SIAMCO
2010
13 years 2 months ago
2010
Abstract. The problem of reliably transmitting a real-valued random vector through a digital noisy channel is relevant for the design of distributed estimation and control techniqu...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
Abstract--In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods propose...
MOC
2002
13 years 4 months ago
2002
Abstract. We analyze the convergence rate of an asynchronous space decomposition method for constrained convex minimization in a reflexive Banach space. This method includes as spe...
MOC
2000
13 years 4 months ago
2000
The ultraconvergence property of a gradient recovery technique proposed by Zienkiewicz and Zhu is analyzed for the Laplace equation in the two dimensional setting. Under the assump...
CPC
2002
13 years 4 months ago
2002
Co-learning is a model involving agents from a large population, who interact by playing a fixed game and update their behaviour based on previous experience and the outcome of th...
COMBINATORICS
2004
13 years 4 months ago
2004
This paper finds and analyzes a formula for the total variation distance between iterations of riffle shuffles and iterations of "cut and then riffle shuffle". This allo...
CAGD
2007
13 years 4 months ago
2007
We present an algorithm which is able to compute all roots of a given univariate polynomial within a given interval. In each step, we use degree reduction to generate a strip boun...
SIP
2001
13 years 5 months ago
2001
We propose a new adaptive filtering algorithm whose convergence rate is very fast even for a highly correlated input signal. It is well-known that convergence rate gets worse when...
NIPS
2008
13 years 6 months ago
2008
We provide a new analysis of an efficient margin-based algorithm for selective sampling in classification problems. Using the so-called Tsybakov low noise condition to parametrize...
NIPS
2008
13 years 6 months ago
2008
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...