A Mind for Madness

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Distributions

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We need to build up a lot of definitions now to properly state the Frobenius Theorem. The main definition will be a distribution on a manifold. Essentially, the theory of distributions is a way to generalize the notion of a vector field and the flow of a vector field.

A distribution is a choice of k-dimensional subspace D_p\subset T_pM at each point of the manifold. Note that this is just a subbundle of the tangent bundle, so we have a nice notion of smoothness. In particular, just as we could check a local frame for smoothness of a vector field (i.e. a 1-dimensional distribution), we can check for smoothness of a distribution by checking if each point has a neighborhood on which there are smooth vector fields X_1, \ldots , X_k such that X_1\big|_q, \ldots , X_k\big|_q forms as basis for D_q at each point of the open set.

The analogous thing for integral curves for vector fields will be what we call an integral manifold. If we think about the natural way to define this we would see that all we want is an immersed submanifold N\subset M such that T_pN=D_p \forall p\in N. Thus in the one dimensional case, the immersed submanifold is just a curve on the manifold.

Unfortunately, it is not the case that integral manifolds exist for all distributions. Our goal is to figure out when they exist. This leads us to our next two definitions. A distribution is integrable if every point of the manifold is in some integral manifold for the distribution. A distribution is called involutive if for any pair of local sections of the distribution, the Lie bracket is also a local section. Note that a local section is really just a vector field where the vectors are chosen from the distribution rather than the whole tangent bundle.

Every integrable distribution is involutive. If D\subset TM is involutive, then given any p\in M and local sections X, Y there is some integral manifold about p, say N. Since both X, Y\in T_pN, we have that [X, Y]_p\in T_pN=D_p, which is the definition of involutive.

This gives us an easy way to see that there are non-integrable distributions (recall, this is not going to happen for 1-distributions, i.e. vector fields, since every point has an integral curve). We don’t even need some weird manifold. Just take \mathbb{R}^3, and let the distribution be the span of two vector fields whose Lie bracket is not in the span. Thus something like \displaystyle D=span\{X=\frac{\partial}{\partial x}+y\frac{\partial}{\partial z}, Y=\frac{\partial}{\partial y}\} will work, since [X, Y]_0=-\frac{\partial}{\partial z}\notin D_0.

I think we need only one more definition to be in a place to move on. A distribution is completely integrable if there exists a flat chart for the distribution in a neighborhood of every point. By this I mean that I can find a coordinate chart such that \displaystyle D=span\{\frac{\partial}{\partial x^1}, \ldots , \frac{\partial}{\partial x^k}\}. This is obviously the strongest condition.

So our definitions at this point satisfy completely integrable distributions are integrable, and integrable distributions are involutive. The utterly remarkable thing that the Frobenius Theorem says, is that all of these implications reverse, and so all of the definitions are actually equivalent! We’ll get there later, though.

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Author: hilbertthm90

I write about math, philosophy, literature, music, science, computer science, gaming or whatever strikes my fancy that day.

One thought on “Distributions

  1. I first learned about this result “integrable equals involutive” from Serge Lang’s Differential and Riemannian Manifolds. However, I never realized that Frobenius’s theorem was just a generalization of the existence theorem for integral curves, until recently (because Spivak explains this, as you do). This is the problem I have with authors like Lang (in this book anyway) who take too thoroughly a “modern” viewpoint without connecting it back to classical intuition or forget about motivation.

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