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Functions: Metaphors, Images and Representations
Please read this post at abstractmath.org. I originally posted the document here but some of the diagrams would not render, and I haven’t been able to figure out why. Sorry for having to redirect.
Proofs using diagrams
Introduction
This post gives a proof of an easy theorem in category theory using the graphbased logic approach of Graph based logic and sketches, (GBLS) by Atish Bagchi and me.
Formal logic is typically defined in terms of formulas and terms, defined recursively as strings of characters, together with rules of inference. GBLS proposes a new approach to logic where diagrams are used instead of strings of characters. The exposition here spells out the proof in more detail than GBLS does and uses various experimental ways of drawing diagrams using Mathematica.
To follow this proof, you need to be familiar with basic category theory. Most special definitions that are needed are defined in this post where they are first used. Section 1 of GBLS also gives the definitions you need with more context.
The theorem
The Theorem to be proved (it is Theorem 8.3.1 of GBLS) says that, in any category, if the triangles in the diagram below commute, then the outside square commutes. This is easy using the associative law: If $xf=h$ and $kx=g$, then $kh=k(xf)=(kx)f=gf$.
Subject Diagram
So what?
This theorem is not interesting. The point of this post is to present a new approach to proving such theorems, using diagrams instead of strings. The reason that exhibiting the dig
rammatic proof is interesting is that many different kinds of categories have a FL cattheory, including these:
 Categories with all finite limits, also called FL categories, left exact categories, cartesian categories, or
essentially algebraic theories. Note that what I am saying here is that the category of all categories with finite limits is the category of models of a particular category (a cattheory) with finite limits. It is easy to get syntax and semantics (theory and models) confused!  Finitely complete and cocomplete categories.
 Cartesian closed categories.
 Locally cartesian closed categories.
 Toposes.
Essentially algebraic stringbased logic is described in detail in Partial Horn logic and cartesian categories, by E. Palmgren and Steven Vickers.
Commercial
My concept of form in A generalization of the concept of sketch generalizes sketches to all the categories that can be defined as models of FL cattheories. So the method of proof using diagrams can be applied to theorems about the objects defined by forms.
Concepts needed for the graphbased proof
To prove the theorem, I will make use of $\mathbf{ThCat}$, the FL cattheory for categories.
 An FL category is a category with all finite limits.
 GLBS uses the word cattheory for what Category theory for computing science and Toposes, triples and theories call the theory of a sketch.
 In many books and articles, and in nLab, a “sketch” is what we call the cattheory (or the theory) of a sketch. For us, the sketch is a generating collection of objects, arrows, diagrams, cones and cocones for the cattheory. The category of models of the sketch and the cattheory are equivalent.
 $\mathbf{ThCat}$ is a category with finite limits freely generated by certain designated objects, arrows, commutative diagrams and limit cones, listed below.
 A model of $\mathbf{ThCat}$ in $\mathbf{Set}$ (the category of sets, whichever one you like) is an FL functor $\mathfrak{C}:\mathbf{ThCat}\to\mathbf{Set}.$
 Such a model $\mathfrak{C}$ is a small category, and every small category is such a model. If this statement worries you, read Section 3.4 of GBLS.
 Natural transformations between models are FLpreserving functors that preserve the structure on the nose.
 The category of models of $\mathbf{ThCat}$ in $\mathbf{Set}$ is equivalent to the category of small categories and morphisms, which, unlike the category of models, includes functors that don’t preserve things on the nose.
 $\mathbf{ThCat}$ is an example of the theory of an FL sketch. Chapter 4 of GBLS describes this idea in detail. The theory has the same models as the sketch.
 The sketch generating $\mathbf{ThCat}$ is defined in detail in section 7.2 of GBLS.
Some objects and arrows of $\mathbf{ThCat}$
I will make use of the following objects and arrows that occur in $\mathbf{ThCat}.$ A formal thing is a construction in $\mathbf{ThCat}$ that becomes an actual thing in a model. So for example a model $\mathfrak{C}$ of $\mathbf{ThCat}$ in $\mathbf{Set}$ is an actual (small) category, and $\mathfrak{C}(\mathsf{ar_2})$ is the set of all composable pairs of arrows in the category $\mathfrak{C}$.
 $\mathsf{ob}$, the formal set of objects.
 $\mathsf{ar}$, the formal set of arrows.
 $\mathsf{ar}_2$, the formal set of composable pairs of arrows.
 $\mathsf{ar}_3$, the formal set of composable triples of arrows.
 $\mathsf{unit} : \mathsf{ob}\to \mathsf{ar}$ that formally picks out the identity arrow of an object.
 $\mathsf{dom},\mathsf{cod} : \mathsf{ar}\to \mathsf{ob}$ that formally pick out the domain and codomain of an arrow.
 $\mathsf{comp} : \mathsf{ar}_2\to \mathsf{ar}$ that picks out the composite of a composable pair.
 $\mathsf{lfac}, \mathsf{rfac} :\mathsf{ar}_2\to \mathsf{ar}$ that pick out the left and right factors in a composable pair.
 $\mathsf{lfac}, \mathsf{mfac},\mathsf{rfac} :\mathsf{ar}_3 \to\mathsf{ar}$ that pick out the left, middle and right factors in a composable triple of arrows.
 $\mathsf{lass}, \mathsf{rass} : \mathsf{ar}_3 \to \mathsf{ar}_2$: $\mathsf{lass}$ formally takes $\langle{h,g,f}\rangle$ to $\langle{hg,f}\rangle$ and $\mathsf{rass}$ takes it to $\langle{h,gf}\rangle$.
$\mathsf{ob}$, $\mathsf{ar}$, $\mathsf{unit}$, $\mathsf{dom}$, $\mathsf{cod}$ and $\mathsf{comp}$ are given primitives and the others are defined as limits of finite diagrams composed of those objects. This is spelled out in Chapter 7.2 of GBLS. The definition of $\mathbf{ThCat}$ also requires certain diagrams to be commutative. They are all provided in GBLS; the one enforcing associativity is shown later in this post.
