Programmers need Advanced Math ?

As software becomes more complicated for high-speed trains, driverless cars, missile weapons. .. and AI deeplearning algorithm, we can’t depend our life safety on the programmers who don’t understand the advanced maths behind these algorithms.

The Advanced Math is the Category Theory – the most advanced math foundation above “Set Theory” since WW2. Functional Programming is based on Category Theory with mathematical functions – always output correctly with no “side-effects”.

https://www.extremetech.com/computing/259977-software-increasingly-complex-thats-dangerous

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Category Theory – Purest of pure mathematical disciplines may also be a cornerstone of applied solutions in computational science

There exists in almost all Universities a clear division between pure and applied mathematics. A friendly (and sometimes not so friendly) rivalry exists between both sides of the divide, with separate conferences, separate journals and in many cases even a whole separate language. Category Theory was seen as such an abstract area of research that even pure mathematicians started to refer to it as “abstract nonsense“, and until the mid 1980’s almost all category theorists occupied a place hidden somewhere up above the ‘cloud level’ in the highest reaches of the peaks that defined “pure” maths.

By the mid 1990’s and then by the turn of the millenium, a whole world of computer programmers were learning basic category theory as part of their induction into functional programming. The best known product of these efforts is the Haskell language, but even in the past 7 or 8 yrs, workshops on category theory for computer programmers of all types have flourished and proliferated. It is almost as if there are two separate communities masquerading as one – mathematical category theory and computer programming category theory – and never the twain would meet. Or so it seemed, until now.

https://www.linkedin.com/pulse/category-theory-classic-dichotomy-purest-pure-may-also-khan-ksg/

Alejandro Serrano: Category Theory Through Functional Programming

(Part 1/3) – λC 2017

What is Category ?

Objects

Morphism (Arrows )

Rule1: Associative

Rule 2: Identity

A <– C –> B

Product of Categories : A x B

Unique

Sum of Categories: A + B

Unique

(Either a b)

Co-Product

Reverse all arrows.

Unique

Functor F: C-> D

Mapping of all objects (A, B) in categories C,D

Mapping of arrows f
f : A -> B
Ff : FA -> FB (preservation)
F Id = Id
F (f。g) = Ff。Fg

Example:

Constant C -> F
FC = k
Ff = Id

Arrow Functor F: C -> D

For any object A in C,

F A = D -> A
(Functional Type is also Type)

Functors compose !

Category of categories:

Objects: categories

Arrows : Functors

Haskell Category (Hask) is always Endo-Functor, ie Category Hask to itself.

Mapping of arrows.

Mapping of Objects = predefined

(Part 2/3) – λC 2017

(part 3/3) – λC 2017

Category Theory II 9: Lenses

Lens = {get, set}

w = whole tuple, p = a field

get :: w -> p
set :: w -> p -> w

Example: take a pair (tuple)

get1 (x, y) = x

get1 :: (a, b) -> a

set1 (x, y) x’ = (x’,y)

set1 :: (a, b) -> a -> (a,b)

Polymorphic Types: change type of field

set :: w -> p’ -> w’
set1 :: (a, b) -> a’ -> (a’,b)

Lens Laws:

set w (get w) = id 

get (set w p) = p

get (set w p) p’ = set w p’

Combine get & set (co-Algebra):

\boxed {w \to(p, p \to w)}

data Store p w = Store p (p-> w)

fmap g (Store p f ) = Store p (g.f)
g: w-> v
pf : p -> w
g.f : p-> v

Store = functor from w to Store = coalgebra = comonad [W a ->a]

instance Comonad (Store p) where

extract (Store p f) = f p

duplicate  (Store x f) = Store x ( λy -> Store y f)

[Think of p as key, f is the lookup function
f p = retrieve current value]

Comonad & coalgebra – compatible?

