# Math 4310 Handout - Quotient Vector Spaces

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1 Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable since quotient spaces can be a bit harder to wrap one s head around, but they re useful for a deeper understanding of certain concepts, and they re essential in other areas of abstract algebra and in many uses of linear algebra elsewhere in mathematics. The definition of a quotient space is a lot like the definition of Z/nZ - it proceeds by first defining an equivalence relation, and then working with the resulting quotient set. Definition 1. Let V be a vector space and W a subspace. We can define an equivalence relation on V (with we ll denote for now) by v v if v v W. It s an easy exercise to see that the axioms of W being a subspace implies this is actually an equivalence relation. (You should think through why this is true, and especially where the hypothesis W is nonempty is important). Definition 2. Let V be a vector space and W a subspace. Then the equivalence class of a vector v under the relation above is the set v + W = {v + w : w W }; this is called an affine subspace a coset for v and W. Again, it s pretty straightforward to see that the set we ve called v + W is actually the equivalence class of v: certainly we have v v + w for any w W, and conversely if v v then v v W means v = v + (v v) v + W. A geometric example of this to keep in mind is if V = R 2 is the plane and W is some line through the origin. Then the cosets of W are all of the translates of that line; so we have a partition of the plane by separating it into a bunch of parallel lines. Similarly if V = R 3 and W is a plane through the origin, its cosets are all of the parallel plane, and we can imagine this as filling up R 3 with a stack of planes. Definition 3. Let V be a vector space and W a subspace. We let V/W denote the quotient set V/, i.e. the set of all cosets v + W ; we call it the quotient vector space. So in our two examples above, V/W is a set of parallel lines or a set of parallel planes, respectively. As the notation suggests, this is actually a vector space. Proposition 4. The quotient set V/W is a vector space itself, with addition and scalar multiplication defined by (v + W ) + (u + W ) = (v + u) + W a (v + W ) = (a v) + W. Proof. The main thing we need to do here is check that these operations are well-defined. So suppose v + W = v + W and u + W = u + W ; for well-definedness of addition we need to check that (v + u) + W = (v + u ) + W. But v + W = v + W means v v W, and similarly u + W = u + W implies u u W. Since W is a subspace and thus closed under addition, we can conclude (v v ) + (u u ) = (v + u) (v + u ) W, and thus (v + u) + W = (v + u ) + W. Similarly, for any a F, v v W means a(v v ) = av av W and thus av + W = av + W, so scalar multiplication is well-defined. So this justifies that V/W has well-defined addition and scalar multiplication operations. To check it s a vector space we then need to verify the eight axioms. But these are all essentially immediate from the appropriate axioms in V ; for instance we can prove distributivity of scalar multiplication over field addition by (a + b) (v + W ) = ((a + b)v) + W = (av + bv) + W = (av + W ) + (bv + W ) = a(v + W ) + b(v + W ), where the second equality uses the same axiom in V, and the rest just come from the definitions of the operations on V/W. 1

2 Since this is a quotient set, it can be a bit hard to think about clearly - it s a set of sets, which we re somehow defining operations on. But in this case the sets are pretty simple: just like in the examples above V/W can be pictured as a set or parallel lines or parallel planes, in the general case V/W we can think of as being the set of W and everything parallel to it that we can fit inside of V. The notation is also fairly suggestive, and fortunately the things it seems to suggest actually do make sense. Writing v + W makes it sound like we re adding a vector v to a plane W, and thinking about it that s what we re doing: we re taking v plus each vector in the plane W! And similarly, (v +W )+(v +W ) actually equals the set of all sums (v + w) + (v + w ) for v + w v + W and v + W v + W. So we really are just doing addition, just we ve expanded addition to let us add together sets (by adding together all of their elements). Likewise, if a 0 is a scalar then a (v + W ) is the set of all multiples a(v + w). (We do need to be a bit careful, though: 0 (v + W ) is defined to be the coset 0 + W = W, but the set of all products 0 (v + w) is just {0}). So now we have this abstract definition of a quotient vector space, and you may be wondering why we re making this definition, and what are some useful examples of it. One reason will be in our study of homomorphisms (in a couple weeks); but even with that in mind it can be hard to give interesting examples within the subject of linear algebra itself. However, there are many important examples that come up when we use linear algebra in other subjects, and especially when we study vector spaces with more structure on them. I ll give two examples where quotient spaces do naturally come up - these may be a bit challenging given where we are in the course, but they can provide a view of where we might go with these abstract definitions. Extended example 1: L 2 space. One example that comes up naturally is from real analysis. Let I = [0, 1] be the unit interval, and then let L 2 (I) F(I, R) be the set of functions f : [0, 1] R that are integrable, and such that 1 0 f(x) 2 dx <. (Strictly speaking the definition usually uses the requirement that f is Lebesgue integrable, which includes more functions than the definition of Riemann integrability you ve likely seen in calculus or analysis classes. But this isn t really important for what I m going to say here). So, L 2 (I) is a set of functions, and it s not too hard to check that it s a real vector space of the space F(I, R) of all functions I R. What we d like to do is put a norm on it, much like the Euclidean norm on R n. Recall that the Euclidean norm is given by x = x 1. x n x = x x2 n. Thus x x is a function taking a vector as an input and returning a nonnegative real number as an output, which satisfies the following properties: (Positive-definiteness): If x 0 then x 0. (Homogeneity): If x R n and a R, we have ax = a x. (Triangle inequality): If x, y R n then x + y x + y. In fact these three properties are taken as the abstract definition of a norm on a vector space. So we want to put a norm on L 2 (I). Since L 2 (I) is defined in terms of integrability of functions, and in particular by requiring that the integral f 2 dx is finite, it s natural to try to define a norm on it by using this integral; so for f L 2 (I) we define ( 1 1/2 f = f(x) dx)

