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We consider an emph{approximate} version of the trace reconstruction problem, where the goal is to recover an unknown string $sin{0,1}^n$ 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 $O(n^3)$.
Trace reconstruction is the problem of learning an unknown string $x$ from independent traces of $x$, where traces are generated by independently deleting each bit of $x$ with some deletion probability $q$. In this paper, we initiate the study of Cir
Many applications like pointer analysis and incremental compilation require maintaining a topological ordering of the nodes of a directed acyclic graph (DAG) under dynamic updates. All known algorithms for this problem are either only analyzed for wo
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