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Semidefinite Programming for Approximate Maximum Likelihood Sinusoidal Parameter Estimation  [cached]
Kenneth W. K. Lui,H. C. So
EURASIP Journal on Advances in Signal Processing , 2009, DOI: 10.1155/2009/178785
Abstract: We study the convex optimization approach for parameter estimation of several sinusoidal models, namely, single complex/real tone, multiple complex sinusoids, and single two-dimensional complex tone, in the presence of additive Gaussian noise. The major difficulty for optimally determining the parameters is that the corresponding maximum likelihood (ML) estimators involve finding the global minimum or maximum of multimodal cost functions because the frequencies are nonlinear in the observed signals. By relaxing the nonconvex ML formulations using semidefinite programs, high-fidelity approximate solutions are obtained in a globally optimum fashion. Computer simulations are included to contrast the estimation performance of the proposed semi-definite relaxation methods with the iterative quadratic maximum likelihood technique as well as Cramér-Rao lower bound.
Barycentric Hermite Interpolation  [PDF]
Burhan Sadiq,Divakar Viswanath
Mathematics , 2011,
Abstract: Let $z_{1},\ldots,z_{K}$ be distinct grid points. If $f_{k,0}$ is the prescribed value of a function at the grid point $z_{k}$, and $f_{k,r}$ the prescribed value of the $r$\foreignlanguage{american}{-th} derivative, for $1\leq r\leq n_{k}-1$, the Hermite interpolant is the unique polynomial of degree $N-1$ ($N=n_{1}+\cdots+n_{K}$) which interpolates the prescribed function values and function derivatives. We obtain another derivation of a method for Hermite interpolation recently proposed by Butcher et al. {[}\emph{Numerical Algorithms, vol. 56 (2011), p. 319-347}{]}. One advantage of our derivation is that it leads to an efficient method for updating the barycentric weights. If an additional derivative is prescribed at one of the interpolation points, we show how to update the barycentric coefficients using only $\mathcal{O}\left(N\right)$ operations. Even in the context of confluent Newton series, a comparably efficient and general method to update the coefficients appears not to be known. If the method is properly implemented, it computes the barycentric weights with fewer operations than other methods and has very good numerical stability even when derivatives of high order are involved. We give a partial explanation of its numerical stability.
The effects of rounding errors in the nodes on barycentric interpolation  [PDF]
Walter F. Mascarenhas,André Pierro de Camargo
Mathematics , 2013,
Abstract: We analyze the effects of rounding errors in the nodes on barycentric interpolation. These errors are particularly relevant for the first barycentric formula with the Chebyshev points of the second kind. Here, we propose a method for reducing them.
On the backward stability of the second barycentric formula for interpolation  [PDF]
Walter F. Mascarenhas,Andre Pierro de Camargo
Mathematics , 2013,
Abstract: We present a new stability analysis for the second barycentric formula for interpolation, showing that this formula is backward stable when the relevant Lebesgue constant is small.
Error Estimates for Generalized Barycentric Interpolation  [PDF]
Andrew Gillette,Alexander Rand,Chandrajit Bajaj
Mathematics , 2010, DOI: 10.1007/s10444-011-9218-z
Abstract: We prove the optimal convergence estimate for first order interpolants used in finite element methods based on three major approaches for generalizing barycentric interpolation functions to convex planar polygonal domains. The Wachspress approach explicitly constructs rational functions, the Sibson approach uses Voronoi diagrams on the vertices of the polygon to define the functions, and the Harmonic approach defines the functions as the solution of a PDE. We show that given certain conditions on the geometry of the polygon, each of these constructions can obtain the optimal convergence estimate. In particular, we show that the well-known maximum interior angle condition required for interpolants over triangles is still required for Wachspress functions but not for Sibson functions.
The stability of barycentric interpolation at the Chebyshev points of the second kind  [PDF]
Walter F. Mascarenhas
Mathematics , 2013,
Abstract: We present a new analysis of the stability of the first and second barycentric formulae for interpolation at the Chebyshev points of the second kind. Our theory shows that the second formula is more stable than previously thought and our experiments confirm its stability in practice. % OLD: We also explain that the first barycentric formula has accuracy problems which are not properly taken into account in the current literature. We also extend our current understanding regarding the accuracy problems of the first barycentric formula.
Padé-type rational and barycentric interpolation  [PDF]
Claude Brezinski,Michela Redivo-Zaglia
Mathematics , 2011,
Abstract: In this paper, we consider the particular case of the general rational Hermite interpolation problem where only the value of the function is interpolated at some points, and where the function and its first derivatives agree at the origin. Thus, the interpolants constructed in this way possess a Pad\'e--type property at 0. Numerical examples show the interest of the procedure. The interpolation procedure can be easily modified to introduce a partial knowledge on the poles and the zeros of the function to approximated. A strategy for removing the spurious poles is explained. A formula for the error is proved in the real case. Applications are given.
Explicit barycentric weights for polynomial interpolation in the roots or extrema of classical orthogonal polynomials  [PDF]
Haiyong Wang,Daan Huybrechs,Stefan Vandewalle
Mathematics , 2012,
Abstract: Barycentric interpolation is arguably the method of choice for numerical polynomial interpolation. The polynomial interpolant is expressed in terms of function values using the so-called barycentric weights, which depend on the interpolation points. Few explicit formulae for these barycentric weights are known. In [H. Wang and S. Xiang, Math. Comp., 81 (2012), 861--877], the authors have shown that the barycentric weights of the roots of Legendre polynomials can be expressed explicitly in terms of the weights of the corresponding Gaussian quadrature rule. This idea was subsequently implemented in the Chebfun package [L. N. Trefethen and others, The Chebfun Development Team, 2011] and in the process generalized by the Chebfun authors to the roots of Jacobi, Laguerre and Hermite polynomials. In this paper, we explore the generality of the link between barycentric weights and Gaussian quadrature and show that such relationships are related to the existence of lowering operators for orthogonal polynomials. We supply an exhaustive list of cases, in which all known formulae are recovered and also some new formulae are derived, including the barycentric weights for Gauss-Radau and Gauss-Lobatto points. Based on a fast ${\mathcal O}(n)$ algorithm for the computation of Gaussian quadrature, due to Hale and Townsend, this leads to an ${\mathcal O}(n)$ computational scheme for barycentric weights.
Lebesgue Constant Minimizing Shape Preserving Barycentric Rational Interpolation Optimization algorithm  [cached]
Qianjin Zhao,Bingbing Wang,Xianwen Fang
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: The barycentric rational interpolation possesses various advantages in comparison with other interpolation, such as small calculation quantity, no poles and no unattainable points. It is definite when weights are given, so how to choose optimal weights becomes the key issue. A new optimization algorithm to compute the optimal weights was found by minimizing the Lebesgue constant. The biggest advantage of this algorithm is that the linearity of interpolation process with respect to the interpolated function is preserved. In this paper, we will study the shape control in barycentric rational interpolation under this new optimization algorithm, then numerical examples are given to shown the effectiveness of this algorithm.
Bounding the maximum likelihood degree  [PDF]
Nero Budur,Botong Wang
Statistics , 2014,
Abstract: Maximum likelihood estimation is a fundamental computational problem in statistics. In this note, we give a bound for the maximum likelihood degree of algebraic statistical models for discrete data. As usual, such models are identified with special very affine varieties. Using earlier work of Franecki and Kapranov, we prove that the maximum likelihood degree is always less or equal to the signed intersection-cohomology Euler characteristic. We construct counterexamples to a bound in terms of the usual Euler characteristic conjectured by Huh and Sturmfels.
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