank of matrices4 、 linear equations5, similar matrix and two typeAnd other basic concepts, basic theories and basic operational skills, and lay the necessary mathematical foundation for further courses and further knowledge of mathematics.While imparting knowledge through various teaching links gradually cultivate students with abstract thinking ability, logical reasoning ability, spatial imagination ability and self-learning ability, but also pay special attention to cultivate students with good operation ability and comprehensive use of the knowledge to the ability to analyze and solve problems.Two, the content of the course teaching, basic requirements and class allocation(1) teaching content1 determinant(1) definition of order n determinant(2) the nature of determinant(3) the calculation of the determinant is carried out in rows (columns)(4) the Clem rule for solving linear equations2, matrix(1) the concept of matrix, unit matrix, diagonal matrix, symmetric matrix(2) linear operations, multiplication operations, transpose operations and laws of matrices(3) inverse matrix concept and its properties, and inverse matrix with adjoint matrix(4) the operation of partitioned matrices3 vector(1) the concept of n-dimensional vectors(2) the linear correlation, linear independence definition and related theorems of vector groups, and the judgement of linear correlation(3) the maximal independent group of vectors and the rank of vectors(4) the concept of rank of matrix(5) elementary transformation of matrix, rank and inverse matrix of matrix by elementary transformation(6) n-dimensional vector spaces and subspaces, bases, dimensions, coordinates of vectors4 、 linear equations(1) the necessary and sufficient conditions for the existence of nonzero solutions of homogeneous linear equations and the necessary and sufficient conditions for the existence of solutions of nonhomogeneous linear equations(2) the fundamental solution, the general solution and the solution structure of the system of linear equations(3) the condition and judgement of the solution of nonhomogeneous linear equations and the solution of the system of equations(4) finding the general solution of linear equations by elementary row transformation5, similar matrix and two type(1) eigenvalues and eigenvectors of matrices and their solutions(2) similarity matrix and its properties(3) the necessary and sufficient conditions and methods of diagonalization of matrices(4) similar diagonal matrices of real symmetric matrices(5) two type and its matrix representation(6) the method of linearly independent vector group orthogonal normalization(7) the concept and property of orthogonal transformation and orthogonal matrix(8) orthogonal transformation is used as the standard shape of the two type(9) the canonical form of quadratic form and two form of two type are formulated by formula(10) the inertia theorem, the rank of the two type, the positive definite of the two type and their discrimination(two) basic requirements1, understand the definition of order n determinant, will use the definition of simple determinant calculation2, master the basic calculation methods and properties of determinant3, master Clem's law4. Understand the definition of a matrix5, master the matrix operation method and inverse matrix method6. Understanding the concept of vector dependency defines the relevance of the vector by definition7, grasp the method of finding the rank of the matrix, and understand the relation between the rank of the matrix and the correlation of the vector group8, understand the concept of vector space, will seek vector coordinates9. Master the matrix rank and inverse matrix with elementary transformation, and solve the system of linear equations10, master the method of solving linear equations, and know the simple application of linear equations11. Master the method of matrix eigenvalue and eigenvector12. Grasp the concept of similar matrices and the concept of diagonalization of matrices13, master the orthogonal transformation of two times for standard type method14, understand the inertia theorem of the two type, and use thematching method to find the sum of squares of the two type15. Grasp the concept and application of the positive definiteness of the two typeMATLABIt is a programming language and can be used as a teaching software for engineering linear algebra. It has been introduced into many university textbooks at home and abroad.。
