# 函数概念并不难，理解“函”字是关键——函数概念如何理解】

The unique 1 single output of a function becomes very important for subsequent development in Math & IT:
functions are composable, associative, identify function，etc (distributive,… ) => it can be treated like vector => structure of a Vector Space “Vect”

Extended to..

“Vect” is a bigger structure “Category” in which “function of functions” is a
Functor” （函子）F：F(f)

Example : F(f) = fmap (in Haskell)

fmap (+1) {2,7,6,3}

=> {3,8,7,4}

here F = fmap, f = +1

The Math branch in the study of functions is called “functional” 泛函。

IT : Functional Programming in Lisp, Haskell, Scala, ensure safety of guaranteed output by math function property. Any unexpected exception (side effects: IO, errors) is handled by a special function called “Monad” (endo-Functor).

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# What is a Field, a Vector Space ? (Abstract Algebra)

Suitable for Upper Secondary School and Junior College Math Students.

Abstract Algebra is scary because it is abstract, and its Math Profs are mostly fierce – but not with this pretty Math lady…

WHAT IS A FIELD (域) ?

WHAT IS A VECTOR SPACE (向量空间) ?

See all 20+ videos here:

# Does Abstract Math belong to Elementary Math ?

The answer is : “Yes” but with some exceptions.

Most pedagogy mistake made in Abstract Algebra teaching is in the wrong order (by historical chronological sequence of discovery):

[X] Group -> Ring -> Field

It would be better, conceptual wise, to reverse the teaching order as:

Field -> Ring -> Group

or better still as (the author thinks):

Ring -> Field -> Group

• Reason 1: Ring is the Integers, most familiar to 8~ 10-year-old kids in primary school arithmetic class involving only 3 operations: ” + – x”.
• Reason 2: Field is the Real numbers familiar in calculators involving 4 operations: ” + – × ÷”, 1 extra division operation than Ring.
• Reason 3: Group is “Symmetry”, although mistakenly viewed as ONLY 1 operation, but not as easily understandable like Ring and Field, because group operation can be non-numeric such as “rotation” of triangles, “permutation” of roots of equation, “composition” of functions, etc. The only familiar Group is (Z,+), ie Integers under ” +” operation.

Some features which separate Advanced Math from Elementary Math are:

• Proof [1]
• Infinity [2]
• Abstract [3]
• Non Visual [4]
•

Note [1]: “Proof” is, unfortunately, postponed from high-school Math to university level. This does not include the Euclidean Geometry axiomatic proof or Trigonometry Identity proof, which are still in Secondary school Elementary Math but less emphasized since the 1990s (unfortunately).

Note [2]: However, some “potential” infinity still in Elementary math, such as 1/3 = 0.3333…only the “Cantor” Infinity of Real number, ${\aleph_{0}, \aleph_{1}}$ etc are excluded.

Note [3]: Some abstract Algebra like the axioms in Ring and Field  (but not Group) can be in Elementary Math to “prove” (as in [1]): eg. By distributive law
$(a + b).(a - b) = a.(a - b) + b.(a - b)$
$(a + b).(a - b) = a^{2}- ab + ba - b^{2}$
By commutative law
$(a + b).(a - b) = a^{2}- ab + ab- b^{2}$
$(a + b). (a - b) = a^{2} - b^{2}$

Note [4]: Geometry was a “Visual” Math in Euclidean Geometry since ancient Greek. By 17 CE, Fermat and Descartes introduced Algebra into Geometry as the Analytical Geometry, still visual in (x, y) coordinate graphs.

20 CE Klein proposed treating Geometry as Group Transformation of Symmetry.

Abstract Algebra concept “Vector Space” with inner (aka dot) product is introduced into High School (Baccalaureate) Elementary Math – a fancy name in  “AFFINE GEOMETRY” (仿射几何 , see Video 31).

eg. Let vectors
$u = (x,y), v = (a, b)$

Translation:
$\boxed {u + v = (x,y) + (a, b) = (x+a, y+b)}$

Stretching by a factor ${ \lambda}$ (“scalar”):
$\boxed {\lambda.u = \lambda. (x,y) = (\lambda{x}, \lambda{y})}$

Distance (x,y) from origin: |(x,y)|
$\boxed {(x,y).(x,y) =x^{2}+ y^{2} = { |(x,y)|}^{2}}$

Angle ${ \theta}$ between 2 vectors ${(x_{1},y_{1}), (x_{2},y_{2})}$:

