Version History: Preliminary version posted as ECCC TR21-108. Invited to Algorithmica Special Issue on COCOON '21.

Abstract: Forthcoming

Version History: Originally published as "Monotone branching programs: pseudorandomness and circuit complexity". 

Previously published as "Pseudorandom generators for read-once monotone branching programs", Electronic Colloquium on Computational Complexity (ECCC) Vol. 2021, Issue 18. Linked here: https://eccc.weizmann.ac.il/report/2021/018/

Abstract: Motivated by the derandomization of space-bounded computation, there has been a long line of work on constructing pseudorandom generators (PRGs) against various forms of read-once branching programs (ROBPs), with a goal of improving the \(O(\log^2n)\) seed length of Nisan’s classic construction to the optimal \(O(\log n)\).

In this work, we construct an explicit PRG with seed length \(\tilde{O}(\log n)\) for constant-width ROBPs that are monotone, meaning that the states at each time step can be ordered so that edges with the same labels never cross each other. Equivalently, for each fixed input, the transition functions are a monotone function of the state. This result is complementary to a line of work that gave PRGs with seed length \(O(\log n)\) for (ordered) permutation ROBPs of constant width, since the monotonicity constraint can be seen as the “opposite” of the permutation constraint.

Our PRG also works for monotone ROBPs that can read the input bits in any order, which are strictly more powerful than read-once \(\mathsf{AC^0}\). Our PRG achieves better parameters (in terms of the dependence on the depth of the circuit) than the best previous pseudorandom generator for read-once \(\mathsf{AC^0}\), due to Doron, Hatami, and Hoza.

Our pseudorandom generator construction follows Ajtai and Wigderson’s approach of iterated pseudorandom restrictions. We give a randomness-efficient width-reduction process which proves that the branching program simplifies to an \(O(\log n)\)-junta after only \(O(\log \log n)\) independent applications of the Forbes-Kelley pseudorandom restrictions.

Version History: Full Version posted as ECC TR21-019. Invited to Theory of Computing Special Issue on CCC '21.

Abstract:

A recent paper of Braverman, Cohen, and Garg (STOC 2018) introduced the concept of a pseudorandom pseudodistribution generator (PRPG), which amounts to a pseudorandom generator (PRG) whose outputs are accompanied with real coefficients that scale the acceptance probabilities of any potential distinguisher. They gave an explicit construction of PRPGs for ordered branching programs whose seed length has a better dependence on the error parameter  than the classic PRG construction of Nisan (STOC 1990 and Combinatorica 1992).

In this work, we give an explicit construction of PRPGs that achieve parameters that are impossible to achieve by a PRG. In particular, we construct a PRPG for ordered permutation branching programs of unbounded width with a single accept state that has seed length \(\tilde{O}(\log^{3/2}n)\)  for error parameter \( \epsilon = 1/ \mathrm{poly}(n)\), where \(n\) is the input length. In contrast, recent work of Hoza et al. (ITCS 2021) shows that any PRG for this model requires seed length \( \Omega(\log^2n)\) to achieve error \( \epsilon = 1/ \mathrm{poly}(n)\).

As a corollary, we obtain explicit PRPGs with seed length \(\tilde{O}(\log^{3/2}n)\)  and error \( \epsilon = 1/ \mathrm{poly}(n)\) for ordered permutation branching programs of width \(w = \mathrm{poly}(n) \)with an arbitrary number of accept states. Previously, seed length \(o(\log^2n)\) was only known when both the width and the reciprocal of the error are subpolynomial, i.e. \(w= n^{o(1)} \) and \(\epsilon = 1/n^{o(1)}\)(Braverman, Rao, Raz, Yehudayoff, FOCS 2010 and SICOMP 2014).

The starting point for our results are the recent space-efficient algorithms for estimating random-walk probabilities in directed graphs by Ahmadenijad, Kelner, Murtagh, Peebles, Sidford, and Vadhan (FOCS 2020), which are based on spectral graph theory and space-efficient Laplacian solvers. We interpret these algorithms as giving PRPGs with large seed length, which we then derandomize to obtain our results. We also note that this approach gives a simpler proof of the original result of Braverman, Cohen, and Garg, as independently discovered by Cohen, Doron, Renard, Sberlo, and Ta-Shma (personal communication, January 2021).

Version History: 

Preliminary version posted on ECCC TR20-138 (PDF version attached as ECCC 2020).

Talks: The ITCS talk for this paper, presented by Edward Pyne, is currently available on YouTube; click the embedded link to view. 

We prove that the Impagliazzo-Nisan-Wigderson [Impagliazzo et al., 1994] pseudorandom generator (PRG) fools ordered (read-once) permutation branching programs of unbounded width with a seed length of \(\tilde{O} (\log d + \log n ⋅ \log(1/\epsilon))\), assuming the program has only one accepting vertex in the final layer. Here, \(n\) is the length of the program, \(d\) is the degree (equivalently, the alphabet size), and \(\epsilon\) is the error of the PRG. In contrast, we show that a randomly chosen generator requires seed length \(\Omega (n \log d)\) to fool such unbounded-width programs. Thus, this is an unusual case where an explicit construction is "better than random."

Except when the program’s width \(w\) is very small, this is an improvement over prior work. For example, when \(w = \mathrm{poly} (n)\) and \(d = 2\), the best prior PRG for permutation branching programs was simply Nisan’s PRG [Nisan, 1992], which fools general ordered branching programs with seed length \(O (\log (wn/\epsilon) \log n)\). We prove a seed length lower bound of \(\tilde{\Omega} (\log d + \log n ⋅ \log(1/\epsilon)) \)for fooling these unbounded-width programs, showing that our seed length is near-optimal. In fact, when\( \epsilon ≤ 1/\log n\), our seed length is within a constant factor of optimal. Our analysis of the INW generator uses the connection between the PRG and the derandomized square of Rozenman and Vadhan [Rozenman and Vadhan, 2005] and the recent analysis of the latter in terms of unit-circle approximation by Ahmadinejad et al. [Ahmadinejad et al., 2020].

We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph \(G\), a positive integer \(r\), and a set \(S\) of vertices, approximates the conductance of \(S\) in the \(r\)-step random walk on \(G\) to within a factor of \(1+ϵ\), where \(ϵ > 0\) is an arbitrarily small constant. More generally, our algorithm computes an \(ϵ\)-spectral approximation to the normalized Laplacian of the \(r\)-step walk. Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is space-efficient) and randomized (while ours is deterministic) for the case of even \(r\) (while ours works for all \(r\)). Along the way, we provide some new results that generalize technical machinery and yield improvements over previous work. First, we obtain a nearly linear-time randomized algorithm for computing a spectral approximation to the normalized Laplacian for odd \(r\). Second, we define and analyze a generalization of the derandomized square for irregular graphs and for sparsifying the product of two distinct graphs. As part of this generalization, we also give a strongly explicit construction of expander graphs of every size.