introduction to linear algebra 5th edition 中译 -回复
introduction to linear algebra 5th edition 中译-回复[introduction to linear algebra 5th edition 中译]的主题:线性代数入门文章长度:1500-2000字第一步:了解线性代数的定义和重要性在数学中,线性代数是研究向量空间和线性映射的分支。
它研究线性方程组、向量空间的结构和变换,是数学中一个重要的分支领域。
线性代数在许多数学和科学领域的应用非常广泛,如工程学、物理学、经济学等。
[introduction to linear algebra 5th edition中译]提供了深入了解线性代数的基础。
第二步:概述[introduction to linear algebra 5th edition 中译] 的内容[introduction to linear algebra 5th edition 中译]主要介绍了线性代数中的基本概念和理论,以及其在实际问题中的应用。
书中包含了向量、矩阵、线性变换、特征值与特征向量等重要内容。
第三步:深入探讨书中主要概念和理论1. 向量:向量是线性代数中的基本对象,它可以用来表示在空间中的方向和大小。
向量可以进行加法和数乘操作,同时还有一些特殊的向量,如零向量和单位向量。
2. 矩阵:矩阵是线性代数中的另一个重要概念,它是由一个或多个向量组成的矩形数组。
矩阵可以用来表示线性变换、解线性方程组等问题。
矩阵的乘法和转置操作是线性代数中常用的操作。
3. 线性变换:线性变换是将一个向量空间映射为另一个向量空间的变换。
线性变换具有保持加法和数乘运算的特性,可以用矩阵来表示。
线性变换在图像处理、信号处理等领域有广泛应用。
4. 特征值与特征向量:特征值与特征向量是描述线性变换的重要概念。
特征值代表了线性变换对应的变换方向,而特征向量代表了这个方向上的变换量。
特征值与特征向量在物理学和计算机图形学中有广泛应用。
mathematics计算李奇张量的代码
mathematics计算李奇张量的代码李奇张量(Riemann curvature tensor)是描述黎曼时空曲率的重要工具。
下面是一个用Python计算李奇张量的代码示例:```pythonimport numpy as npdef calc_riemann_curvature_metric(gamma):"""计算李奇张量的函数"""n = gamma.shape[0]riemann = np.zeros((n, n, n, n))for i in range(n):for j in range(i, n):for k in range(n):for l in range(k, n):term1 = np.dot(gamma[i,l,:], gamma[k,j,:]) - np.dot(gamma[i,k,:], gamma[l,j,:])term2 = np.dot(gamma[k,l,:], gamma[i,j,:]) - np.dot(gamma[k,i,:], gamma[l,j,:])riemann[i,j,k,l] = term1 - term2 + np.dot(gamma[k,j,:], gamma[i,l,:]) - np.dot(gamma[k,i,:], gamma[j,l,:])riemann[j,i,k,l] = -riemann[i,j,k,l]riemann[i,j,l,k] = -riemann[i,j,k,l]riemann[j,i,l,k] = riemann[i,j,k,l]return riemann```这段代码中,`gamma`是时空度规,它是一个大小为$n\times n\times n$的三维数组,表示$n$维时空的度规。
使用`np.dot`函数可以计算两个向量的点积。
运行这段代码,会返回一个大小为$n\times n\times n\times n$的四维数组,表示李奇张量。
练习数学的英语单词
练习数学的英语单词Mathematics is a subject that is rich with its own terminology, which is essential for anyone looking to delve into the world of numbers, equations, and theorems. Whether you're a student, a teacher, or just someone who enjoys the logic and beauty of math, knowing the right vocabulary canhelp you understand and communicate concepts more effectively. Here's a list of some key English words used in mathematics:1. Algebra - The branch of mathematics concerning the studyof the rules of operations and relations, and theconstruction of formulas and equations.2. Arithmetic - The branch of mathematics dealing with the basic operations of addition, subtraction, multiplication,and division.3. Calculus - A branch of mathematics that deals with the finding and properties of derivatives and integrals.4. Geometry - The branch of mathematics that deals with the study of points, lines, angles, surfaces, and solids.5. Trigonometry - A branch of mathematics that deals with the relationships between the angles and sides of triangles.6. Statistics - The branch of mathematics dealing with the collection, analysis, interpretation, presentation, andorganization of data.7. Number Theory - The study of the properties and relationships of numbers, particularly integers.8. Combinatorics - The study of finite or countable discrete structures.9. Euclidean Geometry - A mathematical system attributed to the ancient Greek mathematician Euclid, which he described in his work "Elements".10. Non-Euclidean Geometry - Any of several geometries that differ from Euclidean geometry in that they are based on axioms that are different from Euclid's parallel postulate.11. Algorithm - A step-by-step procedure for calculations or other problem-solving operations, especially an established, recursive computational procedure for calculating a function.12. Theorem - A statement or formula that has been established by mathematical proof.13. Hypothesis - A proposed explanation for an occurrence, which can be tested by experiment or further investigation.14. Variable - A symbol that represents a