Color coding
I will use color coding to separate syntax from semantics.
 Syntax consists of constructions in $\mathbf{ThCat}.$ The description will always be a commutative diagram in black, with annotations as explained later.
 The limit of the description will be an object in $\mathbf{ThCat}$ (the form) whose value in a model $\mathfrak{C}$ will be shown in green, because being an element of the value of a model makes it semantics.
 When a limit cone is defined, the projections (which are arrows in $\mathbf{ThCat}$) will be shown in blue.
Descriptions
In graphbased logic, a type of construction that can be made in a category has a description, which (in the case of our Theorem) is a finite diagram in $\mathbf{ThCat}$. The value of the limit of the description in a model $\mathfrak{C}$ is the set of all instances of that type of construction in $\mathfrak{C}$.
The Subject Diagram
 This diagram is the subject matter of the Theorem. It is not assumed to be commutative.
 As in most diagrams in category theory texts, the labels in this diagram are variables, so the diagram is implicitly universally quantified. The Subject Diagram is a generic diagram of its shape.
 “Any diagram of its shape” includes diagrams in which some of the nodes may represent the same object. An extreme example is the graph in which every node is an object $\mathsf{E}$ and every arrow is its identity arrow. The diagram below is nevertheless an example of the Subject Diagram:
 Shapes of diagrams are defined properly in Section 2.3 of
GBLS and in Section 4.1 of Category Theory for Computing Science.
The description of the Subject Diagram
Diagram SDD below shows the Subject Diagram as the limit of its description. The description is the black diagram.
Diagram SDD
 Following the practice in GBLS, the nodes of the description are annotated by the variable names used in the Subject Diagram. The annotations are purely for the reader’s convenience, to see for example which copy of $\mathsf{ar}$ formally contains the arrow named $f$.
 The description is a commutative diagram in $\mathbf{ThCat}$, and a model functor $\mathfrak{C}$ must take the description to a commutative diagram in the model. Every node in this commutative diagrams is either the set of all nodes or the set of all arrows in the category $\mathfrak{C}$. Every arrow is either the map that takes an arrow to its domain or the map that takes an arrow to its codomain.
 You can see that the description spells out precisely what the domain and comain of each arrow is. So the value of the limit node must be the set of all diagrams of the form of the Subject Diagram. I have named the limit by exhibiting the Subject Diagram rather than by giving it another random name that you have to remember.
Definition of $\mathsf{ar}_2$
The object $\mathsf{ar}_2$ of composable pairs of arrows is defined as a pullback:
In the usual categorical notation this would be shown as
This makes use of the fact that the unnamed blue arrow is induced by the other two projection arrows. In the rest of the post, projection arrows that are induced are normally omitted.
An enrichment of the description
Because $\mathsf{ar}_2$ is defined as a pullback, we can enrich the description of Diagram SDD by adjoining two pullbacks as shown below. This is Diagram 8.10 in GBLS. The enriched diagram has the same limit as the description of Diagram SDD.
Enriched Diagram SDD
Note that the projections from the limit to the two occurrences of $\mathsf{ar}_2$ induce all the other projections. This follows by diagram chasing; remember that the description must be a commutative diagram.
Make the triangles commute
To make the triangles commute, we add two comp arrows to the enriched diagram as shown below. These two arrows are not induced by the description; they are therefore additions to the description — they describe a more restrictive (green) diagram with commutative triangles and so are shown in black.
Diagram TC: The triangles commute
The left comp makes $xf=h$ and the right comp makes $kx=g$.
The outside square commutes
Now we enrich Diagram TC with four objects, <comp,id>, <id,comp> and three comp arrows as shown in bolder black. These objects and arrows already exist in $\mathbf{ThCat}$ and therefore do not change the limit, which must be the same as the limit of Diagram TC.
The outside square commutes
The diagram in bold black is exactly the commutative diagram that requires associativity for these particular objects and arrows, which immediately implies that $gf=kh$, as the Theorem requires.
By the definitions of $\mathsf{ar_2}$ and $\mathsf{ar_3}$, the part of the description in bold black induces the rest of the diagram. Omitting the rest of the diagram would make $\mathsf{ar_2}$ and $\mathsf{ar_3}$ modules in the sense of GBLS, Chapter 7.4. Modules would be vital to deal with proofs more complicated than the one given here.
References
 Graph based logic and sketches, by Atish Bagchi and Charles Wells.
 Category theory for computing science, by Michael Barr and Charles Wells.
 Toposes, triples and theories, by Michael Barr and Charles Wells.
 On the limitations of sketches, by Michael Barr and Charles Wells. Examples of what forms can describe that sketches can’t.
 Left exact logic, Colin McLarty. Journal of pure and applied algebra 41 (1986), pp. 6366.
 Partial Horn logic and cartesian categories, E. Palmgren and Steven Vickers. Annals of Pure and Applied Logic, 143 (2007), pp. 314353. ISSN 01680072.
 A formalism for the specification of essentially algebraic structures in $2$categories, (1992), by John Power and Charles Wells.
 A generalization of the concept of sketch, (1990), by Charles Wells. This is the founding paper for forms.
 Forms, by Charles Wells. This is an introduction to forms aimed a people with only a little knowledge of category theory.
 Forms diagrams, link to Mathematica notebooks and diagrams concerning Forms.
This work is licensed under a Creative Commons AttributionShareAlike 2.5 License.
A proof by diagram chasing
In Rigorous proofs, I went through the details of a mediumeasy epsilondelta proof in great detail as a way of showing what is hidden by the wording of the proof. In this post, I will do the same for an easy diagramchasing proof in category theory. This theorem is stated and proved in Category Theory for Computing Science, page 365, but the proof I give here maximizes the diagramchasing as a way of illustrating the points I want to make.
