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21

There are two aspect that need to be mentioned. The first is the general idea of defining a PRG by having its output look different than uniform to small circuits. This idea goes back to Yao and is really the strongest possible definition you can ask for when aiming explicitly at pseudo-randomness for computationally-bounded observers. The second ...


19

$SL = L$. $RL$ stands for randomized logspace and $RL=L$ is a smaller version of the problem $RP=P$. A major stepping stone was the proof of Reingold in '04 ("Undirected S-T Connectivity in Logspace") that $SL = L$, where $S$ stands for "symmetric" and $SL$ is an intermediate class between $RL$ and $L$. The idea is that you can think of a randomized ...


16

There is basically only one interesting problem in BPP not known to be in P: Polynomial Identity Testing, given an algebraic circuit is the polynomial it generates identically zero. Impagliazzo and Kabanets show that PIT in P would imply some circuit lower bounds. So circuit lower bounds are the only reason (but a pretty good one) that we believe P = BPP.


16

Check Chapter 7 of Salil Vadhan's monograph. Corollary 7.64 is Impagliazzo and Wigderson's result.


15

1) What is meant by necessary is that one way to generate a $k$-wise independent distribution is to break the input in blocks of $k+1$ bits, and let the $(k+1)$th bit of each block be the parity of the other $k$ bits in the block. Obviously this distribution can be broken just by computing parity on $k$ bits. The result you claim follows from the fact that ...


13

Besides polynomial identity testing, one other very important problem known to be in BPP but not in P is approximating the permanent of a non-negative matrix or even the number of perfect matchings in a graph. There is a randomized poly-time algorithm to approximate these numbers within a (1+eps) factor, whereas the best deterministic algorithms achieve only ...


10

If $d$ is of the order of $n$ then you can write a constant-width branching program as a finite-state automaton, and logarithmic seed length is not known. But if $d$ is very small, say a constant, then you can do better and achieve logarithmic seed length -- I think, this is something I thought about years ago but never wrote down. The trick is to use ...


9

I assume you want an efficient algorithm, e.g., one whose running time is polynomial in $n$. (If you don't care about running time, then heuristically about $4n/\lg m$ samples should suffice to uniquely determine $a,b,m,p$, and you can use exhaustive search over all possible $2^{4n}$ parameter choices to find the correct one. Of course, the running time of ...


8

Polylog independence may not be the only way to fool $AC^{0}$ circuits. To illustrate this example, consider the class of linear polynomials. Any zero set of a linear polynomial is $(n-1)$-wise independent but of course this doesn't fool linear polynomials. Hence, $(n-1)$-wise independent distributions do not fool this class. This of course doesn't mean that ...


7

Yes, this notion has been studied. One interesting aspect is that the two notions of pseudorandomness known to be equivalent under the usual adversaries, "next bit predictability" and "indistinguishability", do not seem to be equivalent for deterministic adversaries. (If they were, we would have complexity class separations.) Here are three references; I'm ...


6

The meaning is a bit string $x$ which is distributed uniformly on $\{0,1\}^{|x|}$.


4

I am by no means an expert on this, but a key component of the definition of pseudorandomness (as opposed to attempts to define randomness) is that the goal of something "pseudorandom" is to fool a circuit. In other words, the motivation is to think of the pseudorandom string being supplied to the circuit instead of the truly random string. In that sense, ...


4

You can determine the period reasonably easily if you know the factorization of $M$ as $pq$, the factorization of $(p-1)\cdot (q-1)$ and the factorization of $\ell-1$ for any prime $\ell$ that divides $(p-1)\cdot (q-1)$. This requires two steps, first find the order of $x_0$ modulo $M$. Since this divides $(p-1)\cdot (q-1)$ whose factorization is known, ...


4

Not very practical, but you can sample from a the polytope of feasible flows using the well-known random walk techniques, see for example this classical paper by Kannan, Lovasz and Simonovits. These algorithms allow you to sample in polynomial time (in the dimension) from a distribution which is arbitrarily close to uniform in $L_1$ distance.


3

Given that you said it would be enough for the sequence to be unpredictable, here is a very simple solution. The $i$th value in the sequence is given by $$x_i = 2^{128} i + y_i$$ where $y_i$ is any pseudorandom sequence where you can efficiently find the $i$th value ($y_i$) and where each $y_i$ is 128 bits wide (i.e., $0 \le y_i < 2^{128}$). For ...


2

I think I can show the optimality of a scheme that I skteched in the question, the one that does rejection sampling but reuses the remaining entropy. Description of the scheme. Thinking as an algorithm, the scheme is the following. It has two parameters, an integer $n$, and a bound $m$, $0 \leq n < m$, with the invariant that $n$ was drawn uniformly at ...


2

Hopefully, I can expand just a little on Suresh's response. First, I don't think that the strictness of the inequality is needed in your $(*)$, and I am also not sure why $1/n$ is needed, and not $1/2n$ or something else. However, practically, I think 1/n is enough to get some interesting theoretical results. But then you almost certainly want to assert ...


2

There is now a jump function for SFMT (a fast Mersenne Twister implementation). This allows me to initialise 1000 MTs so that there is no cycle overlap. And SFMT should be faster than MTGP. Almost perfect for my purposes.


1

something close to what you are requesting seems to be proven in Thm 2.10 p6 of these lecture notes by O'Donnell, Lecture 16: Nisan’s PRG for small space but it does not cite the original ref for the proof. a simple statement of the theorem in terms of FSMs is not given in this ref but is translatable. (volunteers?) in the theorem $M^n$ is a transition ...


1

Regarding question A, it is possible to adapt the PRNG to yield uniformly distributed numbers using this. However, the range of this new uniform generator has size smaller than $K$, unless the PRNG is itself uniform. The number of possible outcomes of this new generator will depend on the min-entropy of the original generator. To get some intuition on why ...


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