3 The reason we take a square root is so that the homogeneity property is satisfied. It also turns out to not be too difficult to prove the triangle inequality f + g f + g (though it would be a bit out of the way to do it here). But what about positive-definiteness? That axiom actually fails - for instance a function like { 1 x = 1/2 h(x) = 0 x 1/2, which is zero except at a single point, is certainly integrable and not the zero function, but the integral of f 2 is zero. There s a few ways we could try to fix this. One thing we could do is replace L 2 (I) with the space of continuous functions C(I, R) - on this space our function is actually positive-definiteness. But this turns out not to be such a good idea, because when we work with these spaces in analysis we want to be able to take limits, and it s possible to have a limit of continuous functions that isn t continuous. Another possibility might be to just try to throw out obviously bad functions like the h(x) mentioned above. But there isn t really a reasonable way to do this - for instance neither functions h 1 (x) = { 1 x 1/2 0 x < 1/2 h 2 (x) = { 1 x > 1/2 0 x 1/2, seems particularly better or worse than the other, but their difference is h(x) so we d need to exclude one of them. We could also consider just working with the full set L 2 (I), and dropping the positive-definiteness condition. But this also isn t an ideal thing to do. For instance, one thing we d ultimately like to get out of this theory is being able to write Fourier series expansions f(x) = a 0 + a n sin(2πnx) + b n cos(2πnx), where the coefficients a i and b i come from certain integrals involving the function f. But when we re doing anything with integrals, the function h I wrote down above behaves exactly the same as the zero function, so we d find ourselves writing down the same expansion for both of them. And if we re interpreting things as actual equalities of functions, we certainly don t want to say that two different functions are equal to the same thing! So what s the way to fix this? It turns out that L 2 (I) itself isn t the thing we actually want to work with - we instead want the quotient space L 2 (I) = L 2 (I)/L 0, where L 0 is the collection of all functions that have norm zero: L 0 = {f : 1 0 f(x) 2 = 0}. In the Lebesgue integration theory this is equal to the set of functions which are almost everywhere equal to zero, and thus don t affect integrals at all. It s not too hard to check that this is a subspace of L 2 (I), so we can actually form the quotient we want. Then the norm function is well-defined on the quotient space L 2 (I) because if two functions f, g are in the same coset their difference doesn t affect integrals! So now we can talk about L 2 (I) as a normed vector space; in fact it s what s called a Hilbert space, and the theory of Hilbert spaces is well studied. What the theory tells us is then that for any function f, we do have a Fourier series decomposition f(x) = a 0 + a n sin(2πnx) + b n cos(2πnx), where the coefficients can be written explicitly as integrals involving f. But this is an equality that holds in the space L 2 (I) - so on the level of functions the equality is only up to adding a function in L 0. But this is all we could hope to ask for, because the coefficients being defined as integrals means that changing by something in L 0 can t affect them! (And it s also important to have this restriction because there are Fourier series expansions where the infinite series diverge at a few specific values of x). 3

5 But since (x + I) 2 = 2 + I (because x + I satisfies the polynomial p(x)), so we can reduce any power (x + I) n to something already on our list! So summarizing, we ve constructed a field F [x]/i with nine elements, in which F 3 sits inside in an obvious way (strictly speaking F 3 isn t a subset, but there s a natural embedding as the constant polynomials), and in which we also have a root of the polynomial x 2 2. We can call this field F 9 ; if we let α = x + I we can consider α to be an element that s the square root of 2 over F 3 and our field consists of the 9 elements 0, 1, 2, α, α + 1, α + 2, 2α, 2α + 1, 2α + 2 (and at this point we ll generally abuse notation and think of the subset {0, 1, 2} as being the field F 3 ). In contrast to the first example, in this case we don t ultimately care what our field was constructed as a quotient of! We just want to know there exists a field F 9 containing F 3 and containing an element α satisfying α 2 = 2 (and which necessarily consist exactly of the 9 elements listed). At this point we just want to think of F 9 as an abstract 9-element set with a certain addition and multiplication table; thinking about what s under the hood (i.e. that the elements are cosets in F [x]) just ends up complicating things. 5

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