 $F\alpha:FFa\to Fa$ is also an $F$algebra.
 Initiality means that there is a unique algebra morphism $\eta:a\to Fa$ from $\alpha:Fa\to a$ to $F\alpha:FFa\to Fa$ for which this diagram commutes:
 To that diagram we can adjoin another (obviously) commutative square:
 Then the outside rectangle in the diagram above also commutes.
 This means that $\alpha\circ\eta:a\to a$ is an $F$algebra morphism from $\alpha:Fa\to a$ to itself.
 Another such $F$algebra morphism is $\text{id}_{A}$.
 Initiality of $\alpha$ means that the diagram below commutes:
 Because the upper bow and the left square both commute we are justified in inserting a diagonal arrow as below.
 Now we can read off the diagram that $F\alpha\circ F(\eta)=\text{id}_{Fa}$ and $\eta\circ\alpha=\text{id}_a$. By definition, then, $\eta$ is a twosided inverse to $\alpha$, so $\alpha$ is an isomorphism.
Analysis of the proof
This is an analysis of the proof showing what is not mentioned in the proof, similar to the analysis in Rigorous proofs.
 An $F$algebra is any arrow of the form $\alpha:Fa\to a$. This definition directly verifies statement (1). You do need to know the definition of “functor” and that the notation $Fa$ means $F(a)$ and $FFa$ means $F(F(a))$.
 When I am chasing diagrams, I visualize the commutativity of the diagram in (2) by thinking of the red path and the blue path as having the same composites in this graph:
In other words, $F\alpha\circ F\eta=\eta\circ\alpha$. Notice that the diagram carries all the domain and codomain information for the arrows, whereas the formula “$F\alpha\circ F\eta=\eta\circ\alpha$” requires you to hold the domains and codomains in your head.  (Definition of morphism of $F$algebra) The reader needs to know that a morphism of $F$ algebras is any arrow $\delta:c\to d$ for which
commutes.  (Definition of initial $F$algebra) $\alpha$ is an initial $F$algebra means that for any algebra $\beta:Fb\to b$, there is a unique arrow $\delta$ for which the diagram above commutes.
 (2) is justified by the last two definitions.
 Pulling a “rabbit out of a hat” in a proof means introducing something that is obviously correct with no motivation, and then checking that it results in a proof. Step (9) in the proof given in Rigorous proofs has an example of adding zero cleverly. It is completely OK to pull a rabbit out of a hat in a proof, as long as the result is correct, but it makes students furious.
 In statement (3) of the proof we are considering here, the rabbit is the trivially commutative diagram that is adjoined on the right of the diagram from (2).
 Statement (4) uses a fact known to all diagram chasers: Two joined commutative squares make the outside rectangle commute. You can visualize this by seeing that the three red paths shown below all have the same composite. When I am chasing a complicated diagram I trace the various paths with my finger, or in my head.
You could also show it by pointing out that $\alpha\circ F\alpha\circ F\eta=\alpha\circ\eta\circ\alpha$, but to check that I think most of us would go back and look at the diagram in (3) to see why it is true. Why not work directly with the diagram?  The definition of initiality requires that there be only one $F$algebra morphism from $\alpha:Fa\to a$ to itself. This means that the upper and lower bows in (7) commute.
 The diagonal identity arrow in (8) is justified by the fact that the upper bow is exactly the same diagram as the upper triangular diagram in (8). It follows that the upper triangle in (8) commutes. I visualize this as moving the bow down and to the left with the upper left node $Fa$ as a hinge, so that the two triangles coincide. (It needs to be flipped, too.) I should make an interactive diagram that shows this.
 The lower triangle in (8) also commutes because the square in (2) is given to be commutative.
 (Definition of isomorphism in a category) An arrow $f:a\to b$ in a category is an isomorphism if there is an arrow $g:b\to a$ for which these diagrams commute:
xx
This justifies statement (9).
Representations of mathematical objects
This is a long post. Notes on viewing.
About this post
A mathematical object, or a type of math object, is represented in practice in a great variety of ways, including some that mathematicians rarely think of as "representations".
In this post you will find examples and comments about many different types of representations as well as references to the literature. I am not aware that anyone has considered all these different ideas of representation in one place before. Reading through this post should raise your consciousness about what is going on when you do math.
This is also an experiment in exposition. The examples are discussed in a style similar to the way a Mathematica command is discussed in the Documentation Center, using mostly nonhierarchical bulleted lists. I find it easy to discover what I want to know when it is written in that way. (What is hard is discovering the name of a command that will do what I want.)
Types of representations
Using language
 Language can be used to define a type of object.
 A definition is intended to be precise enough to determine all the properties that objects of that type all have. (Pay attention to the two uses of the word "all" in that sentence; they are both significant, in very different ways.)
 Language can be used to describe an object, exhibiting properties without determining all properties.
 It can also provide metaphors, making use of one of the basic tools of our brain to understand the world.
 The language used is most commonly mathematical English, a special dialect of English.
 The symbolic language of mathematics (distinct from mathematical English) is used widely in calculations. Phrases from the symbolic language are often embedded in a statement in math English. The symbolic language includes among others algebraic notation and logical notation.
 The language may also be a formal language, a language that is mathematically defined and is thus itself a mathematical object. Logic texts generally present the first order predicate calculus as a formal language.
 Neither mathematical English nor the symbolic language is a formal language. Both allow irregularities and ambiguities.
Mathematical objects
The representation itself may be a mathematical object, such as:
 A linear representation of a group. Not only are the groups mathematical objects, so is the representation.
 An embedding of a manifold into Euclidean space. A definition given in a formal language of the first order predicate calculus of the property of commutativity of binary operations. (Thus a property can be represented as a math object.)
Visual representations
A math object can be represented visually using a physical object such as a picture, graph (in several senses), or diagram.
 The visual processing of our brain is our major source of knowledge of the world and takes about a fifth of the brain's processing power. We can learn many things using our vision that would take much longer to learn using verbal descriptions. (Proofs are a different matter.)
