Maths - Topoi

Topos theory is between set theory and category theory. It allows us to take some nice properties of sets and to generalise them to certain categories with the properties described below.

The internal structure of a topos allows us to look at it in a set-like way and the external structure allows us to look at it in a category-like way.

Internal	External
set-like	category-like
defined with equations	defined with arrows.
The internal structure can see inside the set to the elements	The external structure can be seen from the structure of the sub objects (such as subsets)

In Category Theory

An (elementary) topos is a category that:

• Is finitely complete
• Is finitely co-complete
• Has exponentiation
• Has a subobject classifier

This is equivalent to:

• Being Cartesian closed
• Has a subobject classifier

Topos and Logic

A logic is defined over a type. So if we have a type, say 'set', then its sub-stucture will correspond to a logic as described below. A different type, say 'POSET', will have a different logic.

Elementary (set) Topos

The main example of a topos is set, in other words an (elementary) topos is a set-like category.

In order to understand sets we need to describe the structure of subsets. We need to define an element of the powerset. There are at least two ways to do this:

One way to define S as a subset of X (that is: SX) is to specify an inclusion (or injection map) from S to X. An injective function is one where no two distinct inputs give the same output.
It would not work to have a map in the other direction because elements in S are not uniquely identified with elements in X.
However we can define a subset with a map going out of X. This maps onto a two point set 2 or {0,1}. Those elements that are in the subset map to 1 and those that are not map to 0. This is called the characteristic function.
We can complete the diagram by adding an object representing the 'true' element. All elements in S map to it. As we can see, the diagram commutes.
So, in the general case for sets, we have this diagram which commutes. The diagram is also a pullback.
The diagram relates the set category to the logic of boolean algebra but without the law of the excluded middle, that is intuitional or constructive logic.

Alternative Viewpoint

We have two sets, 'A' and 'B', with an arbitrary mapping 'F' between them. We can define a subset of set 'B' by using the predicate 'P'. For every subset of 'B' defined by 'P' there exists a subset of 'A' defined by 'R'.
However this does not necessarily work in the other direction. Here is an example of a subset of 'A' which does not map to a subset of 'B'.

So the map 'F' between objects induces a contravarient map between arrows P->R.

See also Yoneda embedding.

Generalisation to Non-sets

We can generalise from elementary topos based on sets to other topoi.

We generalise this to categories that are not sets. X becomes some category and S becomes a sub-category. The injective mapping becomes a monic arrow. The characteristic function then becomes the sub-object classifier.

Example - Set

The terminal object for set is the one element set.
The sub-object classifier for set is the two element set. We can call these elements true and false.
The truth object for set is an arrow from 1 to true.
So the sub-object classifier '2' looks like Bool but this is not Boolean logic, it is constructive (intuitionistic) logic as described on the page here. This is because of the map from 1. There is no way for the diagram to commute via 'f'.

Logic of Set Example

Here we look at the logic of the set example (described on page here) and how this corresponds to the subset structure.

In the diagrams below the dark green represents a subobject of the light green and the dark blue represents a subobject of the light blue. They can be combined in various ways:

and (meet)		/\(Ω,Ω) -> Ω	f t f f f t f t
or (join)		\/(Ω,Ω) -> Ω	f t f f t t t t
implies		=>(Ω,Ω) -> Ω	f t f t t t f t

Example - Graph

In the set example above the sub-object classifier contains just true and false with the familiar Boolean logic. In the following more complicated examples there are also true and false corresponding to the cases where the subobject is completely in or completely out of the whole object. In addition there may now be other cases which are not but are required to allow the injection map to be valid. A good example of this is a graph category:

The terminal object for graph is one node with a loop.
The sub-object classifier for graph has two nodes and 5 arrows. For nodes: Is not in subgraph Is in subgraph For edges: Is in subgraph Not in subgraph but its source and target are. Not in subgraph but its source is. Not in subgraph but its target is. Not in subgraph and neither is its source or target.
The truth object.
As with sets this is constructive (intuitionistic) logic as described on the page here.

Logic of Graph Example

The logic of the nodes is the same as the set example but now we have the edges:

and (meet)		/\(Ω,Ω) -> Ω	f t f f f t f t
or (join)		\/(Ω,Ω) -> Ω	f t f f t t t t
implies		=>(Ω,Ω) -> Ω	f t f t t t f t

Example - Dynamical Systems

The terminal object for dynamical systems is one node with a loop.
The sub-object classifier for dynamical systems. '0' represents 'true' all other numbers indicate how many steps before it becomes true.
The truth object. points to '0' representing true.
All nodes and edges in the subobject go to '0' which represents 'true' all other nodes goto the number which indicates how many steps before it becomes true.

Subobject Classifier

See page here for a non-categorical approach to sub-objects.

