Lecture 16

In this lecture we covered Nisan's pseudorandom generator for log space.

One thing I had to rush over at the end was the actual definition of the generator, along with its seed length!

Here is an official inductive definition of the generator: Let $h_1, ... h_{\lg n} : \{0,1\}^k \to \{0,1\}^k$ be functions. We define $G_t : \{0,1\}^k \to \{0,1\}^{2^t k}$ inductively by $G_0(x) = x$, $G_{t}(x) = (G_{t-1}(x), G_{t-1}(h_t(x)))$.

Finally, Nisan's generator $G$ maps $\langle x, h_1, ... h_{\lg n} \rangle$ into $G_{\lg n}(x)$. Here the $h_i$'s are the "descriptions" of the hash functions.

Recall that we are using the simple "$a \circ x + b$" pairwise independent hash family; the "description" of such a function is just the $2k$-bit string $\langle a, b\rangle$. Conveniently, choosing this description uniformly at random coincides with choosing $h \sim \mathcal{H}_k$ uniformly at random.

So finally, the seed length is $\ell = k + (\lg n)(2k)$. Since $k = O(\log S)$, this is indeed $O(S \log n)$ seed-length, as claimed. The number of output bits is in fact $nk = O(n \log S)$ (which is, as we noted, slightly more than we need).