 When you look at a graph (for example) your brain creates a mental representation of the graph (see below).
Mental representations
If you are a mathematician, a math object such as "$42$", "the real numbers" or "continuity" has a mental representation in your brain.
 In the math ed literature, such a representation is called "mental image", "concept image", "procept", or "schema". (The word "image" in these names is not thought of as necessarily visual.)
 The procept or schema describe all the things that come to mind when you think about a particular math object: The definition, important theorems, visual images, important examples, and various metaphors that help you understand it.
 The visual images occuring in a mental schema for an object may themselves be mental representations of physical objects. The examples and theorems may be mental representations of ideas you learned from language or pictures, and so on. The relationships between different kinds of representations get quite convoluted.
Metaphors
Conceptual metaphors are a particular kind of mental representation of an object which involve mentally associating some aspects of the objects with some aspects of something else — a physical object, an image, an action or another abstract object.
 A conceptual metaphor may give you new insight into the object.
 It may also mislead you because you think of properties of the other object that the math object doesn't have.
 A graph of a function is a conceptual metaphor.
 When you say that a point on a graph "rises as it goes from left to right" your metaphor is an action.
 When you say that the cosets of a normal subgroup of a group "get along" with the group multiplication, your metaphor identifies a property they have with an aspect of human behavior.
Properties of representations
A representation of a math object may or may not
 determine it completely
 exhibit some of its properties
 suggest easy proofs of some theorems
 provide a useful way of thinking about it
 mislead you about the object's properties
 mislead you about what is significant about the object
Examples of representations
This list shows many of the possibilities of representation. In each case I discuss the example in terms of the two bulleted lists above. Some of the examples are reused from my previous publications.
Functions
Example (F1) "Let $f(x)$ be the function defined by $f(x)=x^3x$."
 This is an expression in mathematical English that a fluent reader of mathematical English will recognize gives a definition of a specific function.
 (F1) is therefore a representation of that function.
 The word "representation" is not usually used in this way in math. My intention is that it should be recognized as the same kind of object as many other representations.
 The expression contains the formula $x^3x$. This is an encapsulated computation in the symbolic language of math. It allows someone who knows basic algebra and calculus to perform calculations that find the roots, extrema and inflection points of the function $f$.
 The word "let" suggests to the fluent reader of mathematical English that (F1) is a definition which is probably going to hold for the next chunk of text, but probably not for the whole article or book.
 Statements in mathematical English are generally subject to conventions. In a calculus text (F1) would automatically mean that the function had the real numbers as domain and codomain.
 The last two remarks show that a beginner has to learn to read mathematical English.
 Another convention is discussed in the following diatribe.
Diatribe
You would expect $f(x)$ by itself to mean the value of $f$ at $x$, but in (F1) the $x$ has the property of a bound variable. In mathematical English, "let" binds variables. However, after the definition, in the text the "$x$" in the expression "$f(x)$" will be free, but the $f$ will be bound to the specific meaning. It is reasonable to say that the term "$f(x)$" represents the expression "$x^3x$" and that $f$ is the (temporary) name of the function. Nevertheless, it is very common to say "the function $f(x)$" to mean $f$.
A fluent reader of mathematical English knows all this, but probably no one has ever said it explicitly to them. Mathematical English and the symbolic language should be taught explicitly, including its peculiarities such as "the function $f(x)$". (You may want to deprecate this usage when you teach it, but students deserve to understand its meaning.)
The positive integers
You have a mental representation of the positive integers $1,2,3,\ldots$. In this discussion I will assume that "you" know a certain amount of math. Nonmathematicians may have very different mental representations of the integers.
 You have a concept of "an integer" in some operational way as an abstract object.
 "Abstract object" needs a post of its own. Meanwhile see Mathematical Objects (abstractmath) and the Wikipedia articles on Mathematical objects and Abstract objects.
 You have a connection in your brain between the concept of integer and the concept of listing things in order, numbering them by $1,2,3,\ldots$.
 You have a connection in your brain between the concept of an integer and the concept of counting a finite number of objects. But then you need zero!
 You understand how to represent an integer using the decimal representation, and perhaps representations to other bases as well.
 Your mental image has the integer "$42"$ connected to but not the same as the decimal representation "42". This is not true of many students.
 The decimal rep has a picture of the string "42" associated to it, and of course the picture of the string may come up when you think of the integer $42$ as well (it does for me — it is a an icon for the number $42$.)
 You have a concept of the set of integers.
 Students need to be told that by convention "the set of integers" means the set of all integers. This particularly applies to students whose native language does not have articles, but American students have trouble with this, too.
 Your concept of "the set of integers" may have the icon "$\mathbb{N}$" associated with it. If you are a mathematician, the icon and the concept of the set of integers are associated with each other but not identified with each other.
 For me, at least, the concept "set of integers" is mentally connected to each integer by the "element of" relation. (See third bullet below.)
 You have a mental representation of the fact that the set of integers is infinite.
 This does not mean that your brain contains an infinite number of objects, but that you have a representation of infinity as a concept, it is brainconnected to the concept of the set of integers, and also perhaps to a proof of the fact that $\mathbb{N}$ is infinite.
 In particular, the idea that the set of integers is mentally connected to each integer does not mean that the whole infinite number of integers is attached in your brain to the concept of the set of integers. Rather, the idea is a predicate in your brain. When it is connected to "$42$", it says "yes". To "$\pi$" it says "No".
 Philosophers worry about the concept of completed infinity. It exists as a concept in your brain that interacts as a meme with concepts in other mathematicians' brains. In that way, and in that way only (as far as I am concerned) it is a physical object, in particular an object that exists in scattered physical form in a social network.
