Preprint Series 2004
 2004:27 V. Temlyakov
 2004:26 T. Jin and R. Yeh
 2004:25 J. Griggs and T. Jin
 2004:24 J. Griggs and T. Jin
 2004:23 V. Temlyakov
 2004:22 R. DeVore, G. Kerkyacharian, D. Picard, and V. Temlyakov
 2004:21 S. Brenner and J. Zhao
 2004:20 P. Binev, A. Cohen, W. Dahmen, R. DeVore, and V. Temlyakov
 2004:19 P. Binev, W. Dahmen, R. DeVore, and N. Dyn
 2004:18 K. Park
 2004:17 G. Kyriazis and P. Petrushev
 2004:16 G. Kyriazis, K. Park, and P. Petrushev
 2004:15 S. Brenner and K. Wang
 2004:14 S. Konyagin and V. Temlyakov
 2004:13 R. Kellogg and M. Stynes
 2004:12 R. Sharpley and V. Vatchev
 2004:11 S. Brenner and L. Sung
 2004:10 R. DeVore, G. Kerkyacharian, D. Picard, and V. Temlyakov
 2004:09 S. Konyagin and V. Temlyakov

2004:08
A. Fokas,
A. Its, and
L. Sung
Assuming that the solution q(x, t) of the nonlinear Schrödinger equation on the halfline exists, it has been shown that q(x, t) can be represented in terms of the solution of a matrix RiemannHilbert (RH) problem formulated in the complex kplane. The jump matrix of this RH ...
[Full Abstract] 
2004:07
W. Dahmen and
P. Petrushev
This paper is concerned with further developing and refining the analysis of a recent algorithmic paradigm for nonlinear approximation termed "Pushtheerror" scheme. It is especially designed to deal with $L _ \infty$ approximation in a multilevel framework. The original version is extended considerably to cover all commonly used multiresolution frameworks ...
[Full Abstract] 
2004:06
D. Donoho,
M. Elad, and
V. Temlyakov
Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. We prove the possibility of stable recovery under a combination of sufficient sparsity ...
[Full Abstract] 
2004:05
S. Konyagin and
V. Temlyakov
We continue investigation of some problems in learning theory in the setting formulated by F. Cucker and S. Smale [CS]. The goal is to find an estimator $f _ z$ on the base of given data $z := ((x _ 1, y _ 1), . . . , (x _ m, y _ m))$ that ...
[Full Abstract] 
2004:04
L. Székely and
H. Wang
We study that over a certain type of trees (e.g. all trees or all binary trees) with a given number of vertices, which trees minimize or maximize the total number of subtrees (or subtrees with at least one leaf). 'frees minimizing the total number of subtrees (or subtrees with ...
[Full Abstract] 
2004:03
É. Czabarka,
O. Sykora,
L. Székely, and
I. Vrto
We extend the lower bound in [15] for the outerplanar crossing number (in other terminologies also called convex, circular and onepage book crossing number) for a more general setting. In this setting we can show a better lower bound for the outerplanar crossing number of hypercubes than the best lower ...
[Full Abstract] 
2004:02
P. Erdős,
A. Seress, and
L. Székely
The function lattice, or generalized Boolean algebra, is the set of $l$tuples with the ith coordinate an integer between 0 and a bound $n _ i$. Two $l$tuples tintersect if they have at least t common nonzero coordinates. We prove a Hilton–Milner type theorem for ...
[Full Abstract] 
2004:01
L. Székely
Jin and Liu discovered an elegant formula for the number of rooted spanning forests in the complete bipartite graph $K _ {a1,a2}$, with $b _ 1$ roots in the first vertex class and $b _ 2$ roots in the second vertex class. We give a simple proof to their ...
[Full Abstract]