2,094 research outputs found
Randomized Algorithms over Finite Fields for the Exact Parity Base Problem
AbstractWe present three randomized pseudo-polynomial algorithms for the problem of finding a base of specified value in a weighted represented matroid subject to parity conditions. These algorithms, the first two being an improved version of those presented by P. M. Camerini et al. (1992, J. Algorithms13, 258–273) use fast arithmetic working over a finite field chosen at random among a set of appropriate fields. We show that the choice of a best algorithm among those presented depends on a conjecture related to the best value of the so-called Linnik constant concerning the distribution of prime numbers in arithmetic progressions. This conjecture, which we call the C-conjecture, is a strengthened version of a conjecture formulated in 1934 by S. Chowla. If the C-conjecture is true, the choice of a best algorithm is simple, since the last algorithm exhibits the best performance, either when the performance is measured in arithmetic operations, or when it is measured in bit operations and mild assumptions hold. If the C-conjecture is false we are still able to identify a best algorithm, but in this case the choice is between the first two algorithms and depends on the asymptotic growth of m with respect to those of U and n, where 2n, 2m, U are the rank, the number of elements, and the maximum weight assigned to the elements of the matroid, respectively
Computing in Additive Networks with Bounded-Information Codes
This paper studies the theory of the additive wireless network model, in
which the received signal is abstracted as an addition of the transmitted
signals. Our central observation is that the crucial challenge for computing in
this model is not high contention, as assumed previously, but rather
guaranteeing a bounded amount of \emph{information} in each neighborhood per
round, a property that we show is achievable using a new random coding
technique.
Technically, we provide efficient algorithms for fundamental distributed
tasks in additive networks, such as solving various symmetry breaking problems,
approximating network parameters, and solving an \emph{asymmetry revealing}
problem such as computing a maximal input.
The key method used is a novel random coding technique that allows a node to
successfully decode the received information, as long as it does not contain
too many distinct values. We then design our algorithms to produce a limited
amount of information in each neighborhood in order to leverage our enriched
toolbox for computing in additive networks
MCMC Learning
The theory of learning under the uniform distribution is rich and deep, with
connections to cryptography, computational complexity, and the analysis of
boolean functions to name a few areas. This theory however is very limited due
to the fact that the uniform distribution and the corresponding Fourier basis
are rarely encountered as a statistical model.
A family of distributions that vastly generalizes the uniform distribution on
the Boolean cube is that of distributions represented by Markov Random Fields
(MRF). Markov Random Fields are one of the main tools for modeling high
dimensional data in many areas of statistics and machine learning.
In this paper we initiate the investigation of extending central ideas,
methods and algorithms from the theory of learning under the uniform
distribution to the setup of learning concepts given examples from MRF
distributions. In particular, our results establish a novel connection between
properties of MCMC sampling of MRFs and learning under the MRF distribution.Comment: 28 pages, 1 figur
Modern Coding Theory: The Statistical Mechanics and Computer Science Point of View
These are the notes for a set of lectures delivered by the two authors at the
Les Houches Summer School on `Complex Systems' in July 2006. They provide an
introduction to the basic concepts in modern (probabilistic) coding theory,
highlighting connections with statistical mechanics. We also stress common
concepts with other disciplines dealing with similar problems that can be
generically referred to as `large graphical models'.
While most of the lectures are devoted to the classical channel coding
problem over simple memoryless channels, we present a discussion of more
complex channel models. We conclude with an overview of the main open
challenges in the field.Comment: Lectures at Les Houches Summer School on `Complex Systems', July
2006, 44 pages, 25 ps figure
Hardness of the (Approximate) Shortest Vector Problem: A Simple Proof via Reed-Solomon Codes
We give a
simple proof that the (approximate, decisional) Shortest Vector Problem is
\NP-hard under a randomized reduction. Specifically, we show that for any and any constant , the -approximate problem
in the norm (-\GapSVP_p) is not in unless \NP
\subseteq \mathsf{RP}. Our proof follows an approach pioneered by Ajtai (STOC
1998), and strengthened by Micciancio (FOCS 1998 and SICOMP 2000), for showing
hardness of -\GapSVP_p using locally dense lattices. We construct
such lattices simply by applying "Construction A" to Reed-Solomon codes with
suitable parameters, and prove their local density via an elementary argument
originally used in the context of Craig lattices.
As in all known \NP-hardness results for \GapSVP_p with , our
reduction uses randomness. Indeed, it is a notorious open problem to prove
\NP-hardness via a deterministic reduction. To this end, we additionally
discuss potential directions and associated challenges for derandomizing our
reduction. In particular, we show that a close deterministic analogue of our
local density construction would improve on the state-of-the-art explicit
Reed-Solomon list-decoding lower bounds of Guruswami and Rudra (STOC 2005 and
IEEE Trans. Inf. Theory 2006).
As a related contribution of independent interest, we also give a
polynomial-time algorithm for decoding -dimensional "Construction A
Reed-Solomon lattices" (with different parameters than those used in our
hardness proof) to a distance within an factor of
Minkowski's bound. This asymptotically matches the best known distance for
decoding near Minkowski's bound, due to Mook and Peikert (IEEE Trans. Inf.
Theory 2022), whose work we build on with a somewhat simpler construction and
analysis
Approximate F_2-Sketching of Valuation Functions
We study the problem of constructing a linear sketch of minimum dimension that allows approximation of a given real-valued function f : F_2^n - > R with small expected squared error. We develop a general theory of linear sketching for such functions through which we analyze their dimension for most commonly studied types of valuation functions: additive, budget-additive, coverage, alpha-Lipschitz submodular and matroid rank functions. This gives a characterization of how many bits of information have to be stored about the input x so that one can compute f under additive updates to its coordinates.
Our results are tight in most cases and we also give extensions to the distributional version of the problem where the input x in F_2^n is generated uniformly at random. Using known connections with dynamic streaming algorithms, both upper and lower bounds on dimension obtained in our work extend to the space complexity of algorithms evaluating f(x) under long sequences of additive updates to the input x presented as a stream. Similar results hold for simultaneous communication in a distributed setting
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