Graph of a function
This is a graph of the function $y=x^3x$:
 The graph is a physical object, either on a screen or on paper
 It is processed by your visual system, the most powerful sensory management system in your brain
 It also represents the graph in the mathematical sense (set of ordered pairs) of the function $y=x^3x$
 Both the mathematical graph and the physical graph are represented by modules in your brain, which associates the two of them with each other by a conceptual metaphor.
 The graph shows some properties of the function: inflection point, going off to infinity in a specific way, and so on.
 These properties are made apparent (if you are knowledgeable) by means of the powerful pattern recognition system in your brain. You see them much more quickly than you can discover them by calculation.
 These properties are not proved by the graph. Nevertheless, the graph communicates information: for example, it suggests that you can prove that there is an inflection point near $(0,0)$.
 The graph does not determine or define the function: It is inaccurate and it does not (cannot) show all of the graph.
 More subtle details about this graph are discussed in my post Representations 2.
Continuity
Example (C1) The $\epsilon\delta$ definition of the continuity of a function $f:\mathbb{R}\to\mathbb{R}$ may be given in the symbolic language of math:
A function $f$ is continuous at a number $c$ if \[\forall\epsilon(\epsilon\gt0\implies(\forall x(\exists\delta(xc\lt\delta\impliesf(x)f(c)\lt\epsilon)))\]
 To understand (C1), you must be familiar with the notation of first order logic. For most students, getting the notation right is quite a bit of work.
 You must also understand the concepts, rules and semantics of first order logic.
 Even if you are familiar with all that, continuity is still a difficult concept to understand.
 This statement does show that the concept is logically complicated. I don't see how it gives any other intuition about the concept.
Example (C2) The definition of continuity can also be represented in mathematical English like this:
A function $f$ is continuous at a number $c$ if for any $\epsilon\gt0$ and for any $x$ there is a $\delta$ such that if $xc\lt\delta$, then $f(x)f(c)\lt\epsilon$.
 This definition doesn't give any more intuition that (C1) does.
 It is easier to read that (C1) for most math students, but it still requires intimate familiarity with the quirks of math English.
 The fact that "continuous" is in boldface signals that this is a definition. This is a convention.
 The phrase "For any $\epsilon\gt0$" contains an unmarked parenthetic insertion that makes it grammatically incoherent. It could be translated as: "For any $\epsilon$ that is greater than $0$". Most math majors eventually understand such things subconsciously. This usage is very common.
 Unless it is explicitly pointed out, most students won't notice that if you change the phrase "for any $x$ there is a $\delta$" to "there is a $\delta$ for any $x$" the result means something quite different. Cauchy never caught onto this.
 In both (C1) and (C2), the "if" in the phrase "A function $f$ is continuous at a number $c$ if…" means "if and only if" because it is in a definition. Students rarely see this pointed out explicitly.
Example (C3) The definition of continuity can be given in a formally defined first order logical theory.
 The theory would have to contain function symbols and axioms expressing the algebra of real numbers as an ordered field.
 I don't know that such a definition has ever been given, but there are various semiautomated and automated theoremproving systems (which I know little about) that might be able to state such a definition. I would appreciate information about this.
 Such a definition would make the property of continuity a mathematical object.
 An automated theoremproving system might be able to prove that $x^3x$ is continuous, but I wonder if the resulting proof would aid your intuition much.
Example (C4) A function from one topological space to another is continuous if the inverse of every open set in the codomain is an open set in the domain.
 This definition is stated in mathematical English.
 All definitions start with primitive data.
 In definitions (C1) – (C3), the primitive data are real numbers and the statement uses properties of an ordered field.
 In (C4), the data are real numbers and the arithmetic operations of a topological field, along with the open sets of the field. The ordering is not mentioned.
 This shows that a definition need not mention some important aspects of the structure.
 One marvelous example of this is that a partition of a set and an equivalence relation on a set are based on essentially disjoint sets of data, but they define exactly the same type of structure.
Example (C4) "The graph of a continuous function can be drawn without picking up the chalk".
 This is a metaphor that associates an action with the graph.
 It is incorrect: The graphs of some continuous functions cannot be drawn. For example, the function $x\mapsto x^2\sin(1/x)$ is continuous on the interval $[1,1]$ but cannot be drawn at $x=0$.
 Generally speaking, if the function can be drawn then it can be drawn without picking up the chalk, so the metaphor provides a useful insight, and it provides an entry into consciousnessraising examples like the one in the preceding bullet.
References
 1.000… and .999… (post)

Concept Image and Concept Definition in Mathematics with particular reference to Limits and Continuity, David Tall and Schlomo Vinner, 1981
 Conceptual blending (post)
 Conceptual blending (Wikipedia)
 Conceptual metaphors (Wikipedia)
 Convention (abstractmath)
 Definitions (abstractmath)
 Embodied cognition (Wikipedia)
 Handbook of mathematical discourse (see articles on conceptual blend, mental representation, representation, metaphor, parenthetic assertion)
 Images and Metaphors (abstractmath).
 The interplay of text, symbols and graphics in math education, Lin Hammill
 Math and the modules of the mind (post)
 Mathematical discourse: Language, symbolism and visual images, K. L. O’Halloran.
 Mathematical objects (abmath)
 Mathematical objects (Wikipedia)
 Mathematical objects are “out there?” (post)
 Metaphors in computing science (post)
 Procept (Wikipedia)
 Representations 2 (post)
 Representations and models (abstractmath)
 Representations II: dry bones (post)
 Representation theorems (Wikipedia) Concrete representations of abstractly defined objects.
 Representation theory (Wikipedia) Linear representations of algebraic structures.
 Semiotics, symbols and mathematical visualization, Norma Presmeg, 2006.
 The transition to formal thinking in mathematics, David Tall, 2010
 Theory in mathematical logic (Wikipedia)
 What is the object of the encapsulation of a process? Tall et al., 2000.
 Where mathematics comes from, by George Lakoff and Rafael Núñez, Basic Books, 2000.