(Coalgebra) coalg ::a -> w a
(Comonad) extract :: w a -> a
duplicate :: w a ->w (w a)

coalg w = Store (get w) (set w)

set w (get w) = id [Lens Law]

\boxed {\text {Lens = comonad + coalgebra}}

If Type change (Set = Index Store : a = p, b = p’, t = w)

IStore a b t = IStore a (b ->t)

Object-oriented: eg. school.class.student

<=>”.” = functional programming 



9.2 Lenses 

Type Lense s t a b = forall f . Functor f => (a -> f b) -> s -> f t

s \to \forall f. (a \to f b ) \to f t

s  ->IStore a b t

forall  = polymorphism (?  Natural Transformation)

Yoneda Embedding Lemma

 [C, Set](C (a -> – ), C (b -> – )) ~ C(b, a)

Adjunction: C (Ld,C) ~ D (D,  Rc)


Reference: “Understanding Yoneda”

https://bartoszmilewski.com/2013/05/15/understanding-yoneda/

adjoint functor in nLab

Yoneda lemma in nLab

维基百科: Hom functor
https://www.zhihu.com/question/23324349/answer/54242934

The Yoneda Lemma

Representable Functor F of C ( a, -):

\boxed {(-)^{a} = \text {F} \iff a = \text {log F}}

Video 4.2 Yoneda Lemma

Prove : 

Yoneda Lemma :
\text {F :: C} \to \text {Set}

\boxed {\alpha \text { :: [C, Set] (C (a, -),F) } \simeq \text {F a}}

\alpha : \text {Natural Transformation}
\simeq : \text {(Natural) Isomorphism, "naturally" } \forall ( a, F )

Proof: By “Diagram chasing” below, shows that

Left-side
: \alpha \text { :: [C, Set] (C (a, -),F) } is indeed a (co-variant) Functor. (Higher-order Function)

Right-side: Functor “F a“. (Data Structure)

\boxed {\forall x, (a \to x) \to \text { F } x \simeq \text { F } a }

Note: When talking about the natural transformations, always mention their component “x”: \alpha_{x}, \beta_{x}

Video 5.1 Yoneda Embedding

Example 1: F = List functor [x]

\boxed {\alpha \text { :: } (a \to x) \to [x] \simeq [a] }

Example 2: F = C (b, -)

\boxed {\alpha \text { :: [C, Set] (C (a, -), C (b, -) ) } \simeq \text {C (b, a)}}

Note: check a , b is in co-variant or contra-variant position.

Example 3: F = Id

\boxed {\alpha \text { :: } (a \to x) \to x \simeq a }

Right-hand-side: a (data structure)

Left-hand-side : “(a -> x) “is a function called “handler” (or “continuation“) which takes the argument “a” to provide it as output : “(a -> x ) -> x“.

eg. handler to database query, over internet…(technique used in Compiler)

Co-Yoneda Lemma : (Contra-variant a , F)

\boxed {\alpha \text { :: [C, Set] (C(-, a),F) } \simeq \text {F a}}

Yoneda Embedding: Full and Faithful
\alpha \text { :: [C, Set] (C (a, -), C (b, -) ) } \simeq \text {C (b, a)}

Note: Instead of proving a , b are isomorphic, sometimes it is easier to prove the functors C(a, -) & C(b, -) are isomorphic.[Proof Trivial: functors preserve composition and identity]

ApplicationCo-Yoneda Lemma : (Contra-variant a , F)
\boxed {\text {[C, Set] (C(- , a), C(- , b) } \simeq \text {C(a ,b)}}

Pre-order Category \boxed {a \leq b}

\forall x, \text {C(x , a)} \to \text {C(x , b)} \simeq \text {C(a , b)}

C(x , a) = x \leq a = \{\varnothing, 1 \}
C(x , b) = x \leq b =\{\varnothing, 1 \}

3 possibilities: (“1” = singleton)
id_{\varnothing} :: \varnothing \to \varnothing
absurd :: \varnothing \to 1
id_{1} :: 1 \to 1
Note: 1 \to \varnothing impossible (function must have an image)

Verify

Right-Hand Side: a \leq b

Left-Hand Side: \forall x,( x \leq a \implies x \leq b) \implies a \leq b

Yoneda Embedding (Lagatta)