Products of Metric Spaces
Shortly we’re going to need a construction that’s sort of interesting in its own right.
The famous Pythagorean theorem tells us that in a right triangle the length of the side opposite the right angle stands in a certain relation to the lengths and of the other two sides: . So let’s say we’ve got metric spaces and . For the moment we’ll think of them as being perpendicular and define a distance function on by
The quantity inside the radical here must be nonnegative, since it’s the sum of two nonnegative numbers. Since the result needs to be nonnegative, we take the unique nonnegative square root.
Oops, I don’t think I mentioned this before. Since the function has as its derivative, it’s always increasing where is positive. And since we can eventually a square above any real number we choose, its values run from zero all the way up to infinity. Now the same sort of argument as we used to construct the exponential function gives us an inverse sending any nonnegative number to a unique nonnegative square root.
Okay, that taken care of, we’ve got a distance function. It’s clearly nonnegative and symmetric. The only way for it to be zero is for the quantity in the radical to be zero, and this only happens if each of the terms and are zero. But since these are distance functions, that means and , so .
The last property we need is the triangle inequality. That is, for any three pairs , , we have the inequality
Substituting from the definition of we get the statement
The triangle inequalities for and tell us that and . So if we make these substitutions on the left, it increases the left side of the inequality we want. Thus if we can prove the stronger inequality
we’ll get the one we really want. Now since squaring preserves the order on the nonnegative reals, we can find this equivalent to
Some cancellations later:
We square and cancel some more:
Moving these terms around we find
So at the end of the day, our triangle inequality is equivalent to asking if a certain quantity squared is nonnegative, which it clearly is!
Now here’s the important thing at the end of all that calculation: this is just one way to get a metric on the product of two metric spaces. There are many other ones which give rise to different distance functions, but the same topology and the same uniform structure. And often it’s the topology that we’ll be most interested in.
In particular, this will give us a topology on any finite-dimensional vector space over the real numbers, but we don’t want to automatically equip that vector space with this norm unless we say so very explicitly. In fact, we don’t even want to make that same assumption about the two spaces being perpendicular to each other. The details of exactly why this is so I’ll leave until we get back to linear algebra, but I want to be clear right now that topology comes for free, but we may have good reason to use different “distances”.