411 research outputs found
An asymptotic bound for secant varieties of Segre varieties
This paper studies the defectivity of secant varieties of Segre varieties. We
prove that there exists an asymptotic lower estimate for the greater
non-defective secant variety (without filling the ambient space) of any given
Segre variety. In particular, we prove that the ratio between the greater
non-defective secant variety of a Segre variety and its expected rank is lower
bounded by a value depending just on the number of factors of the Segre
variety. Moreover, in the final section, we present some results obtained by
explicit computation, proving the non-defectivity of all the secant varieties
of Segre varieties of the shape (P^n)^4, with 1 < n < 11, except at most
\sigma_199((P^8)^4) and \sigma_357((P^10)^4).Comment: 14 page
Reed-Muller codes for random erasures and errors
This paper studies the parameters for which Reed-Muller (RM) codes over
can correct random erasures and random errors with high probability,
and in particular when can they achieve capacity for these two classical
channels. Necessarily, the paper also studies properties of evaluations of
multi-variate polynomials on random sets of inputs.
For erasures, we prove that RM codes achieve capacity both for very high rate
and very low rate regimes. For errors, we prove that RM codes achieve capacity
for very low rate regimes, and for very high rates, we show that they can
uniquely decode at about square root of the number of errors at capacity.
The proofs of these four results are based on different techniques, which we
find interesting in their own right. In particular, we study the following
questions about , the matrix whose rows are truth tables of all
monomials of degree in variables. What is the most (resp. least)
number of random columns in that define a submatrix having full column
rank (resp. full row rank) with high probability? We obtain tight bounds for
very small (resp. very large) degrees , which we use to show that RM codes
achieve capacity for erasures in these regimes.
Our decoding from random errors follows from the following novel reduction.
For every linear code of sufficiently high rate we construct a new code
, also of very high rate, such that for every subset of coordinates, if
can recover from erasures in , then can recover from errors in .
Specializing this to RM codes and using our results for erasures imply our
result on unique decoding of RM codes at high rate.
Finally, two of our capacity achieving results require tight bounds on the
weight distribution of RM codes. We obtain such bounds extending the recent
\cite{KLP} bounds from constant degree to linear degree polynomials
Efficient processing node proximity via random walk with restart
Graph is a useful tool to model complicated data structures. One important task in graph analysis is assessing node proximity based on graph topology. Recently, Random Walk with Restart (RWR) tends to pop up as a promising measure of node proximity, due to its proliferative applications in e.g. recommender systems, and image segmentation. However, the best-known algorithm for computing RWR resorts to a large LU matrix factorization on an entire graph, which is cost-inhibitive. In this paper, we propose hybrid techniques to efficiently compute RWR. First, a novel divide-and-conquer paradigm is designed, aiming to convert the large LU decomposition into small triangular matrix operations recursively on several partitioned subgraphs. Then, on every subgraph, a “sparse accelerator” is devised to further reduce the time of RWR without any sacrifice in accuracy. Our experimental results on real and synthetic datasets show that our approach outperforms the baseline algorithms by at least one constant factor without loss of exactness
Local Volatility Calibration by Optimal Transport
The calibration of volatility models from observable option prices is a
fundamental problem in quantitative finance. The most common approach among
industry practitioners is based on the celebrated Dupire's formula [6], which
requires the knowledge of vanilla option prices for a continuum of strikes and
maturities that can only be obtained via some form of price interpolation. In
this paper, we propose a new local volatility calibration technique using the
theory of optimal transport. We formulate a time continuous martingale optimal
transport problem, which seeks a martingale diffusion process that matches the
known densities of an asset price at two different dates, while minimizing a
chosen cost function. Inspired by the seminal work of Benamou and Brenier [1],
we formulate the problem as a convex optimization problem, derive its dual
formulation, and solve it numerically via an augmented Lagrangian method and
the alternative direction method of multipliers (ADMM) algorithm. The solution
effectively reconstructs the dynamic of the asset price between the two dates
by recovering the optimal local volatility function, without requiring any time
interpolation of the option prices
Root to Kellerer
We revisit Kellerer's Theorem, that is, we show that for a family of real
probability distributions which increases in convex
order there exists a Markov martingale s.t.\ .
To establish the result, we observe that the set of martingale measures with
given marginals carries a natural compact Polish topology. Based on a
particular property of the martingale coupling associated to Root's embedding
this allows for a relatively concise proof of Kellerer's theorem.
We emphasize that many of our arguments are borrowed from Kellerer
\cite{Ke72}, Lowther \cite{Lo07}, and Hirsch-Roynette-Profeta-Yor
\cite{HiPr11,HiRo12}.Comment: 8 pages, 1 figur
Resolution of Linear Algebra for the Discrete Logarithm Problem Using GPU and Multi-core Architectures
In cryptanalysis, solving the discrete logarithm problem (DLP) is key to
assessing the security of many public-key cryptosystems. The index-calculus
methods, that attack the DLP in multiplicative subgroups of finite fields,
require solving large sparse systems of linear equations modulo large primes.
This article deals with how we can run this computation on GPU- and
multi-core-based clusters, featuring InfiniBand networking. More specifically,
we present the sparse linear algebra algorithms that are proposed in the
literature, in particular the block Wiedemann algorithm. We discuss the
parallelization of the central matrix--vector product operation from both
algorithmic and practical points of view, and illustrate how our approach has
contributed to the recent record-sized DLP computation in GF().Comment: Euro-Par 2014 Parallel Processing, Aug 2014, Porto, Portugal.
\<http://europar2014.dcc.fc.up.pt/\>
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