Weizmann Logo
ECCC
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



REPORTS > AUTHORS > ALEX SAMORODNITSKY:
All reports by Author Alex Samorodnitsky:

TR07-123 | 21st November 2007
Shachar Lovett, Roy Meshulam, Alex Samorodnitsky

Inverse Conjecture for the Gowers norm is false

Revisions: 2


Let $p$ be a fixed prime number, and $N$ be a large integer.
The 'Inverse Conjecture for the Gowers norm' states that if the "$d$-th Gowers norm" of a function $f:\F_p^N \to \F_p$ is non-negligible, that is larger than a constant independent of $N$, then $f$ can be non-trivially ... more >>>


TR07-033 | 14th February 2007
Michael Navon, Alex Samorodnitsky

Linear programming bounds for codes via a covering argument

We recover the first linear programming bound of McEliece, Rodemich, Rumsey, and Welch for binary error-correcting codes and designs via a covering argument. It is possible to show, interpreting the following notions appropriately, that if a code has a large distance, then its dual has a small covering radius and, ... more >>>


TR06-054 | 16th April 2006
Alex Samorodnitsky

Low-degree tests at large distances

We define tests of boolean functions which
distinguish between linear (or quadratic) polynomials, and functions
which are very far, in an appropriate sense, from these
polynomials. The tests have optimal or nearly optimal trade-offs
between soundness and the number of queries.

In particular, we show that functions with small ... more >>>


TR05-163 | 19th December 2005
Dvir Falik, Alex Samorodnitsky

Edge-isoperimetric inequalities and influences

We give a combinatorial proof of the result of Kahn, Kalai, and Linial, which states that every balanced boolean function on the n-dimensional boolean cube has a variable with influence of at least Omega(log(n)/n).The methods of the proof are then used to recover additional isoperimetric results for the cube, with ... more >>>


TR05-116 | 12th October 2005
Alex Samorodnitsky, Luca Trevisan

Gowers Uniformity, Influence of Variables, and PCPs

Gowers introduced, for d\geq 1, the notion of dimension-d uniformity U^d(f)
of a function f: G -> \C, where G is a finite abelian group and \C are the
complex numbers. Roughly speaking, if a function has small Gowers uniformity
of dimension d, then it ``looks random'' on ... more >>>


TR05-084 | 31st July 2005
Mickey Brautbar, Alex Samorodnitsky

Approximating the entropy of large alphabets

We consider the problem of approximating the entropy of a discrete distribution P on a domain of size q, given access to n independent samples from the distribution. It is known that n > q is necessary, in general, for a good additive estimate of the entropy. A problem of ... more >>>


TR05-065 | 26th June 2005
Alexander Barvinok, Alex Samorodnitsky

Random Weighting, Asymptotic Counting, and Inverse Isoperimetry

For a family X of k-subsets of the set 1...n, let |X| be the cardinality of X and let Gamma(X, mu) be the expected maximum weight of a subset from X when the weights of 1...n are chosen independently at random from a symmetric probability distribution mu on R. We ... more >>>


TR01-063 | 5th August 2001
Michal Parnas, Dana Ron, Alex Samorodnitsky

Proclaiming Dictators and Juntas or Testing Boolean Formulae

Revisions: 1

We consider the problem of determining whether a given
function f : {0,1}^n -> {0,1} belongs to a certain class
of Boolean functions F or whether it is far from the class.
More precisely, given query access to the function f and given
a distance parameter epsilon, we would ... more >>>


TR99-017 | 4th June 1999
Yevgeniy Dodis, Oded Goldreich, Eric Lehman, Sofya Raskhodnikova, Dana Ron, Alex Samorodnitsky

Improved Testing Algorithms for Monotonicity.

Revisions: 1


We present improved algorithms for testing monotonicity of functions.
Namely, given the ability to query an unknown function $f$, where
$\Sigma$ and $\Xi$ are finite ordered sets, the test always accepts a
monotone $f$, and rejects $f$ with high probability if it is $\e$-far
from being monotone (i.e., every ... more >>>




ISSN 1433-8092 | Imprint