I'm reading an article by Ricardo Mañé, Hausdorff dimension is diffeomorphism. I'm having a technical problem. Sorry for my ignorance, but I would like a reference which explains how to equip the Grassmann manifolds with a metric.

I found it surprisingly difficult to find a reference for this when I was studying Mane's papers on multiplicative ergodic theorems. My hypothesis was that people working with the Grassmannian in other areas are happy with the fact that the Grassmannian is metrisable for abstract topological reasons, and don't actually care very much about a precise metric, but I might be wrong about this... in my answer I'm going to assume that we're considering a finitedimensional space equipped with an inner product structure. If you are interested in precise metrics on the Grassmannian, the most popular definition of which I am aware is this one: $$d(V,W):=\max\left\{\sup_{w \in W, \w\=1}\inf \{\vw\ \colon v \in V \},\sup_{v \in V, \v\=1}\inf \{\vw\ \colon w \in W\}\right\}$$ This is I think not quite the same as the one suggested by Ryan Budney, but produces the same topology. This one seems to be the most popular definition for people working in multiplicative ergodic theory (it is in Barreira and Pesin's book, for example). There are some equivalent ways of describing this metric which seem to be less wellknown. If we know a priori that $V$ and $W$ have the same dimension, then the maximum in the expression above is always attained by both expressions simultaneously! Hence if we fix a dimension $r$, then the expression $$d(V,W):=\sup_{v \in V,\v\=1}\inf\left\{\vw\\colon w \in W\right\}$$ is actually a metric for the component of the Grassmannian which consists of all $r$dimensional subspaces. This does not seem to be very wellknown; I actually discovered this by reading Kato's book on perturbation theory, which isn't exactly the first place I'd go to to find out about Grassmannian manifolds... Another way to put a metric on the Grassmannian is as follows. We can identify a subspace $U$ with the unique linear operator of orthogonal projection onto that subspace, and take the metric given by setting the distance between two subspaces to be the operator norm distance between the orthogonal projection operators corresponding to those subspaces. I personally like this approach a great deal, because I think it makes it very obvious that the Grassmannian is compact (well, obvious if you're a functional analyst!). This metric is also, rather pleasantly I think, exactly identical to the first metric I defined above. You can find a proof that the two things are the same in the book on Hilbert spaces by Akhiezer and Glazman. There's a short discussion on this topic in my paper "A rapidlyconverging lower bound for the joint spectral radius via multiplicative ergodic theory", which is basically the result of a gentle argument between myself and the referee over how the metric on the Grassmannian should be defined! 


The $k$dimensional Grassmannian over a vector space $V$ naturally embeds into the projectivization of the $k$th exterior power of $V$. If you have a Euclidean structure on $V$, then it extends to its exterior products as well. Then a metric on the projectivization is provided, say, by the angle between the corresponding directions, or by sine of that angle. 


My "answer" is just a (still another) reformulation of Ian's. Given two subspaces $V$, $W \subset \mathbb{R}^n$ with the same dimension, define their distance as: $$ d(V,W):= \inf_J \ J  i_V\ $$ where $i_V : V \to \mathbb{R}^n$ denotes the inclusion, $\\mathord{\cdot}\$ is the Euclidian operator norm, and $J$ runs over the linear maps $V \to \mathbb{R}^n$ whose such that $J(V) \subset W$. It's not too difficult to prove that the the infimum is attained at map $J=P_WV$ (i.e. the orthogonal projection onto $W$ restricted to $V$). Trivially, $$ \P_WV  i_V\ = \sup_{v\in V, \v\=1} \inf_{w\in W} \wv\, $$ so my formula is equivalent to Ian's. As an easy consequence, we have the following interesting fact:
Proof: $$ d(TV,TW) = \inf_{K(TV)\subset TW} \Ki_{TV}\=\inf_{J(V)\subset W}\big\T(Ji_V)T^{1}_{TV}\big\ \le \T\ \, \T^{1}\ \, d(V,W). $$ (The proofs of the "fact" using other characterizations of $d$ become messier, I think.) 