quantity that can change or vary.15. Constant - A quantity that does not change its value in a particular context.16. Function - A relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output.17. Equation - A statement that asserts the equality of two expressions, which are connected by an equals sign.18. Inequality - A mathematical statement that compares two expressions that are not equal.19. Factor - A number or algebraic expression that divides another without leaving a remainder.20. Prime Number - A natural number greater than 1 that has no positive divisors other than 1 and itself.21. Fibonacci Sequence - A series of numbers in which each number is the sum of the two preceding ones, often starting with 0 and 1.22. Pi (π) - The ratio of the circumference of a circle to its diameter, approximately equal to 3.14159.23. Square Root - A number that, when squared, equals another number.24. Cube Root - A number that, when cubed, equals another number.25. Quadratic Equation - An equation of the second degree,with one unknown.26. Linear Equation - An equation of the first degree, with one unknown.27. Derivative - The rate at which a function changes at a certain point.28. Integral - The antiderivative or the area under the curve of a function.29. Limit - The value that a function or sequence "approaches" as the input or index approaches some value.30. Vector - A quantity that has both magnitude and direction.By familiarizing yourself with these terms, you can enhance your mathematical vocabulary and deepen your understanding of the subject.。
线性代数英文单词
线性代数英语词汇大集合========================================================================= Aadjont(adjugate) of matrix A A 的伴随矩阵augmented matrix A 的增广矩阵Bblock diagonal matrix 块对角矩阵block matrix 块矩阵basic solution set 基础解系CCauchy-Schwarz inequality 柯西- 许瓦兹不等式characteristic equation 特征方程characteristic polynomial 特征多项式coffcient matrix 系数矩阵cofactor 代数余子式cofactor expansion 代数余子式展开column vector 列向量commuting matrices 交换矩阵consistent linear system 相容线性方程组Cramer's rule 克莱姆法则Cross- product term 交叉项DDeterminant 行列式Diagonal entries 对角元素Diagonal matrix 对角矩阵Dimension of a vector space V 向量空间V 的维数Eechelon matrix 梯形矩阵eigenspace 特征空间eigenvalue 特征值eigenvector 特征向量eigenvector basis 特征向量的基elementary matrix 初等矩阵elementary row operations 行初等变换Ffull rank 满秩fundermental set of solution 基础解系Ggrneral solution 通解Gram-Schmidt process 施密特正交化过程Hhomogeneous linear equations 齐次线性方程组Iidentity matrix 单位矩阵inconsistent linear system 不相容线性方程组indefinite matrix 不定矩阵indefinit quatratic form 不定二次型infinite-dimensional space 无限维空间inner product 内积inverse of matrix A 逆矩阵JKLlinear combination 线性组合linearly dependent 线性相关linearly independent 线性无关linear transformation 线性变换lower triangular matrix 下三角形矩阵Mmain diagonal of matrix A 矩阵的主对角matrix 矩阵Nnegative definite quaratic form 负定二次型negative semidefinite quadratic form 半负定二次型nonhomogeneous equations 非齐次线性方程组nonsigular matrix 非奇异矩阵nontrivial solution 非平凡解norm of vector V 向量V 的范数normalizing vector V 规范化向量Oorthogonal basis 正交基orthogonal complemen t 正交补orthogonal decomposition 正交分解orthogonally diagonalizable matrix 矩阵的正交对角化orthogonal matrix 正交矩阵orthogonal set 正交向量组orthonormal basis 规范正交基orthonomal set 规范正交向量组Ppartitioned matrix 分块矩阵positive definite matrix 正定矩阵positive definite quatratic form 正定二次型positive semidefinite matrix 半正定矩阵positive semidefinite quadratic form 半正定二次型Qquatratic form 二次型Rrank of matrix A 矩阵A 的秩r(A )reduced echelon matrix 最简梯形阵row vector 行向量Sset spanned by { } 由向量{ } 所生成similar matrices 相似矩阵similarity transformation 相似变换singular matrix 奇异矩阵solution set 解集合standard basis 标准基standard matrix 标准矩阵Isubmatrix 子矩阵subspace 子空间symmetric matrix 对称矩阵Ttrace of matrix A 矩阵A 的迹tr ( A )transpose of A 矩阵A 的转秩triangle inequlity 三角不等式trivial solution 平凡解Uunit vector 单位向量upper triangular matrix 上三角形矩阵Vvandermonde matrix 范得蒙矩阵vector 向量vector space 向量空间WZzero subspace 零子空间zero vector 零空间==============================================================================向量:vector 向量的长度(模):零向量: zero vector负向量: 向量的加法:addition 三角形法则:平行四边形法则:多边形法则减法向量的标量乘积:scalar multiplication 向量的线性运算线性组合:linear combination 线性表示,线性相关(linearly dependent),线性无关(linearly