Inapproximability for metric embeddings into R^d

We consider the problem of computing the smallest possible distortion for embedding of a given n-point metric space into R^d, where d is fixed (and small). For d=1, it was known that approximating the minimum distortion with a factor better than roughly n^1/12 is NP-hard. From this result we derive inapproximability with factor roughly n^1/(22d-10) for every fixed d\ge 2, by a conceptually very simple reduction. However, the proof of correctness involves a nontrivial result in geometric topology (whose current proof is based on ideas due to Jussi Vaisala). For d\ge 3, we obtain a stronger inapproximability result by a different reduction: assuming P \ne NP, no polynomial-time algorithm can distinguish between spaces embeddable in R^d with constant distortion from spaces requiring distortion at least n^c/d, for a constant c>0. The exponent c/d has the correct order of magnitude, since every n-point metric space can be embedded in R^d with distortion O(n^{{2/d}\log{3/2}n)} and such an embedding can be constructed in polynomial time by random projection. For d=2, we give an example of a metric space that requires a large distortion for embedding in R^2, while all not too large subspaces of it embed almost isometrically.