An algorithm for combining data sets having different frames of reference

The task of fitting a curve or surface to a number of related data sets is common in metrology. Thedata matching problem arises when sets of supplementary measurements are added to those of an underlying base set. Each supplementary set may have its own reference frame. This problem is formally equivalent to thedata splicing problem in which no one set covers the entire region of interest. In either case, the presence of reference-frame transformation parameters means that least-squares fitting of a curve to the assembled data is generally a nonlinear problem. A linear version of this problem was treated in Anthony and Harris [1].An algorithm is presented which exploits the inherent block structure of the problem, thereby reducing memory requirements and execution time. It employs the Gauss-Newton optimisation technique, returning both the fittingand reference-frame transformation parameters. The approach is quite general. Results are presented in the case of indentation analysis (for hardness testing, where the critical parameters are those of the fit). Applications also arise for which the transformation parameters are of primary interest; one such application is indicated.     

Traveling Wave Solutions for Combustion in a Porous Medium

Traveling wave solutions are sought for a model of combustion in a porous medium. The problem is formulated as a nonlinear eigenvalue problem for a system of ordinary differential equations of order four, defined over an infinite interval. A shooting method is used to prove existence, and a priori bounds for the solution and parameters are obtained.     

Compression and denoising using <Emphasis Type="Italic">l</Emphasis><Subscript>0</Subscript>-norm

In this paper, we deal with l ₀-norm data fitting and total variation regularization for image compression and denoising. The l ₀-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l ₀-norm, it is usually approximated by other functions such as the l ₁-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l ₀-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm.     

Construction of rational surfaces yielding good codes

In the present article, we consider Algebraic Geometry codes on some rational surfaces. The estimate of the minimum distance is translated into a point counting problem on plane curves. This problem is solved by applying the upper bound à la Weil of Aubry and Perret together with the bound of Homma and Kim for plane curves. The parameters of several codes from rational surfaces are computed. Among them, the codes defined by the evaluation of forms of degree 3 on an elliptic quadric are studied. As far as we know, such codes have never been treated before. Two other rational surfaces are studied and very good codes are found on them. In particular, a [57,12,34] code over F₇ and a [91,18,53] code over F₉ are discovered, these codes beat the best known codes up to now.     

Genetic algorithms for the selection of smoothing parameters in additive models

Summary  Additive models of the type y=f ₁(x ₁)+...+f _p(x _p)+ε where f _j , j=1,..,p, have unspecified functional form, are flexible statistical regression models which can be used to characterize nonlinear regression effects. One way of fitting additive models is the expansion in B-splines combined with penalization which prevents overfitting. The performance of this penalized B-spline (called P-spline) approach strongly depends on the choice of the amount of smoothing used for components f _j . In particular for higher dimensional settings this is a computationaly demanding task. In this paper we treat the problem of choosing the smoothing parameters for P-splines by genetic algorithms. In several simulation studies this approach is compared to various alternative methods of fitting additive models. In particular functions with different spatial variability are considered and the effect of constant respectively local adaptive smoothing parameters is evaluated.     

Fitting scattered data on spherelike surfaces using tensor products of trigonometric and polynomial splines

Summary A method is presented for fitting a function defined on a general smooth spherelike surfaceS, given measurements on the function at a set of scattered points lying onS. The approximating surface is constructed by mapping the surface onto a rectangle, and using a tensor-product of polynomial splines with periodic trigonometric splines. The use of trigonometric splines allows a convenient solution of the problem of assuring that the resulting surface is continuous and has continuous tangent planes at all points onS. Two alternative algorithms for computing the coefficients of the tensor fit are presented; one based on global least-squares, and the other on the use of local quasi-interpolators. The approximation order of the method is established, and the numerical performance of the two algorithms is compared.Supported in part by the National Science Foundation under Grant DMS-8902331 and by the Alexander von Humboldt Foundation     

On Optimization over the Efficient Set in Linear Multicriteria Programming

The efficient set of a linear multicriteria programming problem can be represented by a reverse convex constraint of the form g(z)≤0, where g is a concave function. Consequently, the problem of optimizing some real function over the efficient set belongs to an important problem class of global optimization called reverse convex programming. Since the concave function used in the literature is only defined on some set containing the feasible set of the underlying multicriteria programming problem, most global optimization techniques for handling this kind of reverse convex constraint cannot be applied. The main purpose of our article is to present a method for overcoming this disadvantage. We construct a concave function which is finitely defined on the whole space and can be considered as an extension of the existing function. Different forms of the linear multicriteria programming problem are discussed, including the minimum maximal flow problem as an example. The research was partly done while the third author was visiting the Department of Mathematics, University of Trier with the support by the Alexander von Humboldt Foundation. He thanks the university as well as the foundation.     

Generalized Isotonic Regression

We present a new computational and statistical approach for fitting isotonic models under convex differentiable loss functions through recursive partitioning. Models along the partitioning path are also isotonic and can be viewed as regularized solutions to the problem. Our approach generalizes and subsumes the well-known work of Barlow and Brunk on fitting isotonic regressions subject to specially structured loss functions, and expands the range of loss functions that can be used (e.g., adding Huber loss for robust regression). This is accomplished through an algorithmic adjustment to a recursive partitioning approach recently developed for solving large-scale ?₂-loss isotonic regression problems. We prove that the new algorithm solves the generalized problem while maintaining the favorable computational and statistical properties of the l₂ algorithm. The results are demonstrated on both real and synthetic data in two settings: fitting count data using negative Poisson log-likelihood loss, and fitting robust isotonic regressions using Huber loss. Proofs of theorems and a MATLAB-based software package implementing our algorithm are available in the online supplementary materials.     

On the piezotronics and tactile sensing virtual simulation

A flexible finger with muscles made of Nitinol wires and the skin made of auxetic material is analyzed from the tactile sensing point of view. The recognizing of the shape and texture of 3D objects is performed by simulation the action of an array of nanopiezotronic transistors integrated into the skin. The array of nanopiezotronic transistors makes possible the detection of the pressure-induced changes in the auxetic skin. The shape and texture of the objects is best estimated by determining the surface and texture as an n-ellipsoid defined by 12 parameters. An inverse problem is solved in order to find these parameters from the condition that the n-ellipsoid best fits the set of data points probed by touch with the finger. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Complexity of Weighted Approximation over

We study approximation of univariate functions defined over the reals. We assume that the rth derivative of a function is bounded in a weighted L_p norm with a weight ψ. Approximation algorithms use the values of a function and its derivatives up to order r−1. The worst case error of an algorithm is defined in a weighted L_q norm with a weight ρ. We study the worst case (information) complexity of the weighted approximation problem, which is equal to the minimal number of function and derivative evaluations needed to obtain error . We provide necessary and sufficient conditions in terms of the weights ψ and ρ, as well as the parameters r, p, and q for the weighted approximation problem to have finite complexity. We also provide conditions which guarantee that the complexity of weighted approximation is of the same order as the complexity of the classical approximation problem over a finite interval. Such necessary and sufficient conditions are also provided for a weighted integration problem since its complexity is equivalent to the complexity of the weighted approximation problem for q=1.