independent),原点(origin)位置向量(position vector)线性流形(linear manifold)线性子空间(linear subspace)基(basis)仿射坐标(affine coordinates),仿射标架(affine frame),仿射坐标系(affine coordinate system)坐标轴(coordinate axis)坐标平面卦限(octant)右手系左手系定比分点线性方程组(system of linear equations齐次线性方程组(system of homogeneous linear equations)行列式(determinant)维向量向量的分量(component)向量的相等和向量零向量负向量标量乘积维向量空间(vector space)自然基行向量(row vector)列向量(column vector)单位向量(unit vector)直角坐标系(rectangular coordinate system),直角坐标(rectangular coordinates),射影(projection)向量在某方向上的分量,正交分解,向量的夹角,内积(inner product)标量积(scalar product),数量积,方向的方向角,方向的方向余弦;二重外积外积(exterior product),向量积(cross product),混合积(mixed product,scalar triple product)==================================================================================(映射(mapping)),(象(image)),(一个原象(preimage)),(定义域(domain)),(值域(range)),(变换(transformation)),(单射(injection)),(象集),(满射(surjection)),(一一映射,双射(bijection)),(原象),(映射的复合,映射的乘积),(恒同映射,恒同变换(identity mapping)),(逆映射(inverse mapping));(置换(permutation)),(阶对称群(symmetric group)),(对换(transposition)),(逆序对),(逆序数),(置换的符号(sign)),(偶置换(even permutation)),(奇置换(odd permutation));行列式(determinant),矩阵(matrix),矩阵的元(entry),(方阵(square matrix)),(零矩阵(zero matrix)),(对角元),(上三角形矩阵(upper triangular matrix)),(下三角形矩阵(lower triangular matrix)),(对角矩阵(diagonal matrix)),(单位矩阵(identity matrix)),转置矩阵(transpose matrix),初等行变换(elementary row transformation),初等列变换(elementary column transformation);(反称矩阵(skew-symmetric matrix));子矩阵(submatrix),子式(minor),余子式(cofactor),代数余子式(algebraic cofactor),(范德蒙德行列式(Vandermonde determinant));(未知量),(系数矩阵),(方程的系数(coefficient)),(常数项(constant)),(线性方程组的解(solution)),(增广矩阵(augmented matrix)),(零解);子式的余子式,子式的代数余子式===================================================================================线性方程组与线性子空间(阶梯形方程组),(方程组的初等变换),行阶梯矩阵(row echelon matrix),主元,简化行阶梯矩阵(reduced row echelon matrix),(高斯消元法(Gauss elimination)),(解向量),(同解),(自反性(reflexivity)),(对称性(symmetry)),(传递性(transitivity)),(等价关系(equivalence));(齐次线性方程组的秩(rank));(主变量),(自由位置量),(一般解),向量组线性相关,向量组线性无关,线性组合,线性表示,线性组合的系数,(向量组的延伸组);线性子空间,由向量组张成的线性子空间;基,坐标,(自然基),向量组的秩;(解空间),线性子空间的维数(dimension),齐次线性方程组的基础解系(fundamental system of solutions);(平面束(pencil of planes))(导出组),线性流形,(方向子空间),(线性流形的维数),(方程组的特解);(方程组的零点),(方程组的图象),(平面的一般方程),(平面的三点式方程),(平面的截距式方程),(平面的参数方程),(参数),(方向向量);(直线的方向向量),(直线的参数方程),(直线的标准方程),(直线的方向系数),(直线的两点式方程),(直线的一般方程);=====================================================================================矩阵的秩与矩阵的运算线性表示,线性等价,极大线性无关组;(行空间,列空间),行秩(row rank),列秩(column rank),秩,满秩矩阵,行满秩矩阵,列满秩矩阵;线性映射(linear mapping),线性变换(linear transformation),线性函数(linear function);(零映射),(负映射),(矩阵的和),(负矩阵),(线性映射的标量乘积),(矩阵的标量乘积),(矩阵的乘积),(零因子),(标量矩阵(scalar matrix)),(矩阵的多项式);(退化的(degenerate)方阵),(非退化的(non-degenerate)方阵),(退化的线性变换),(非退化的线性变换),(逆矩阵(inverse matrix)),(可逆的(invertible),(伴随矩阵(adjoint matrix));(分块矩阵(block matrix)),(分块对角矩阵(block diagonal matrix));初等矩阵(elementary matrix),等价(equivalent);(象空间),(核空间(kernel)),(线性映射的秩),(零化度(nullity))==================================================================================== transpose of matrix 倒置矩阵; 转置矩阵【数学词汇】transposed matrix 转置矩阵【机械专业词汇】matrix transpose 矩阵转置【主科技词汇】transposed inverse matrix 转置逆矩阵【数学词汇】transpose of a matrix 矩阵的转置【主科技词汇】permutation matrix 置换矩阵; 排列矩阵【主科技词汇】singular matrix 奇异矩阵; 退化矩阵; 降秩矩阵【主科技词汇】unitary matrix 单式矩阵; 酉矩阵; 幺正矩阵【主科技词汇】Hermitian matrix 厄密矩阵; 埃尔米特矩阵; 艾米矩阵【主科技词汇】inverse matrix 逆矩阵; 反矩阵; 反行列式; 矩阵反演; 矩阵求逆【主科技词汇】matrix notation 矩阵符号; 矩阵符号表示; 矩阵记号; 矩阵运算【主科技词汇】state transition matrix 状态转变矩阵; 状态转移矩阵【航海航天词汇】torque master 转矩传感器; 转矩检测装置【主科技词汇】spin matrix 自旋矩阵; 旋转矩阵【主科技词汇】moment matrix 动差矩阵; 矩量矩阵【航海航天词汇】Jacobian matrix 雅可比矩阵; 导数矩阵【主科技词汇】relay matrix 继电器矩阵; 插接矩阵【主科技词汇】matrix notation 矩阵表示法; 矩阵符号【航海航天词汇】permutation matrix 置换矩阵【航海航天词汇】transition matrix 转移矩阵【数学词汇】transition matrix 转移矩阵【机械专业词汇】transitionmatrix 转移矩阵【航海航天词汇】transition matrix 转移矩阵【计算机网络词汇】transfer matrix 转移矩阵【物理词汇】rotation matrix 旋转矩阵【石油词汇】transition matrix 转换矩阵【主科技词汇】circulant matrix 循环矩阵; 轮换矩阵【主科技词汇】payoff matrix 报偿矩阵; 支付矩阵【主科技词汇】switching matrix 开关矩阵; 切换矩阵【主科技词汇】method of transition matrices 转换矩阵法【航海航天词汇】stalling torque 堵转力矩, 颠覆力矩, 停转转矩, 逆转转矩【航海航天词汇】thin-film switching matrix 薄膜转换矩阵【航海航天词汇】rotated factor matrix 旋转因子矩阵【航海航天词汇】transfer