 Where mathematics comes from (Wikipedia) This is a review of the preceding book. It is a permanent link to the version of 04:23, 25 October 2012. The review is opinionated, partly wrong, not well written and does not fit the requirements of a Wikipedia entry. I recommend it anyway; it is well worth reading. It contains links to three other reviews.
Notes on Viewing
This post uses MathJax. If you see mathematical expressions with dollar signs around them, or badly formatted formulas, try refreshing the screen. Sometimes you have to do it two or three times.
Idempotents by sketches and forms
Sketches and forms
A sketch of a mathematical structure is a collection of objects and arrows that make up a digraph (directed graph), together with some specified cones, cocones and diagrams in the digraph. A model of the sketch is a digraph morphism from the digraph to some category that takes the cones to limit cones, the cocones to colimit cocones, and the diagrams to commutative diagrams. A morphism of models of a sketch from one model to another in the same category is a natural transformation. Sketches can be used to define all kinds of algebraic structures in the sense of universal algebra, and many other types of structures (including many types of categories).
There are many structures that sketches cannot sketch. Forms were first defined in [4]. They can define anything a sketch can define and lots of other things. [5] gives a leisurely description of forms suitable for people who have a little bit of knowledge of categories and [1] gives a more thorough description.
An idempotent is a very simple kind of algebraic structure. Here I will describe both a sketch and a form for idempotents. In another post I will do the same for binops (magmas).
Idempotent
An idempotent is a unary operation $u$ for which $u^2=u$.
 If $u$ is a morphism in a category whose morphisms are set functions, a function $u:S\to S$ is an idempotent if $u(u(x))=u(x)$ for all $x$ in the domain.
 Any identity element in any category is an idempotent.
 A nontrivial example is the function $u(x,y):=(x,0)$ on the real plane.
Any idempotent $u$ makes the following diagram commute
and that diagram can be taken as the definition of idempotent in any category.
A sketch for idempotents
The sketch for idempotents contains a digraph with one object and one arrow from that object to itself (above left) and one diagram (above right). It has no cones or cocones. So this is an almost trivial example. When being expository (well, I can hardly say "when you are exposing") your first example should not be trivial, but it should be easy. Let's call the sketch $\mathcal{S}$.
 The diagram looks the same as the green diagram above. It is in black, because I am showing things in syntax (things in sketches and forms) in black and semantics (things in categories of models) in green.
 The green diagram is a commutative diagram in some category (unspecified).
 The black diagram is a diagram in a digraph. It doesn't make sense to say it is commutative because digraphs don't have composition of arrows.
 Each sketch has a specific digraph and lists of specific diagrams, cones and cocones. The left digraph above is not in the list of diagrams of $\mathcal{S}$ (see below).
The definition of sketch says that every diagram in the official list of diagrams of a given sketch must become a commutative diagram in a model. This use of the word "become" means in this case that a model must be a digraph morphism $M:\mathcal{S}\to\mathcal{C}$ for some category $\mathcal{C}$ for which the diagram below commutes.
 $G$ is "generic" because anything you prove about $G$ is true of every model of $\mathcal{S}$ in any category.
 In particular, in the category $\text{Th}(\mathcal{S})$, $G(f)\circ G(f)=G(f)$.
 $G$ is a universal morphism in the sense of category theory: It lifts any model $M:\mathcal{S}\to\mathcal{C}$ to a unique functor $\bar{M}=M\circ G:\text{Th}(\mathcal{S})\to\mathcal{C}$ which can therefore be regarded as the same model. See Note [2].
Sketching categories
You can sketch categories with a sketch CatSk containing diagrams and cones, but no cocones. This is done in detail in [3]. The resulting theory $\text{Th}(\mathbf{CatSk})$ is required to be the least categorywithfinitelimits generated by $\mathcal{S}$ with the diagrams becoming commutative diagrams and the cones becoming limit cones. This theory is the FLTheory for categories, which I will call ThCat (suppressing mention of FL).
Doctrines
In general the theory of a particular kind of structure contains a parameter that denotes its doctrine. The sketch $\mathcal{S}$ for idempotents didn't require cones, but you can construct theories $\text{Th}(\mathcal{S})$, $\text{Th} (\text{FP},\mathcal{S})$ and $\text{Th}(\text{FL},\mathcal{S})$ for idempotents (FP means it is a category with finite products).
In a strong sense, all these theories have the same models, namely idempotents, but the doctrine of the theory allows you to use more mechanisms for proving properties of idempotents. (The doctrine for $\text{Th}(\mathcal{S})$ provides for equational proofs for unary operations only, a doctrine which has no common name such as FP or FS.) The paper [1] is devoted to explicating proof in the context of forms, using graphs and diagrams instead of formulas that are strings of symbols.
Describing composable pairs of arrows
The form for any type of structure is constructed using the FL theory for some type of category, for example category with all limits, cartesian closed category, topos, and so on. The form for idempotents can be constructed in ThCat (no extra structure needed). The form for reflexive function spaces (for example) needs the FL theory for cartesian closed categories (see [5]).
Such an FL theory must contain objects $\text{ob}$ and $\text{ar}$ that become the set of objects and the set of arrows of the category that a model produces. (Since FL theories have models in any category with finite limits, I could have said "object of objects" and "object of arrows". But in this post I will talk about only models in Set.)
ThCat contains an object $\text{ar}_2$ that represents composable pairs of arrows. That requires a cone to define it:
This must become a limit cone in a model.
 I usually show cones in blue.
 $\text{dom}$ and $\text{cod}$ give (in a model) the domain and codomain of an arrow.
 $\text{lfac}$ gives the left factor and $\text{rfac}$ gives the right factor. It is usually useful to give suggestive names to some of the projections in situations like this, since they will be used elsewhere (where they will be black!).
 The objects and arrows in the diagram (including $\text{ar}_2$) are already members of the FL theory for categories.
 This diagram is annotated in green with sample names of objects and arrows that might exist in a model. Atish and I introduced that annotation system in [1] to help you chase the diagram and think about what it means.
