Pseudorandom Generators for Sliding-Window Algorithms
Augusto Modanese

TL;DR
This paper develops pseudorandom generators tailored for low-space sliding-window algorithms, enabling efficient derandomization and decision procedures for probabilistic cellular automata within sublinear time and space constraints.
Contribution
It introduces new PRG constructions for sliding-window branching programs and applies these to simulate probabilistic cellular automata in sublinear space.
Findings
PRGs with near-optimal seed length for SWBPs
Deciding PACA languages using sublinear space in specific time regimes
Extension of derandomization techniques to sliding-window models
Abstract
A sliding-window algorithm of window size is an algorithm whose current operation depends solely on the last symbols read. We construct pseudorandom generators (PRGs) for low-space randomized sliding-window algorithms that have access to a binary randomness source. More specifically, we lift these algorithms to the non-uniform setting of branching programs and study them as a subclass thereof that we call sliding-window branching programs (SWBPs), accordingly. For general SWBPs, given a base PRG with seed length that -fools width-, length- (general) branching programs, we give two PRG constructions for fooling any same-width SWBP of length and window size (where we assume ). The first uses an additional random bits, whereas the…
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Taxonomy
TopicsCellular Automata and Applications · Advanced Data Storage Technologies · DNA and Biological Computing
