A Structural Theorem for Local Algorithms with Applications to Coding, Testing, and Privacy

Oded's comments

I got confused by the first application; that is, the application to relaxed Locally Decodable Codes (RLDCs). The point is that the lower bound of Gur and Lachish applied to non-adaptive algorithms, whereas the straightforward transformation from adaptive to non-adaptive algorithms incures an exponential increase in the (Boolean) query complexity.

Related posts: lower bound for non-adaptive RLDC and best known RLDC.

The original abstract

We prove a general structural theorem for a wide family of local algorithms, which includes property testers, local decoders, and PCPs of proximity. Namely, we show that the structure of every algorithm that makes $q$ adaptive queries and satisfies a natural robustness condition admits a sample-based algorithm with $n^{1- 1/O(q^2 \log^2 q)}$ sample complexity, following the definition of Goldreich and Ron (TOCT 2016). We prove that this transformation is nearly optimal. Our theorem also admits a scheme for constructing privacy-preserving local algorithms.

Using the unified view that our structural theorem provides, we obtain results regarding various types of local algorithms, including the following.

• We strengthen the state-of-the-art lower bound for relaxed locally decodable codes, obtaining an exponential improvement on the dependency in query complexity; this resolves an open problem raised by Gur and Lachish (SODA 2020).
• We show that any (constant-query) testable property admits a sample-based tester with sublinear sample complexity; this resolves a problem left open in a work of Fischer, Lachish, and Vasudev (FOCS 2015) by extending their main result to adaptive testers.
• We prove that the known separation between proofs of proximity and testers is essentially maximal; this resolves a problem left open by Gur and Rothblum (ECCC 2013, Computational Complexity 2018) regarding sublinear-time delegation of computation.

Available from ECCC TR20-154.