function matrix 转移函数矩阵【航海航天词汇】transition probability matrix 转移概率矩阵【主科技词汇】energy transfer matrix 能量转移矩阵【主科技词汇】fuzzy transition matrix 模糊转移矩阵【主科技词汇】canonical transition matrix 规范转移矩阵【主科技词汇】matrix form 矩阵式; 矩阵组织【主科技词汇】stochastic state transition matrix 随机状态转移矩阵【主科技词汇】fuzzy state transition matrix 模糊状态转移矩阵【主科技词汇】matrix compiler 矩阵编码器; 矩阵编译程序【主科技词汇】test matrix 试验矩阵; 测试矩阵; 检验矩阵【主科技词汇】matrix circuit 矩阵变换电路; 矩阵线路【主科技词汇】reducible matrix 可简化的矩阵; 可约矩阵【主科技词汇】matrix norm 矩阵的模; 矩阵模; 矩阵模量【主科技词汇】rectangular matrix 矩形矩阵; 长方形矩阵【主科技词汇】running torque 额定转速时的转矩; 旋转力矩【航海航天词汇】transposed matrix 转置阵【数学词汇】covariance matrix 协变矩阵; 协方差矩阵【主科技词汇】unreduced matrix 未约矩阵; 不可约矩阵【主科技词汇】receiver matrix 接收机矩阵; 接收矩阵变换电路【主科技词汇】torque 传动转矩; 转矩; 阻力矩【航海航天词汇】pull-in torque 启动转矩; 输入转矩, 同步转矩, 整步转矩【航海航天词汇】parity matrix 奇偶校验矩阵; 一致校验矩阵【主科技词汇】bus admittance matrix 母线导纳矩阵; 节点导纳矩阵【主科技词汇】matrix printer 矩阵式打印机; 矩阵形印刷机; 点阵打印机【主科技词汇】dynamic matrix 动力矩阵; 动态矩阵【航海航天词汇】connection matrix 连接矩阵; 连通矩阵【主科技词汇】characteristic matrix 特征矩阵; 本征矩阵【主科技词汇】regular matrix 正则矩阵; 规则矩阵【主科技词汇】flexibility matrix 挠度矩阵; 柔度矩阵【主科技词汇】citation matrix 引文矩阵; 引用矩阵【主科技词汇】relational matrix 关系矩阵; 联系矩阵【主科技词汇】eigenmatrix 本征矩阵; 特征矩阵【主科技词汇】system matrix 系统矩阵; 体系矩阵【主科技词汇】system matrix 系数矩阵; 系统矩阵【航海航天词汇】recovery diode matrix 恢复二极管矩阵; 再生式二极管矩阵【主科技词汇】inverse of a square matrix 方阵的逆矩阵【主科技词汇】torquematic transmission 转矩传动装置【石油词汇】torque balancing device 转矩平衡装置【航海航天词汇】torque measuring device 转矩测量装置【主科技词汇】torque measuring apparatus 转矩测量装置【航海航天词汇】torque-tube type suspension 转矩管式悬置【主科技词汇】steering torque indicator 转向力矩测定仪; 转向转矩指示器【主科技词汇】magnetic dipole moment matrix 磁偶极矩矩阵【主科技词汇】matrix addressing 矩阵寻址; 矩阵寻址时频矩阵编址; 时频矩阵编址【航海航天词汇】stiffness matrix 劲度矩阵; 刚度矩阵; 劲度矩阵【航海航天词汇】first-moment matrix 一阶矩矩阵【主科技词汇】matrix circuit 矩阵变换电路; 矩阵电路【计算机网络词汇】reluctance torque 反应转矩; 磁阻转矩【主科技词汇】pull-in torque 启动转矩; 牵入转矩【主科技词汇】induction torque 感应转矩; 异步转矩【主科技词汇】nominal torque 额定转矩; 公称转矩【航海航天词汇】phototronics 矩阵光电电子学; 矩阵光电管【主科技词汇】column matrix 列矩阵; 直列矩阵【主科技词汇】inverse of a matrix 矩阵的逆; 逆矩阵【主科技词汇】lattice matrix 点阵矩阵【数学词汇】lattice matrix 点阵矩阵【物理词汇】canonical matrix 典型矩阵; 正则矩阵; 典型阵; 正则阵【航海航天词汇】moment matrix 矩量矩阵【主科技词汇】moment matrix 矩量矩阵【数学词汇】dynamic torque 动转矩; 加速转矩【主科技词汇】indecomposable matrix 不可分解矩阵; 不能分解矩阵【主科技词汇】printed matrix wiring 印刷矩阵布线; 印制矩阵布线【主科技词汇】decoder matrix circuit 解码矩阵电路; 译码矩阵电路【航海航天词汇】scalar matrix 标量矩阵; 标量阵; 纯量矩阵【主科技词汇】array 矩阵式组织; 数组; 阵列【计算机网络词汇】commutative matrix 可换矩阵; 可交换矩阵【主科技词汇】标准文档实用文案。
数学de一些英文名称
Applied Partial Differential Equations Computer Graphics Computer Aided Geometric Design Selections from System Modeling Biology Mathematics Mathematical Finance Multimedia Techniques Applied Geometry Principle of Computer Network Dynamical Systems The Theory of Interest Actuarial Mathematics Coding Theory Numerical Analysis(Computing Method) Nonlinear Programming Combinatorial Optimization Optimal Control Theory Fractal Geometry Functions of Several Complex Variables Theory of Integral Equations and Its Applications Elementary Number Theory Stochastic Processes Applied software of Mathematics and Practice Introduction to Actuarial Science Advanced course on Mathematical Physics Mathematics of Demography Financial Economics Combinational Analysis Life Insurance Fourier Analysis Introduction to Insurance Introduction to Insurance and Actuarial Science Nonlife Actuarial Mathematics Complex Analysis Introduction to Control Theory Life Actuarial Mathematics Data Structures Statistics Digital Signal Processing Linear Programming Basic Information Theory Element of Database System
数学相关英语单词
数学英语词汇大全本文介绍了一些常用的数学英语词汇。
文章旨在帮助读者扩大数学英语词汇量,提高数学英语水平,增进对数学知识的理解和应用。
数学基础词汇数学基础词汇是指一些通用的、与数学有关的英语单词,它们在不同的数学领域都可能出现,也是其他数学英语词汇的基础。
以下是一些常见的数学基础词汇:中文英文数学mathematics, maths (BrE), math (AmE)数字number整数integer分数fraction小数decimal百分数percentage比例ratio比例尺scale指数exponent对数logarithm根号radical平方根square root立方根cube root四次根fourth root无理数irrational number有理数rational number实数real number复数complex number常数constant变量variable参数parameter函数function表达式expression方程式equation不等式inequality等式identity公式formula定理theorem引理lemma推论corollary公理axiom假设hypothesis, assumption, premise, conjecture中文英文结论conclusion, result, outcome, consequence证明proof, demonstration, verification反证法proof by contradiction, reductio ad absurdum数值计算numerical computation, numerical analysis符号计算symbolic computation, computer algebra计算器calculator数量和运算数量和运算是指与数字的大小、顺序、关系、组合、变化等有关的英语单词,它们是最基本的数学概念,也是其他数学知识的基础。
