Randomness-efficient Curve Samplers

TL;DR

This paper presents an explicit construction of low-degree curve samplers that are optimal in randomness complexity, leveraging advanced combinatorial and coding techniques.

Contribution

It introduces the first explicit construction of curve samplers with near-optimal randomness complexity and controlled degree, improving prior results.

Findings

Achieves optimal randomness complexity up to a constant factor.

Constructs low-degree curve samplers with degree polynomial in parameters.

Combines extractor machinery, limited independence, and list-recoverable codes.

Abstract

Curve samplers are sampling algorithms that proceed by viewing the domain as a vector space over a finite field, and randomly picking a low-degree curve in it as the sample. Curve samplers exhibit a nice property besides the sampling property: the restriction of low-degree polynomials over the domain to the sampled curve is still low-degree. This property is often used in combination with the sampling property and has found many applications, including PCP constructions, local decoding of codes, and algebraic PRG constructions. The randomness complexity of curve samplers is a crucial parameter for its applications. It is known that (non-explicit) curve samplers using $O(\log N+\log(1/\delta))$ random bits exist, where $N$ is the domain size and $\delta$ is the confidence error. The question of explicitly constructing randomness-efficient curve samplers was first raised in \cite{TU06} where they obtained curve samplers with near-optimal randomness complexity. We present an explicit construction of low-degree curve samplers with {\em optimal} randomness complexity (up to a constant factor), sampling curves of degree $\left(m\log_q(1/\delta)\right)^{O(1)}$ in $\mathbb{F}_q^m$. Our construction is a delicate combination of several components, including extractor machinery, limited independence, iterated sampling, and list-recoverable codes.