Pith. sign in

REVIEW 1 cited by

Approximate Trace Reconstruction via Median String (in Average-Case)

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2107.09497 v1 pith:327OB36Y submitted 2021-07-20 cs.DS

classification cs.DS
keywords approximatemediantimealgorithmepsilonnear-linearstringtrace
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We consider an \emph{approximate} version of the trace reconstruction problem, where the goal is to recover an unknown string $s\in\{0,1\}^n$ from $m$ traces (each trace is generated independently by passing $s$ through a probabilistic insertion-deletion channel with rate $p$). We present a deterministic near-linear time algorithm for the average-case model, where $s$ is random, that uses only \emph{three} traces. It runs in near-linear time $\tilde O(n)$ and with high probability reports a string within edit distance $O(\epsilon p n)$ from $s$ for $\epsilon=\tilde O(p)$, which significantly improves over the straightforward bound of $O(pn)$. Technically, our algorithm computes a $(1+\epsilon)$-approximate median of the three input traces. To prove its correctness, our probabilistic analysis shows that an approximate median is indeed close to the unknown $s$. To achieve a near-linear time bound, we have to bypass the well-known dynamic programming algorithm that computes an optimal median in time $O(n^3)$.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Generalized Trace Reconstruction Problem: Recovering a String of Probabilities

    cs.DS 2024-12 conditional novelty 8.0 of 10

    Worst-case probability strings require e^{Ω(√n)} traces to distinguish under deletions, while random probability strings are recoverable to ℓ1 error ε with poly(n,1/ε) traces.

Pith tools