Name
Abstract | ||
|---|---|---|
In the beautifully simple-to-state problem of trace reconstruction, the goal is to reconstruct an unknown binary string x given random “traces” of x where each trace is generated by deleting each coordinate of x independently with probability p <; 1. The problem is well studied both when the unknown string is arbitrary and when it is chosen uniformly at random. For both settings, there is still an exponential gap between upper and lower sample complexity bounds and our understanding of the problem is still surprisingly limited. In this paper, we consider natural parameterizations and generalizations of this problem in an effort to attain a deeper and more comprehensive understanding. Perhaps our most surprising results are: 1) We prove that exp(O(n
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup>
√{logn})) traces suffice for reconstructing arbitrary matrices. In the matrix version of the problem, each row and column of an unknown √n×√n matrix is deleted independently with probability p. Our results contrasts with the best known results for sequence reconstruction where the best known upper bound is exp(O(n
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup>
)). 2) An optimal result for random matrix reconstruction: we show that Θ(logn) traces are necessary and sufficient. This is in contrast to the problem for random sequences where there is a super-logarithmic lower bound and the best known upper bound is exp(O(log
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup>
n)). 3) We show that exp(O(k
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/3</sup>
log
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2/3</sup>
n)) traces suffice to reconstruct k-sparse strings, providing an improvement over the best known sequence reconstruction results when k = o(n/log
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>
n). 4) We show that poly(n) traces suffice if x is k-sparse and we additionally have a “separation” promise, specifically that the indices of 1's in x all differ by Ω(k logn). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIT.2021.3066010 | IEEE Transactions on Information Theory |
Keywords | Field | DocType |
Deletion Channel,Mixture Models,Statistical Reconstruction | Binary logarithm,Discrete mathematics,Combinatorics,Parameterized complexity,Exponential function,Generalization,Matrix (mathematics),Upper and lower bounds,Omega,Mathematics,Random matrix | Journal |
Volume | Issue | ISSN |
67 | 6 | 0018-9448 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshay Krishnamurthy | 1 | 337 | 32.04 |
| Arya Mazumdar | 2 | 307 | 41.81 |
| Andrew McGregor | 3 | 103 | 8.08 |
| Soumyabrata Pal | 4 | 2 | 3.76 |