$\boxed { (x_{1},y_{1}).(x_{2},y_{2}) =| (x_{1},y_{1})|.| (x_{2},y_{2})| \cos \theta}$

Ref: 《Elements of Mathematics – From Euclid to Gödel》by John Stillwell (Princeton University Press, 2016) [NLB # 510.711]

# Big Picture: Linear Algebra

The Big Picture of Linear Algebra 线性代数 (MIT Open-Courseware by the famous Prof Gilbert Strang)

A.X = B

$A(m,n) = \begin{pmatrix} a_{11} & a_{12} & \ldots & a_{1n}\\ a_{21} & a_{22} & \ldots & a_{2n}\\ \vdots & \vdots & \ddots & \vdots\\ a_{m1}&a_{m2} &\ldots & a_{mn} \end{pmatrix}$

A(m, n)  is a matrix of m rows, n columns

4 sub-Vector Spaces:
Column Space ${C(A)}$ , dim = r = rank (A)
Row Space ${C(A^{T})}$ , dim = r = rank (A)

Nullspace ${N(A)}$   ${\perp C(A^{T})}$ , dim = n – r

Left Nullspace ${N(A^{T})}$ ${\perp C(A)}$ , dim = m – r

Abstract Vector Spaces ​向量空间

Any object satisfying these 8 axioms belong to the algebraic structure of Vector Space: eg. Vectors, Polynomials, Functions, …

Note: “Vector Space” + “Linear Map” = “Category$Vect_{K}$

Eigenvalues & Eigenvectors (valeurs propres et vecteurs propres) 特征值/特征向量

[ Note: “Eigen-” is German for Characteristic 特征.]

Important Trick: (see Monkeys & Coconuts Problem)
If a transformation A is linear, and the “before” state and “after” state of the “vector” v remain the same  (keep the status-quo) , then : Eigenvalue $\boxed {\lambda = 1}$

$\boxed {A.v = \lambda.v = 1.v = v}$

Try to compute:
${ \begin{pmatrix} a_{11} & a_{12} & \ldots & a_{1n}\\ a_{21} & a_{22} & \ldots & a_{2n}\\ \vdots & \vdots & \ddots & \vdots\\ a_{n1}&a_{n2} &\ldots & a_{nn} \end{pmatrix} }^{1000000000}$
is more difficult than this diagonalized equivalent matrix:
${ \begin{pmatrix} b_{11} & 0 & \ldots & 0\\ 0 & b_{22} & \ldots & 0\\ \vdots & \vdots & \ddots & \vdots\\ 0 &0 &\ldots & b_{nn} \end{pmatrix} }^{1000000000}$

$= { \begin{pmatrix} {b_{11}}^{1000000000}&0 &\ldots &0\\ 0 &{b_{22}}^{1000000000} &\ldots & 0\\ \vdots &\vdots & \ddots & \vdots\\ 0 &0 &\ldots &{b_{nn}}^{1000000000} \end{pmatrix}}$

Note: This is the secret of the Google computation of diagonalized matrix of billion columns & billion rows, where all the bjk are the “PageRanks” (web links coming into a particular webpage and links going out from that webpage).

The Essence of Determinant (*): (行列式)

(*) Determinant was invented by the ancient Chinese Algebraists 李冶 / 朱世杰 /秦九韶 in 13th century (金 / 南宋 / 元) in《天元术》.The Japanese “和算” mathematician 关孝和 spread it further to Europe before the German mathematician Leibniz named it the “Determinant” in 18th century. The world, however, had to wait till the 19th century to discover the theory of Matrix 矩阵 by JJ Sylvester (Statistical Math private Tutor of Florence Nightingale, the world’s first nurse) closely linked to the application of Determinant.

[NOTE] 金庸 武侠小说 《神雕侠女》里 元朝初年的 黄蓉 破解 大理国王妃 瑛姑 苦思不解的 “行列式”, 大概是求 eigenvalues & eigenvectors ? 🙂

# Espaces Vectoriels

Cours math sup, math spé, BCPST.

The French University (engineering) 1st & 2nd year Prépa Math: “Vector Space” (向量空间), aka Linear Algebra (线性代数), used in Google Search Engine. The French treats the subject abstractly, very theoretical, while the USA and UK (except Math majors) are more applied (directly using matrices).

Note: First year French (Engineering) University “Classe Prépa”: Math Sup (superior); 2nd year Math Spé (special).

Part 2:

Applications Lineaires (Linear Algebra):

# Espaces vectoriels (1)

Introduction to Vector Space