This cone is a graphbased description of the object of composable arrows in a category (as opposed to a linguistic or stringbased description).
Describing endomorphisms
Now an idempotent must be an endomorphism, so we provide a cone describing the object of endomorphisms in a category. This cone already exists in the FL theory for categories.
 $\text{loop}$ is a monomorphism (in fact a regular mono because it is the mono produced by an equalizer) so it is not unreasonable to give the element annotation for $\text{endo}$ and $\text{ar}$ the same name.
 "$\text{dc}$" takes $f$ to its domain and codomain.
 $\text{loop}$ and "$\text{dc}$" were not created when I produced the cone above. They were already in the FL theory for categories.
This particular purple cone is the limit cone defining $\text{endo}$ just defined. Now $\text{diag}$ is a specific arrow in the FL theory for categories. In a model of the theory (which is a category in Set or in some other category) takes an endomorphism to the corresponding pair of composable arrows.
The object of idempotents
Now using these arrows we can define the object $\text{idm}$ of idempotents using the diagram below. See Note [3].
Idm is an object in ThCat. In any category, in other words in any model of ThCat, idm becomes the set of idempotent arrows in that category.
In the terminology of [5], the object idm is the form for idempotents, and the cone it is the limit of is the description of idempotent.
Now take ThCat and adjoin an arrow $g:1\to\text{idm}$. You get a new FL category I will call the FLtheory of the form for idempotents. A model of the theory of the form in Set is a category with a specified idempotent. A particular example of a model of the form idm in the category of real linear vector spaces is the map $u(x,y):=(x,0)$ of the (set of points of) the real plane to itself (it is an idempotent endomorphism of $\textbf{R}^2$).
This example is typical of forms and their models, except in one way: Idempotents are also sketchable, as I described above. Many mathematical structures can be perceived as models of forms, but not models of sketches, such as reflexive function spaces as in [5].
Notes
[1] The diagrams shown in this post were drawn in Mathematica. The code for them is shown in the notebook SketchFormExamples.nb . I am in the early stages of developing a package for drawing categorical diagrams in Mathematica, so this notebook shows the diagrams defined in very primitive machinecodelike Mathematica. The package will not rival xypic for TeX any time soon. I am doing it so I can produce diagrams (including 3D diagrams) you can manipulate.
[2] In practice I would refer to the names of the objects and arrows in the sketch rather than using the M notation: I might write $f\circ f=f$ instead of $M(f)\circ M(f)=M(f)$ for example. Of course this confuses syntax with semantics, which sounds like a Grievous Sin, but it is similar to what we do all the time in writing math: "In a semigroup, $x$ is an idempotent if $xx=x$." We use same notation for the binary operation for any semigroup and we use $x$ as an arbitrary element of most anything. Actually, if I write $f\circ f=f$ I can claim I am talking in the generic model, since any statement true in the generic model is true in any model. So there.
[3] In the Mathematica notebook SketchFormExamples.nb in which I drew these diagrams, this diagram is plotted in Euclidean 3space and can be viewed from different viewpoints by running your cursor over it.
References
[1] Atish Bagchi and Charles Wells, GraphBase Logic and Sketches, draft, September 2008, on ArXiv.
[2] Michael Barr and Charles Wells, Category Theory for Computing Science (1999). Les Publications CRM, Montreal (publication PM023).
[3] Michael Barr and Charles Wells, Toposes, Triples and Theories (2005). Reprints in Theory and Applications of Categories 1.
[4] Charles Wells, A generalization of the concept of sketch, Theoretical Computer Science 70, 1990
[5] Charles Wells, An Introduction to forms.
Showing categorical diagrams in 3D
The interactive examples in this post require installing Wolfram CDF player, which is free and works on most desktop computers using Firefox, Safari and Internet Explorer, but not Chrome. The source code is the Mathematica Notebook algebra1.nb, which is available for free use under a Creative Commons AttributionShareAlike 2.5 License. The notebook can be read by CDF Player if you cannot make the embedded versions in this post work.
In GraphBased Logic and Sketches, Atish Bagchi and I needed to construct a lot of cones based on fairly complicated diagrams. We generally show the base diagram and left the reader to imagine the cone. This post is an experiment in presenting such a diagram in 3D, with its cone and other constructions based on it.
To understand this post, you need a basic understanding of categories, functors and limit cones (see References).
The notebook and CDF files that generate this display may be downloaded from here:
These files may be used and modified as you wish according to the Creative Commons rule listed under “Permissions” (at the top of the window).
The sketch for categories: composition
A finitelimit sketch (FL sketch) is a category with finite limits given by specifying certain nodes and arrows, commutative diagrams using these nodes and arrows, and limit cones based on diagrams using the given nodes and arrows. A model of an FL sketch is a finitelimitpreserving functor from the FL sketch into some category . Detailed descriptions of FLsketches are in References [1], [2] and [3] (below).
Categories themselves may be sketched by FLsketches. Here I will present the part of the sketch that constructs (in a model) the object of composites of two arrows. This is the specification for composite:
 The composite of two arrows and is defined if and only if .
 The composite is denoted by .
 The domain of is and the codomain is .
We start with a diagram in the FL sketch for categories that gives the data corresponding to two arrows that may be composed. This diagram involves nodes ob and ar, which in a model become the object of objects and the object of arrows of the category object in . (Suppose is the category of sets; then the model is simply a small category. The node ob goes to the set of objects of the small category and ar goes to the set of arrows.) The arrows labeled dom and cod take (in a model) an arrow to its domain and codomain respectively. Here is the diagram:
You can move the diagram around in three dimensions to see it from different perspectives. (Of course it isn’t really in three dimensions. Your eyestobrain module reconstructs the illusion of three dimensions when you twirl the diagram around.)
Note that this is a diagram, not a directed graph (digraph). (In the paper, Atish and I, like most category theorists, say “graph” instead of “digraph”.) It has an underlying digraph (see Chapter 2 of GraphBased Logic and Sketches), but the labeling of several different nodes of the underlying digraph by the name of the same node of the sketch is meaningful.
