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2011 26th Annual IEEE Conference on Computational Complexity
Noisy Interpolation of Sparse Polynomials, and Applications
San Jose, California USA
June 08-June 11
ISBN: 978-0-7695-4411-3
Let f in F_q[x] be a polynomial of degree d _ q=2: It is well-known that f can be uniquely recovered from its values at some 2d points even after some small fraction of the values are corrupted. In this paper we establish a similar result for sparse polynomials. We show that a k-sparse polynomial f 2 Fq[x] of degree d _ q=2 can be recovered from its values at O(k) randomly chosen points, even if a small fraction of the values of f are adversarially corrupted. Our proof relies on an iterative technique for analyzing the rank of a random minor of a matrix.We use the same technique to establish a collection of other results. Specifically, _ We show that restricting any linear [n; k; _n]q code to a randomly chosen set of O(k) coordinates with high probability yields an asymptotically good code. _ We improve the state of the art in locally decodable codes, showing that similarly to Reed Muller codes matching vector codes require only a constant increase in query complexity in order to tolerate a constant fraction of errors. This result yields a moderate reduction in the query complexity of the currently best known codes. _ We improve the state of the art in constructions of explicit rigid matrices. For any prime power q and integers n and d we construct an explicit matrix M with exp(d) _ n rows and n columns such that the rank of M stays above n=2 even if every row of M is arbitrarily altered in up to d coordinates. Earlier, such constructions were available only for q = O(1) or q = (n):
Index Terms:
Sparse polynomials, interpolation, locally decodable codes, matrix rigidity
Citation:
Shubhangi Saraf, Sergey Yekhanin, "Noisy Interpolation of Sparse Polynomials, and Applications," ccc, pp.86-92, 2011 26th Annual IEEE Conference on Computational Complexity, 2011
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