4,204 research outputs found
Interpolation and Approximation of Polynomials in Finite Fields over a Short Interval from Noisy Values
Motivated by a recently introduced HIMMO key distribution scheme, we consider
a modification of the noisy polynomial interpolation problem of recovering an
unknown polynomial from approximate values of the residues of
modulo a prime at polynomially many points taken from a short
interval
Numerical Analysis of the Non-uniform Sampling Problem
We give an overview of recent developments in the problem of reconstructing a
band-limited signal from non-uniform sampling from a numerical analysis view
point. It is shown that the appropriate design of the finite-dimensional model
plays a key role in the numerical solution of the non-uniform sampling problem.
In the one approach (often proposed in the literature) the finite-dimensional
model leads to an ill-posed problem even in very simple situations. The other
approach that we consider leads to a well-posed problem that preserves
important structural properties of the original infinite-dimensional problem
and gives rise to efficient numerical algorithms. Furthermore a fast multilevel
algorithm is presented that can reconstruct signals of unknown bandwidth from
noisy non-uniformly spaced samples. We also discuss the design of efficient
regularization methods for ill-conditioned reconstruction problems. Numerical
examples from spectroscopy and exploration geophysics demonstrate the
performance of the proposed methods
Fast multi-dimensional scattered data approximation with Neumann boundary conditions
An important problem in applications is the approximation of a function
from a finite set of randomly scattered data . A common and powerful
approach is to construct a trigonometric least squares approximation based on
the set of exponentials . This leads to fast numerical
algorithms, but suffers from disturbing boundary effects due to the underlying
periodicity assumption on the data, an assumption that is rarely satisfied in
practice. To overcome this drawback we impose Neumann boundary conditions on
the data. This implies the use of cosine polynomials as basis
functions. We show that scattered data approximation using cosine polynomials
leads to a least squares problem involving certain Toeplitz+Hankel matrices. We
derive estimates on the condition number of these matrices. Unlike other
Toeplitz+Hankel matrices, the Toeplitz+Hankel matrices arising in our context
cannot be diagonalized by the discrete cosine transform, but they still allow a
fast matrix-vector multiplication via DCT which gives rise to fast conjugate
gradient type algorithms. We show how the results can be generalized to higher
dimensions. Finally we demonstrate the performance of the proposed method by
applying it to a two-dimensional geophysical scattered data problem
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