82 research outputs found
Subspace Evasive Sets
In this work we describe an explicit, simple, construction of large subsets
of F^n, where F is a finite field, that have small intersection with every
k-dimensional affine subspace. Interest in the explicit construction of such
sets, termed subspace-evasive sets, started in the work of Pudlak and Rodl
(2004) who showed how such constructions over the binary field can be used to
construct explicit Ramsey graphs. More recently, Guruswami (2011) showed that,
over large finite fields (of size polynomial in n), subspace evasive sets can
be used to obtain explicit list-decodable codes with optimal rate and constant
list-size. In this work we construct subspace evasive sets over large fields
and use them to reduce the list size of folded Reed-Solomon codes form poly(n)
to a constant.Comment: 16 page
On the size of Kakeya sets in finite fields
A Kakeya set is a subset of F^n, where F is a finite field of q elements,
that contains a line in every direction. In this paper we show that the size of
every Kakeya set is at least C_n * q^n, where C_n depends only on n. This
improves the previously best lower bound for general n of ~q^{4n/7}.Comment: Improved bound and added reference
Improved rank bounds for design matrices and a new proof of Kelly's theorem
We study the rank of complex sparse matrices in which the supports of
different columns have small intersections. The rank of these matrices, called
design matrices, was the focus of a recent work by Barak et. al. (BDWY11) in
which they were used to answer questions regarding point configurations. In
this work we derive near-optimal rank bounds for these matrices and use them to
obtain asymptotically tight bounds in many of the geometric applications. As a
consequence of our improved analysis, we also obtain a new, linear algebraic,
proof of Kelly's theorem, which is the complex analog of the Sylvester-Gallai
theorem
Outlaw distributions and locally decodable codes
Locally decodable codes (LDCs) are error correcting codes that allow for
decoding of a single message bit using a small number of queries to a corrupted
encoding. Despite decades of study, the optimal trade-off between query
complexity and codeword length is far from understood. In this work, we give a
new characterization of LDCs using distributions over Boolean functions whose
expectation is hard to approximate (in~~norm) with a small number of
samples. We coin the term `outlaw distributions' for such distributions since
they `defy' the Law of Large Numbers. We show that the existence of outlaw
distributions over sufficiently `smooth' functions implies the existence of
constant query LDCs and vice versa. We give several candidates for outlaw
distributions over smooth functions coming from finite field incidence
geometry, additive combinatorics and from hypergraph (non)expanders.
We also prove a useful lemma showing that (smooth) LDCs which are only
required to work on average over a random message and a random message index
can be turned into true LDCs at the cost of only constant factors in the
parameters.Comment: A preliminary version of this paper appeared in the proceedings of
ITCS 201
Affine extractors over large fields with exponential error
We describe a construction of explicit affine extractors over large finite
fields with exponentially small error and linear output length. Our
construction relies on a deep theorem of Deligne giving tight estimates for
exponential sums over smooth varieties in high dimensions.Comment: To appear in Comput. Comple
Variety Evasive Sets
We give an explicit construction of a large subset of F^n, where F is a
finite field, that has small intersection with any affine variety of fixed
dimension and bounded degree. Our construction generalizes a recent result of
Dvir and Lovett (STOC 2012) who considered varieties of degree one (affine
subspaces).Comment: 13 page
Extensions to the Method of Multiplicities, with applications to Kakeya Sets and Mergers
We extend the "method of multiplicities" to get the following results, of
interest in combinatorics and randomness extraction. (A) We show that every
Kakeya set (a set of points that contains a line in every direction) in
\F_q^n must be of size at least . This bound is tight to within a factor for every as , compared to previous bounds
that were off by exponential factors in . (B) We give improved randomness
extractors and "randomness mergers". Mergers are seeded functions that take as
input (possibly correlated) random variables in and a
short random seed and output a single random variable in that is
statistically close to having entropy when one of the
input variables is distributed uniformly. The seed we require is only
-bits long, which significantly improves upon
previous construction of mergers. (C) Using our new mergers, we show how to
construct randomness extractors that use logarithmic length seeds while
extracting fraction of the min-entropy of the source.
The "method of multiplicities", as used in prior work, analyzed subsets of
vector spaces over finite fields by constructing somewhat low degree
interpolating polynomials that vanish on every point in the subset {\em with
high multiplicity}. The typical use of this method involved showing that the
interpolating polynomial also vanished on some points outside the subset, and
then used simple bounds on the number of zeroes to complete the analysis. Our
augmentation to this technique is that we prove, under appropriate conditions,
that the interpolating polynomial vanishes {\em with high multiplicity} outside
the set. This novelty leads to significantly tighter analyses.Comment: 26 pages, now includes extractors with sublinear entropy los
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