First we have to get down an explicit condition on convergence of complex sequences. In any metric space we can say that the sequence converges to a limit if for every there is some so that for all . Of course, here we’ll be using our complex distance function . Now we just have to replace any reference to real absolute values with complex absolute values and we should be good.
Cauchy’s condition comes in to say that the series converges if and only for every there is an so that for all the sum .
Similarly, we say that the series is absolutely convergent if the series is convergent, and this implies that the original series converges.
Since the complex norm is multiplicative, everything for the geometric series goes through again: if , and it diverges if . The case where is more complicated, but it can be shown to diverge as well.
The ratio and root tests are basically proven by comparing series of norms with geometric series. Since once we take the norm we’re dealing with real numbers, and since the norm is multiplicative, we find that the proofs go through again.
Now that we’ve got some topological fields to use as examples, let’s focus in on power series over or .
Remember that a power series is like an infinite polynomial. In fact, we introduced a topology so we could see in any power series a sequence of polynomials that converged to it. To be explicit, we write the series as a limit
where the are coefficients selected from our base field.
Now evaluation of power series is specified by two conditions: it should agree with evaluation of polynomials when we’ve got a power series that cuts off after a finite number of terms, and it should be continuous.
The first condition says that each of our approximating polynomials should evaluate just the same as it did before. That is, if we cut off after the degree- term and evaluate at the point in the base field, we should get .
The second condition says that evaluation should preserve limits. And we’ve got a sequence right here: the th term is the evaluation of the th approximating polynomial! So the power series should evaluate to the limit . If this limit exists, that is. And that’s why we need a topological field to make sense of evaluations.
Now we’re back in the realm of infinite series, and taking the limit of a sequence of partial sums. The series in question has as its th term the evaluated monomial . We can start using our old techniques to sum these series.
Prodded by some comments, I think I’ll go even further afield from linear algebra. It’s a slightly different order than I’d originally thought of, but it will lead to some more explicit examples when we’re back in the realm of linear algebra, so it’s got its own benefits.
I’ll note here in passing that mathematics actually doesn’t proceed in a straight line, despite the impression most people get. The lower-level classes are pretty standard, yes — natural-number arithmetic, fractions, algebra, geometry, calculus, and so on. But at about this point where most people peter out, the subject behaves more like an alluvial fan — many parallel rivulets carry off in different directions, but they’re all ultimately part of the same river. So in that metaphor, I’m pulling a bit of an avulsion.
Anyhow, power series are sort of like polynomials, except that the coefficients don’t have to die out at infinity. That is, when we consider the algebra of polynomials as a vector space over it’s isomorphic to the infinite direct sum
but the algebra of power series — written — is isomorphic to the infinite direct product
It’s important to note here that the do not form a basis here, since we can’t write an arbitrary power series as a finite linear combination of them. But really they should behave like a basis, because they capture the behavior of every power series. In particular, if we specify that then we have a well-defined multiplication extending that of power series.
I don’t want to do all the fine details right now, but I can at least sketch how this all works out, and how we can adjust our semantics to talk about power series as if the were an honest basis. The core idea is that we’re going to introduce a topology on the space of polynomials.
So what polynomials should be considered “close” to each other? It turns out to make sense to consider those which agree in their lower-degree terms to be close. That is, we should have the space of tails
as an open set. More concretely, for every polynomial with degree there is an open set consisting of those polynomials so that divides the difference .
Notice here that any power series defines, by cutting it off after successively higher degree terms, a descending sequence of these open sets. More to the point, it defines a sequence of polynomials. If the power series’ coefficients are zero after some point — if it’s a polynomial itself — then this sequence stops and stays at that polynomial. But if not it never quite settles down to any one point in the space. Doesn’t this look familiar?
Exactly. Earlier we had sequences of rational numbers which didn’t converge to a rational number. Then we completed the topology to give us the real numbers. Well here we’re just doing the same thing! It turns out that the topology above gives a uniform structure to the space of polynomials, and we can complete that uniform structure to give the vector space underlying the algebra of power series.
So here’s the punch line: once we do this, it becomes natural to consider not just linear maps, but continuous linear maps. Now the images of the can’t be used to uniquely specify a linear map, but they will specify at most one value for a continuous linear map! That is, any power series comes with a sequence converging to it — its polynomial truncations — and if we know the values then we have uniquely defined images of each of these polynomial truncations since each one is a finite linear combination. Then continuity tells us that the image of the power series must be the limit of this sequence of images, if the limit exists.