If the arrow 'f' is monic (injective) then it maps to a subobject (subset) of 'B'. That is: there is a subobject of B that is isomorphic to A.

The inclusion relation on subobjects is:

• reflexive
• transitive

In order to define the subobject we must define the monic 'upto equality' which is not in the spirit of category theory. We will go on to define a 'subobject classifier' which defines a subobject, in a much more category theoretic way, by the composition of maps (and also relates the whole subject to logic).

Naming Arrows

In category theory we don't usually identify elements, such as elements of a set, because we only tend to determine things 'upto isomorphism'. The objects in a category are whole 'structures', they don't represent individual elements.

However, there is the possibility to indicate elements indirectly using arrows. For instance, if we want to enter a specific element we can use:

1->A

because, in sets, 1 is the single element set so it will indicate an element uniquely.

We can also use naming arrows. An arrow:

f: A->B

Is a subset of a function spacef:{0}->B^A

fis called the 'name' of the function.

Classifier

In the opposite direction there is a correspondence between subsets of B and functions:

B->2

This is in the space 2^B which corresponds to the powerset(B), that is, all the possible subsets of B.

The characteristic arrow: xf classifies objects of B to determine if they are images of A. This is a pullback square. The object 2 has two values: 0 and 1. We also use the symbol Ω and call the elements 'true' and 'false'.

This means the topos theory is related to logic. (see also 'characteristic function' in number theory).

Example in Set

	Example in set
Set A (blue) is subset of B (red).
If we take the inverse of f we get the concept of a 'bundle' as discussed on this page.
classifier for set.

Predicate Category

The relationship SX above is a predicate. We can form a category of predicates on sets as follows:

Objects	predicates are pairs (S,X) where SX X(i) implies an element iS is a free variable in S.
Morphisms	(S,X) -> (T,Y) where: u:S-> T and X(i) implies: Y(u(i))

See also - Predicate logic.

Family of Sets

A family of sets consists of an index set I and, for each element k of I, a set S_k.

There seems to be two ways to conceptulise this:

• As a 2-category. A set of sets.
• As disjoint sets.

Modest Sets

Set theoretic model of polymorphism.

Indexed Categories

(fibred category)

example

many sorted algebra ∑

Generalisation from Sets to Categories

Here we have illustrated some basic concepts using sets but they can be generalised to categories as shown here:

Sets	Categories
Injective Function No two distinct inputs give the same output. We can define 'A' a subset of 'B' by using an injective function from C	Monic Arrow Whenever f•g = f•h then g=h
Here we are showing this as an inclusion function because we are interested in subset classifiers. Injective function f: C >->B determines A as a subset of B (AB) Im f = {f(x): xC} where Im = Image Set inclusion is a partial odering on the Power set
Surjective function	Epic Arrow
Inverse Functions
Power Set	Power Object subobjects of d form a poset (Sub(d),)

Topological Space

In many cases the concept of a metric space is unnessary, however we still need the concept of 'nearness' and hence 'continuity'. 'Topological space' based on the 'topological open set' is the most general way we can do this. This allows us to define nearness purely using the concept of a subset.

Hausdorff Space

Hausdorff space is a bit more specific than general topological space. Space is Hausdorff if, in addition to being a topological open set, for any two points:
x₁, x₂X there are disjoint open sets U₁, U₂ that contain x₁and x₂.

Basis

A basis is a subcollection B= U

where

• U is the open set
• B is the basis
• = a subcollection (subset) - I need to add the proper symbol for this.

such that evey element of U is a union of open sets in B.

Computing Topos

https://github.com/fdilke/bewl

to install in openSUSE:

in YaST install:

• Scala
• SBT
• Git

on command line:

git clone https://github.com/fdilke/bewl.git
cd bewl
sbt console

EuclideanSpace

home

Prerequisites

See page here for a non-categorical approach to sub-objects.

Related Subjects

• Lattice - relation between logic and spaces
• Topological Space
• Category of Presheaves

Inverse Functions

Venn Diagrams

Some of us, of a certain age, can remember something called 'new maths' introduced at school. This was an idea to introduce set theory at an early stage (I hope they still do it). An important part of this was Venn diagrams, they give a very intuitive way to look at sets and their intersections and unions. For me an important part of this is the way it demonstrates a deep and beautiful link between sets (union, intersection), logic (and, or) and topology.

Sets, Logic and Topology

There is a problem with Venn diagrams, they do not work so well when we increase the number of sets. Its alright with 3 sets, we can draw 3 circles showing all possible intersections, but its more difficult with 4 or more sets.

To have a diagram that scales up to many sets it seems to be necessary to separate the sets from the logic. This diagram is less intuitive and compact than Venn diagrams but it is still very powerful. It must obey certain axioms so that the sets are consistent with the logic part. For more information see the page about sheaves.