Explain Cantor's Diagonal Method to a Non-Math Person

In a way, Cantor’s first proof of the uncountability of the reals (which wasn’t by diagonalization) is more directly along the lines of this picture. [Indeed, my feelings are that Cantor’s first proof is the only one which truly deals with the reals, as I prefer to think of the diagonalization proof as not dealing with the reals at all, but rather with 2^N (which, under the standard topology, is homeomorphic to 3^N, 10^N, etc., and to Cantor space, but not the reals), P(N), N^N [Baire space, homeomorphic to the irrationals but not the reals], and other such powers. Of course, in mathematical orthodoxy, topology is extra structure imposed on a set after the fact, so we can construct discontinuous functions, and, in particular, identify the reals with all of the above; however, I feel, superior foundational frameworks for most purposes are those which keep these distinct (as can be accomplished very easily by switching from classical to intuitionistic logic (which is consistent with the principle that all real functions are continuous), though I would want to push even further), in recognition of the fundamental intuition that, among other things, the continuum is a connected space and thus should admit only constant functions into 2].

Anyway, stuffing the ideological hijack, the way Cantor’s first proof works is as follows: suppose you have some countable sequence of reals. We shall construct from it a system of inequalities such that nothing in the list satisfies all of these constraints, but some real number does. This will show that the list is incomplete.

How do we do so? We’ll start off with no constraints and start scanning through the list. Every time we find a number C in the list which satisfies all our constraints so far, we’ll add a new constraint which rules it out [either “The number I’m thinking of is less than C” or “The number I’m thinking of is greater than C”, alternating], and then keep going through the rest of the list, carrying out the same process.

Obviously, by design, no number in the list will satisfy all the constraints we ever announce. But how do we know some real number does? Well, let A be the least upper bound of all the values our number is constrained to be above (call these the “under-numbers”), and let B be the greatest lower bound of all the values our number is constrained to be below (call these the “over-numbers”). Note that, given any under-number L and over-number R, one of the two constraints was added before the other, so that either L satisfies the constraint of being under R or R satisfies the constraint of being over L; thus, either way, L < R. Since every under-number is less than every over-number, we can conclude that A <= B. Now, either there was a last constraint generated (in which case, because under- and over-numbers are disjoint, we must have that A < B) or there was no last constraint generated (in which case, A is a strict upper bound on the under-numbers, and B is a strict lower bound on the over-numbers). Either way, there is a number between A and B which satisfies all the constraints. Q.E.D.

I’m not following… What if my constraints are “The number is greater than zero” “The number is less than 1” “The number is less than 1/2” “The number is less than 1/3” “The number is less than 1/4” “The number is less than 1/5” and so on?

Remember, we alternate between “less than” and “greater than” constraints; either there are finitely many of both kinds, or there are infinitely many of both kinds. In the first case, the conjunction of all the constraints is an open interval of the form (A, B) where A < B. In the second case, the conjunction of all the constraints is a closed interval [A, B], where A <= B. Either way, the interval is inhabited.

(Since I began to bring it up, I might as well clarify that, in intuitionistic logic, it is consistent for the naturals to surject the reals; the above proof (which deals with reals qua reals) falters on just this excluded middle. On the other hand, the diagonalization proof goes down just as smoothly intuitionistically as anywhere else; it just happens to address a space not identified with the reals.)

Ah, OK, I hadn’t noticed that. But then there’s a problem that we could choose two constraints “The number I’m thinking of is less than 1” “The number I’m thinking of is greater than 2”, and there isn’t any number left which meets both constraints. I could get around this by requiring that each number I choose must satisfy all constraints established thus far, but that doesn’t help, either: If there are constraints on the number I can add to the list at each point, then I could argue that maybe I would be able to hit all of the reals, if I weren’t so constrained (I couldn’t, for instance, start my list with 0 1 -1, but maybe something like that is necessary to encompass all reals).

While I don’t have a particularly rigorous understanding of this topic, it’s one of my favorite parts of math. There are so many seeming paradoxes in it… for instance, pick any two distinct real numbers between 0 and 1. Not only is there guaranteed to be a rational number between those two real numbers, there are an INFINITE number of rational numbers between those two real numbers. So for any two real numbers there are INFINITE rationals between them. So seems like there should be more rationals than reals, right?

Except that the complete opposite is true.

Is the question of whether there is an infinite set larger than countable but smaller than uncountable still unsolved?

I have a 4-year degree in Mathematics (and an MBA) and this thread made my head hurt. So, ok… it’s been awhile. But I will confess that perhaps I never “got it.” Which is why as a student sitting in advanced calculus, something finally flashed in my head. “Why am I majoring in math? Sure it impresses the babes, but hey…”

I kicked ass in my math classes and actually understood it all for about 15 minutes after I graduated. Then, like a helium balloon, it slowly leaked out. Now I’m lucky to be able to do some basic statistics. I still love the math questions out here, though. Makes me exercise my brain, so for all of you math folks who can still pump this stuff through your grey matter, I salute you!