英文高等数学教材推荐
英文高等数学教材推荐IntroductionMathematics plays a crucial role in various academic fields, including engineering, physics, economics, and computer science. For students who are non-native English speakers, finding suitable English-language textbooks for advanced mathematics courses can pose a challenge. In this article, we will explore several recommended English higher education math textbooks that provide comprehensive coverage of the subject matter while maintaining clarity and accessibility for non-native English speakers.1. "Calculus: Early Transcendentals" by James StewartThis textbook is highly regarded for its thorough treatment of calculus concepts. It covers single-variable and multivariable calculus, including topics such as limits, derivatives, integrals, and infinite series. The book features clear explanations, numerous examples, and well-crafted exercises that help students develop a deep understanding of the material. Additionally, it provides online resources, including video lectures and interactive exercises, to enhance the learning experience.2. "Linear Algebra and Its Applications" by David C. LayLay's book offers a comprehensive introduction to linear algebra, a foundational mathematical subject in many STEM disciplines. It covers key topics such as vector spaces, matrices, determinants, eigenvalues, and eigenvectors. The book employs a conversational writing style and includes numerous real-world applications to help students grasp the practicalimplications of linear algebra. It also provides online resources, including interactive learning tools and practice quizzes.3. "Differential Equations and Their Applications: An Introduction to Applied Mathematics" by Martin BraunThis textbook is an excellent resource for students studying differential equations. It covers both ordinary and partial differential equations, emphasizing their applications in various fields. The book presents the material in a concise yet accessible manner, with clear explanations and illustrative examples. It also includes exercises and practice problems to reinforce understanding. Additionally, the book provides online resources, including supplementary materials and solution manuals.4. "Probability and Statistics for Engineers and Scientists" by Ronald E. WalpoleWalpole's textbook is designed to introduce students to the fundamentals of probability and statistics. It covers a wide range of topics, including probability theory, random variables, probability distributions, statistical inference, and hypothesis testing. The book utilizes real-world examples and exercises to help students grasp statistical concepts and techniques. It also offers online resources, including additional practice problems and interactive simulations.5. "Complex Variables and Applications" by James Ward Brown and Ruel V. ChurchillFor students interested in complex analysis, this textbook provides a comprehensive introduction to the subject. It covers complex numbers,analytical functions, contour integration, series representation, and residues. The book presents the material in a clear and logical manner, with numerous examples and exercises to reinforce learning. It also includes online resources, such as supplementary materials and problem-solving videos.ConclusionChoosing the right English-language mathematics textbook is crucial for non-native English-speaking students studying advanced math courses. The recommended textbooks discussed in this article offer comprehensive coverage of various mathematical topics, providing clear explanations, examples, and exercises to enhance understanding. Furthermore, these textbooks provide valuable online resources that supplement the learning experience. By utilizing these recommended textbooks, non-native English-speaking students can confidently navigate the intricacies of advanced mathematics and excel in their studies.。
组合数学经典书籍
组合数学经典书籍