Here, the key fact is that in the diagram there are two arrows, one labeled dom and the other cod, to the same node labeled ob, and two other arrows to two different nodes labeled ob.
Now click c1.
This shows a cone over the diagram. One of the nodes in the sketch must be cp (in other words given beforehand; that is, we are specifying not only that the blue stuff is a limit cone but that the limit is the node cp.) In a model, this cone must become a limit cone. It follows from the properties of limits that the elements of cp in the model in Sets are pairs of arrows with the property that one has a codomain that is the same as the domain of the other. The label “cp” stands for “compatible pairs”.
Now click c2.
The green stuff is a diagram showing two arrows from the node labeled ar to the left and right nodes labeled ob in the original black diagram. This is not a cone; it is just a diagram. In a model, any arrow in the vertex must have domain the same as the domain of one of the arrows in the compatible pair, and codomain the same as the codomain of the other arrow of the pair. Thus in the model, an arrow living in the set labeled with “ar” in green must satisfy requirement 3 in the specification for composition given above.
Note that the requirement that the green diagram be commutative in a model is vacuous, so it doesn’t matter whether we specify it specifically as a diagram in the sketch or not.
Now click c3.
The arrow labeled comp must be specified as an arrow in the sketch. We want its value to be the composite of an element of cp in a model, in other words a compatible pair of arrows. At this point that will not necessarily be true. But all can be saved:
Now click c4.
We must specify that the diagram given by the thick arrows must be a diagram of the sketch. The fact that it must become commutative in a model means exactly that the red arrow comp from cp to ar takes a compatible pair to an arrow that satisfies requirements 1–3 of the specification of composite shown above.
References
 Peter T. Johnstone, Sketches of an Elephant: A Topos Theory Compendium, Volume 2 (Oxford Logic Guides 44), by Oxford University Press, ISBN 9780198524960.
 Michael Barr and Charles Wells, Category theory for computing science (1999). (This is the easiest to start with but it doesn’t get very far.)
 Michael Barr and Charles Wells, Toposes, Triples and Theories (2005). Reprints in Theory and Applications of Categories 1.
A tiny step towards killing stringbased math
I discussed endographs of real functions in my post Endographs and cographs of real functions. Endographs of finite functions also provide another way of thinking about functions, and I show some examples here. This is not a new idea; endographs have appeared from time to time in textbooks, but they are not used much, and they have the advantage of revealing some properties of a function instantly that cannot be seen so easily in a traditional graph or cograph.
In contrast to endographs of functions on the real line, an endograph of a finite function from a set to itself contains all the information about the function. For real functions, only some of the arrows can be shown; you are dependent on continuity to interpolate where the infinite number of intermediate arrows would be, and of course, it is easy to produce a function, with, say, smallscale periodicity, that the arrows would miss, so to speak. But with an endograph of a finite function, WYSIATI (what you see is all there is).
Here is the endograph of a function. It is one function. The graph has four connected components.
You can see immediately that it is a permutation of the set , and that it is involution (a permutation for which ). In cycle notation, it is the permutation , and the connected components of the endograph correspond to the cycle structure.
Here is another permutation:
You can see that to get you would have to have , since you have to apply the 3cycle 3 times and the transposition twice to get the identity. The cycle structure tells you this, but you have to visualize it acting to see that. The endograph gives the newbie a jumpstart on the visualization. “The power to understand and predict the quantities of the world should not be restricted to those with a freakish knack for manipulating abstract symbols” (Brett Victor). This is an argument for insisting that this permutation is the endograph, and the abstract string of symbols is a representation of secondary importance. [See Note 1.]
Here is the cograph of the same function. It requires a bit of visualization or tracing arrows around to see its cycle structure.
If I had rearranged the nodes like this
the cycle structure would be easier to see. This does not indicate as much superiority of the endograph metaphor over the cograph metaphor as you might think: My endograph code [Note 2] uses Mathematica’s graphdisplaying algorithm, which automatically shows cycles clearly. The cograph code that I wrote specifies the placement of the nodes explicitly, so I rearranged them to obtain the second cograph above using my knowledge of the cycle structure.
The following endographs of functions that are not permutations exhibit the general fact that the graph of a finite function consists of cycles with trees attached. This structure is obvious from the endographs, and it is easy to come up with a proof of this property of finite functions by tracing your finger around the endographs.
This is the endograph of the polynomial over the finite field of 11 elements.
I constructed this explicitly by writing a list of rules, and then used Mathematica’s interpolating polynomial to determine that it is given by the polynomial
in GF[17].
Quite a bit is known about polynomials over finite fields that give permutations. For example there is an easy proof using interpolating polynomials that a polynomial that gives a transposition must have degree . The best reference for this stuff is Lidl and Niederreiter, Introduction to Finite Fields and their Applications
The endographs above raise questions such as what can you say about the degree or coefficients of a polynomial that gives a digraph like the function below that is idempotent (). Students find idempotence vs. involution difficult to distinguish between. Digraphs show you almost immediately what is going on. Stare at the digraph below for a bit and you will see that if you follow to a node and then follow it again you stay where you are (the function is the identity on its image). That’s another example of the insights you can get from a new metaphor for a mathematical object.
The following function is not idempotent even though it has only trivial loops. But the digraph does tell you easily that it satisfies .
Notes
[1] Atish Bagchi and I have contributed to this goal in Graph Based Logic and Sketches, which gives a bare glimpse of the possibility of considering that the real objects of logic are diagrams and their limits and morphisms between them, rather than hardtoparse strings of letters and logical symbols. Implementing this (and implementing Brett Victor’s ideas) will require sophisticated computer support. But that support is coming into existence. We won’t have to live with stringbased math forever.
[2] The Mathematica notebook used to produce these pictures is here. It has lots of other examples.