Anyhow, a question for Cantor. So what? What is he proving that has any practical application? That infinity doesn’t always equal infinity? uh… ok. Does that help me open my pop-tarts in the morning? I think not! So in the whole scheme of things, what did this proof help? Did it expand set theory, or number theory to another level?

SFP

By definition, any set larger than a countable set is uncountable. You’re thinking of the continuum hypothesis, which is known to be independent of the standard axioms of set theory. In other words, you can assume it’s true, or false, or only true on Tuesdays, and no one will ever prove you wrong.

Cantor’s notions are foundational to modern set theory and have important consequences for computability theory.

If by “smaller than uncountable”, you mean “smaller than the reals”, then it’s not only unsolved, but unsolvable. It’s impossible to prove the question one way or the other using the standard axioms. You can, if you like, introduce another axiom that just flat-out states that there is such a set, or you can introduce an axiom that flat-out states that there is not such a set, and either way, it’s consistent with all of the other axioms.

Did I just read all the way through this thread without seeing a single reference to “A Certain Ambiguity” by Suri and Bal? You people need to read this.

Remember, we only add a new constraint to the collection we are constructing when we run across something in the list which satisfies all the constraints we’ve added so far, thus preventing this situation.

Indeed. Of course, there are also infinitely many reals between every two reals, infinitely many irrationals between every two rationals, infinitely many rationals between every two irrationals, infinitely many reals between every two rationals, etc. So this kind of reasoning couldn’t have been expected to provide a separation in size anyway.

Why, does number theory help you open your pop-tarts in the morning?

As for set theory, as ultrafilter noted, it would be fairly safe to say it did not even really exist as a mathematical subject until the work of Cantor. But one of the reasons I like to highlight the generalized version of Cantor’s theorem is that it divorces it from what people think of as set theory, and shows how it applies to pretty much any field of mathematical interest: the applications in logic and computer science are legion (and some have been mentioned already), but the theorem also applies in various forms in topology, analysis, linear algebra, …; in any field where you have a notion of functions of multiple arguments, and can compose these in the usual ways (e.g., computable functions, continuous functions, n-times differentiable or smooth functions, linear functions, functions definable in a particular language, functions which can be quickly calculated, …), the theorem’s effects reverberate.

Huh! When you put it that way, maybe it DOES help me open up my pop tarts. Neat!

…And looking back, I see that you also mentioned that already. So we’re allowed to add any number we want to our list, but we don’t always add a constraint. OK, I think I’ve got it now.

I’ve got “Introduction to Mathematical Logic” somewhere at home. I’ll dig it out tonight and answer, if I remember :stuck_out_tongue:

Computer Scientist here. (And a Theory guy as well.)

Cantor’s proof led directly to the single most important result in the history of Computer Science (IMO): Turing’s proof of the unsolvability of the Halting Problem. This implies that there is no automated way to solve a legion of important problems, such as proving programs correct and the like.

The diagonalization technique is also used to show that complexity classes are provably distinct. E.g., that there really are problems that require quadratic time and cannot be solved in linear time. This is the basis of the entire field of Complexity Theory. (OTOH, it has been proven that diagonalization cannot be used to proved P vs. NP.) All the proofs that determine the difficulty of solving various logics (e.g., Presburger Arithmetic) all directly or indirectly rely on diagonalization.

One of my PhD student’s thesis involved using diagonalization to show separation of problems involving communication. I.e., what you can do sending log bits vs. linear bits.

Early in teaching Theory of Computation, I use Cantor’s proof to show that the set of all problems (mappings of strings to yes/no) is uncountable while the number of solutions (programs) is countable. I.e., there are more problems than solutions. Many, many more. I think it is crucial for Computer Science majors to know that there are limits to what computers can do.

Along with it’s spinoff Goedel’s Incompleteness Theorem and the Heisenberg Uncertainty principle, by the late 1930s there was revelation that within a given system there are limits as to what you can do within that system. This has had a major impact in the philosophy of Science.

And it all started with Cantor.

Just to be clear, the Uncertainty Principle does not derive from a diagonalization argument. In fact, it’s not even unique to quantum mechanics: It was known and accepted in classical waves long before quantum appeared on the scene. It’s just that due to wave-particle duality, it suddenly applied to things that we had previously thought we could be certain about.

Weird. Doesn’t that imply that it is in fact true? That no such set exists? Because if such a set existed, I could describe it, and show that it was larger than countable but smaller than uncountable. And that would prove that such a set existed.

In other words, for a hypothesis of the form “Something of type A exists”, isn’t proving the hypothesis unprovable the same as proving it false?

Since mappings of strings to yes/no can be mapped trivially to real numbers between 0 and 1 in binary notation, that proves that most real numbers cannot be the result of a computer program, even if many transcendental numbers (e.g., e and pi) can be calculated by a computer program. But we cannot specify or define any of these uncalculatable numbers, because if we could define one, we could write a computer program to calculate it to any required accuracy.

It’s possible that such a set exists but that it’s not finitely describable.

There are definable numbers that are not computable. Chaitin’s constant is the most famous example.