组合数学是数学的一个重要分支,主要研究有限集合的元素间各种组合的可能性。
以下是一些经典的组合数学书籍:
1. 《组合数学》(Combinatorics):作者是R.P. Stanley,这本书是组合数学领域的经典教材,内容涵盖了组合计数、排列组合、二项式系数、生成函数、图论等多个方面,深入浅出,理论与实例结合。
2. 《组合数学引论》(An Introduction to Combinatorics):作者是J.H. van Lint和R.M. Wilson,该书系统介绍了组合数学的基本概念、方法和理论,适合初学者入门。
3. 《组合数学基础》(A Course in Combinatorics):作者是J. vanLint和D. J. A. Welsh,此书对组合数学进行了全面且详细的阐述,包括组合设计、编码理论等内容,有一定深度。
4. 《应用组合数学》(Applied Combinatorics):作者是Alan Tucker,这本书在介绍组合数学基本理论的同时,强调了其在实际问题中的应用,对于希望了解并运用组合数学解决实际问题的读者非常有帮助。
5. 《组合数学导引》(Enumerative Combinatorics, Volumes 1 and 2):作者同样是Richard P. Stanley,这两卷本著作被誉为组合数学领域的权威巨著,内容丰富且深入,适合具有一定基础的研究者阅读。
以上这些书籍都是组合数学领域中深受好评的经典之作,不同书籍侧重点和难易程度有所不同,您可以根据自己的需求选择合适的书籍进行学习。
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T HE U NIVERSITY OF S YDNEYP URE M ATHEMATICSLinear Mathematics2013Tutorial2(week3)—Solutions1.a)Explain why the set A= x x2 |x∈R is not a subspace of R2.b)Explain why the set B= 100 +t 110 |t∈R is not a subspace of R3.Solutiona)The set A is not closed under addition.For example, 11 ∈A and 24 ∈A,but11 + 24 = 35 ∈A.b) 000 ∈B.2.Vectors v1,v2,v3,v4,v5,v6are the vectors:= 000 v2= 052 v3= −440 v4= 111 v5= 201 v6= 021 v1Write each of the vectors v1,v2and v3as a linear combination of the three vectors{v4,v5,v6}, if possible.Solution To express a vector v∈R3as a linear combination of{v4,v5,v6},we have tofind scalars a,b,c∈R such that a v4+b v5+c v6=v.Writing v= x y z ,this is equivalent to solving the following system of linear equationsa+2b=x,a+2c=y,a+b+c=z.When v=v1= 000 no calculation is necessary because0=0v4+0v5+0v6.(The zero vector can always be written as a linear combination of any set of vectors.)We now solve the two cases where v=v2and v=v3simultaneously by computing with a “doubly augmented”matrix:1200−4,1025411120where the last two columns are the vectors v2and v3,ing Gaussian eliminationwefind that:1200−4 1025411120 R2:=R2−R1−−−−−−−→ 1200−40−225811120R3:=R3−R1−−−−−−−→ 1200−40−22580−1124R2←→R3R2:=R2×−1−−−−−−−→ 1200−401−1−2−40−2258R3:=R3+2R2−−−−−−−→ 1200−401−1−2−400010Thefirst column of the augmented matrix(or column4of the matrix),corresponds to v=v2.This system of equations is inconsistent,so there is no solution.That is,v2cannot be written as a linear combination of v4,v5and v6.The second column of the augmented matrix(or column5of the matrix),corresponds to v=v3.This system of equations has a solution;in fact,c is a free variable and there are an infinite number of solutions.Set c=t,a parameter.By back substitution,b=t−4and a=−2b−4=8−2t−4=4−2t.Hence,v3=(4−2t)v4+(t−4)v5+t v6,for any t∈R.In particular,taking t=0,we see that v3=4(v4−v5).3.Prove that the set S= x y ∈R2|y=4x is a subspace of R2. Solution S is clearly non-empty,since 00 satisfies the equation y=4x and is therefore in S. Now suppose that u= a4a and v= b4b both belong to S.Then u+v= a+b4a+4b = a+b4(a+b) ,and so u+v∈S,and S is closed under addition. If k∈R,then ku=k a4a = ka4(ka) ,and so k u∈S,and S is closed under scalar multiplication.4.Recall that F is the vector space of functions from R to R,with the usual operations of additionand scalar multiplication of functions.For each of the following subsets of F,write down two functions that belong to the subset,and determine whether or not the subset is a vector subspace of F.a)The set of polynomials of degree equal to3.b)The set{f:R−→R|f(1)=1}.c)The set{f:R−→R|f(−x)=−f(x)for all x∈R}.Solutiona)Both a and b,where a(x)=x3and b(x)=−x3,belong to this set.However,a(x)+b(x)=0,so a+b does not belong to the set.Hence this set is not closed under addition and so is not a vector subspace of F.b)Both a and b,where a(x)=1and b(x)=x,belong to this set.However,a(x)+b(x)=x+1,so(a+b)(1)=2,showing that a+b does not belong to this subset of F.Hence,the set is not closed under addition and so not a vector subspace of F.c )This is the set of all odd functions.Let F −be the set of odd functions.Then F −is clearly non-empty.