Abstract
A recent paper of Braverman, Cohen, and Garg (STOC 2018) introduced the concept of a weighted pseudorandom generator (WPRG), 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 WPRGs for ordered branching programs whose seed length has a better dependence on the error parameter ε than the classic PRG construction of Nisan (STOC 1990 and Combinatorica 1992).
In this work, we give an explicit construction of WPRGs that achieve parameters that are impossible to achieve by a PRG. In particular, we construct a WPRG for ordered permutation branching programs of unbounded width with a single accept state that has seed length Õ(log^{3/2} n) for error parameter ε = 1/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 Ω(log² n) to achieve error ε = 1/poly(n).
As a corollary, we obtain explicit WPRGs with seed length Õ(log^{3/2} n) and error ε = 1/poly(n) for ordered permutation branching programs of width w = poly(n) with an arbitrary number of accept states. Previously, seed length o(log² n) was only known when both the width and the reciprocal of the error are subpolynomial, i.e. w = n^{o(1)} and ε = 1/n^{o(1)} (Braverman, Rao, Raz, Yehudayoff, FOCS 2010 and SICOMP 2014).
The starting point for our results are the recent spaceefficient algorithms for estimating randomwalk probabilities in directed graphs by Ahmadenijad, Kelner, Murtagh, Peebles, Sidford, and Vadhan (FOCS 2020), which are based on spectral graph theory and spaceefficient Laplacian solvers. We interpret these algorithms as giving WPRGs 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 TaShma (these proceedings).
BibTeX  Entry
@InProceedings{pyne_et_al:LIPIcs.CCC.2021.33,
author = {Pyne, Edward and Vadhan, Salil},
title = {{Pseudodistributions That Beat All Pseudorandom Generators (Extended Abstract)}},
booktitle = {36th Computational Complexity Conference (CCC 2021)},
pages = {33:133:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959771931},
ISSN = {18688969},
year = {2021},
volume = {200},
editor = {Kabanets, Valentine},
publisher = {Schloss Dagstuhl  LeibnizZentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2021/14307},
URN = {urn:nbn:de:0030drops143070},
doi = {10.4230/LIPIcs.CCC.2021.33},
annote = {Keywords: pseudorandomness, spacebounded computation, spectral graph theory}
}
Keywords: 

pseudorandomness, spacebounded computation, spectral graph theory 
Collection: 

36th Computational Complexity Conference (CCC 2021) 
Issue Date: 

2021 
Date of publication: 

08.07.2021 