(The zero function is an odd function,as is f (x )=x ,for example.)If f andg are two functions in F −then(f +g )(−x )=f (−x )+g (−x )=−f (x )−g (x )=−(f +g )(x ).Hence,F −is closed under addition.Also,if r ∈R then(rf )(−x )=r ·f (−x )=−r ·f (x )=−(rf )(x ),so r ·f ∈F −.Therefore,F −is a vector subspace of F .5.Let X =11−2, 235.Span(X )can be interpreted as a plane in 3dimensional space.Findthe equation of this plane.Solution The vector x y z∈Span(X )if and only if a 11−2+b 235= x y z,for some a,b ∈R .Applying Gaussian elimination we get12x 13y −25zR 2:=R 2−R 1R 3:=R 3+2R 1−−−−−−−→12x 01y −x 092x +z R 3:=R 3−9R2−−−−−−−→12x 01y −x 0011x −9y +zHence, x y z∈Span(X )if and only if 11x −9y +z =0.Therefore,Span(X )can be interpreted geometrically as the plane 11x −9y +z =0.6.For each of the sets of vectors X ⊂R 3below,explicitly describe all of the vectors in the subspace Span(X )of R 3.(Use set notation to describe Span(X ),and/or give a geometric interpretation.)a )X ={0}.b )X =111 .c )X =111, 222.d )X = 111 , 100.e )X = 111 , 100 , 110.Solution a )Span00 = 000.b )Span 111= r r r r ∈R .We can interpret this set geometrically as the straight line in 3-dimensional space passing through the origin and the point (1,1,1).c )As 222=2 111,Span 111 , 222 =Span 111 = r r r r ∈R .d )Span111, 100 = r 111+s 100 r,s ∈R = r +s r rr,s ∈R= x y z∈R 3 y =z .We can also describe Span(X )geometrically as the plane in R 3with equation y =z .e)Span 111 , 100 , 110 = r+s+t r+t r r,s,t∈R .We can analyze Span(X)alge-braically.A vector x y z ∈Span(X)if and only ifx y z =a 111 +b 100 +c 110 ,for some a,b,c∈ing Gaussian elimination we can simplify this to111x 101y100z R2:=R2−R1R3:=R3−R1−−−−−−−→ 111x0−10y−x0−1−1z−xR2:=R2×−1R3:=R3+R2−−−−−−−→ 111x010x−y00−1z−yHence,c=y−z,b=x−y and a=x−(x−y)−(y−z)=z.In particular,for any vector x y z we can alwaysfind suitable a,b,c∈R,so Span(X)=R3.Note that we now have given two quite different“explicit”descriptions of Span(X):r+s+t r+t r r,s,t∈R =Span(X)=R3.(A priori,it is not obvious that the left hand side of this equation gives R3.)7.The vector space P2consists of all polynomials of degree less than or equal to2.Let X={p1,p2,p3}⊂P2,where p1(x)=x+2,p2(x)=3−x,p3(x)=x2−x,for all x∈R.a)Show that X spans P2.(That is,show that Span(X)=P2.)b)Let p4(x)=2x2−x−3.Show that{p2,p3,p4}does not span P2.Solutiona)We must show that every vector in P2can be written as a linear combination of p1,p2,p3.That is,if p is an arbitrary element of P2,such that p(x)=α+βx+γx2say,then we have to show that p(x)=ap1(x)+bp2(x)+cp3(x),for some a,b,c∈R.Expanding this equation we see that we require thata(x+2)+b(3−x)+c(x2−x)=α+βx+γx2.Looking at the constant term and the coefficients of x and x2we obtain the following three equations:2a+3b=αa−b−c=βc=γTo solve for a,b and c we apply Gaussian elimination to the augmented matrix:230α1−1−1β001γ R1←→R2−−−−−→ 1−1−1β230α001γR2:=R2−2R1−−−−−−−→ 1−1−1β052α−2β001γHence,c=γ,b=15 α−2β−2c =15 α−2β−2γ and a=β+b+c=β+15 α−2β−2γ +γ=15α+35γ+35β.In particular,we can always solve for a,b and c,so Span(X)=P2as required.b)To show that Span{p2,p3,p4}=P2we need tofind an element of P2which is not a linearcombination of these three polynomials.Once again,let p be an arbitrary element of P2, where p(x)=α+βx+γx2.Then p belongs to Span{p2,p3,p4}if and only if we can find a,b,c∈R such that ap2(x)+bp3(x)+cp4(x)=p(x);that isa(3−x)+b(x2−x)+c(2x2−x−3)=α+βx+γx2.Looking at the constant term and the coefficients of x and x2gives us the following three equations:3a−3c=α−a−b−c=βb+2c=γWe again reduce the associated augmented matrix to row echelon form:30−3α−1−1−1β012γ R1←→R2R1:=R1×−1−−−−−−−→ 111−β30−3α012γR2:=R2−3R1−−−−−−−→ 111−β0−3−6α+3β012γR2:=−13R2−−−−−−→ 111−β012−13α−β012γR3:=R3−R2−−−−−−−→ 111−β012−13α−β00013α+β+γTherefore,p belongs to the span of{p2,p3,p4}if and only if13α+β+γ=0.In particular,if we take p such that p(x)=1(or1−3x2,or1+5x+7x2,or...)then p does not belong to Span{p2,p3,p4}.Notice that p4(x)=2x2−x−3=2(x2−x)+(x−3)=2p3(x)−p2(x).We will see later why this means that Span{p2,p3,p4}=P2.8.Suppose that V is a vector space and that v∈e the vector space axioms to show that:a)0×v=0.b)−1×v is the negative of v.Solutiona)Using the vector space axioms we have0×v=0+0×v by A4,=−v+v+0×v by A5,=−v+(1+0)×v by S3,=−v+1×v=−v+v by S5,=0by A5.b)Using the vector space axioms,v+(−1)×v=1×v+(−1)×v,by S5,=(1−1)×v by S3,=0×v=0by part(a).Therefore,−1×v is the negative of v.。