Behavioral Models for List Decoding

Recently it has been shown that list decoding of Reed-Solomon codes may be translated into a bivariate interpolation problem. The data consist of pairs in a finite field and the aim is to find a bivariate polynomial that interpolates the given pairs and is minimal with respect to some criterion. We present a systems theoretic approach to this interpolation problem. With the data points we associate a set of time series, also called trajectories. For this set of trajectories we construct the Most Powerful Unfalsified Model (MPUM). This is the smallest possible model that explains these trajectories. The bivariate polynomial is then derived from a specific polynomial representation of the MPUM.     

A discrete adapted hierarchical basis solver for radial basis function interpolation

In this paper we develop a discrete Hierarchical Basis (HB) to efficiently solve the Radial Basis Function (RBF) interpolation problem with variable polynomial degree. The HB forms an orthogonal set and is adapted to the kernel seed function and the placement of the interpolation nodes. Moreover, this basis is orthogonal to a set of polynomials up to a given degree defined on the interpolating nodes. We are thus able to decouple the RBF interpolation problem for any degree of the polynomial interpolation and solve it in two steps: (1) The polynomial orthogonal RBF interpolation problem is efficiently solved in the transformed HB basis with a GMRES iteration and a diagonal (or block SSOR) preconditioner. (2) The residual is then projected onto an orthonormal polynomial basis. We apply our approach on several test cases to study its effectiveness.     

High-Dimensional Adaptive Sparse Polynomial Interpolation and Applications to Parametric PDEs

We consider the problem of Lagrange polynomial interpolation in high or countably infinite dimension, motivated by the fast computation of solutions to partial differential equations (PDEs) depending on a possibly large number of parameters which result from the application of generalised polynomial chaos discretisations to random and stochastic PDEs. In such applications there is a substantial advantage in considering polynomial spaces that are sparse and anisotropic with respect to the different parametric variables. In an adaptive context, the polynomial space is enriched at different stages of the computation. In this paper, we study an interpolation technique in which the sample set is incremented as the polynomial dimension increases, leading therefore to a minimal amount of PDE solving. This construction is based on the standard principle of tensorisation of a one-dimensional interpolation scheme and sparsification. We derive bounds on the Lebesgue constants for this interpolation process in terms of their univariate counterpart. For a class of model elliptic parametric PDE’s, we have shown in Chkifa et al. (Modél. Math. Anal. Numér. 47(1):253–280, 2013) that certain polynomial approximations based on Taylor expansions converge in terms of the polynomial dimension with an algebraic rate that is robust with respect to the parametric dimension. We show that this rate is preserved when using our interpolation algorithm. We also propose a greedy algorithm for the adaptive selection of the polynomial spaces based on our interpolation scheme, and illustrate its performance both on scalar valued functions and on parametric elliptic PDE’s.     

A simplex-based numerical framework for simple and efficient robust design optimization

The Simplex Stochastic Collocation (SSC) method is an efficient algorithm for uncertainty quantification (UQ) in computational problems with random inputs. In this work, we show how its formulation based on simplex tessellation, high degree polynomial interpolation and adaptive refinements can be employed in problems involving optimization under uncertainty. The optimization approach used is the Nelder-Mead algorithm (NM), also known as Downhill Simplex Method. The resulting SSC/NM method, called Simplex², is based on (i) a coupled stopping criterion and (ii) the use of an high-degree polynomial interpolation in the optimization space for accelerating some NM operators. Numerical results show that this method is very efficient for mono-objective optimization and minimizes the global number of deterministic evaluations to determine a robust design. This method is applied to some analytical test cases and a realistic problem of robust optimization of a multi-component airfoil.     

基于EEP法的一维有限元自适应求解

基于新近提出的一维有限元后处理超收敛算法——单元能量投影（EEP）法,将有限元自适应求解问题转化为对超收敛解答的自适应分段多项式插值问题;对于大多数问题,一步便可获得满意的有限元网格划分,在该网格上再次进行有限元计算,一般即可获得满足用户给定的误差限的有限元解答．即便未能完全满足精度要求,一般只需局部细分加密网格一至二步即可．该法简单实用、高效可靠,是一个颇具优势和潜力的自适应方法．以二阶椭圆型常微分方程模型问题为例,对该法的基本思想、实施策略及具体算法做一介绍,并给出有代表性的数值算例用以展示该法的优良性能和效果．     

Improvements on the Johnson bound for Reed-Solomon codes

For Reed-Solomon codes with block length n and dimension k, the Johnson theorem states that for a Hamming ball of radius smaller than , there can be at most O(n²) codewords. It was not known whether for larger radius, the number of codewords is polynomial. The best known list decoding algorithm for Reed-Solomon codes due to Guruswami and Sudan [Venkatesan Guruswami, Madhu Sudan, Improved decoding of Reed-Solomon and algebraic-geometry codes, IEEE Transactions on Information Theory 45 (6) (1999) 1757-1767] is also known to work in polynomial time only within this radius. In this paper we prove that when k<αn for any constant 0<α<1, we can overcome the barrier of the Johnson bound for list decoding of Reed-Solomon codes (even if the field size is exponential). More specifically in such a case, we prove that for Hamming ball of radius (for any c>0) there can be at most number of codewords. For any constant c, we describe a polynomial time algorithm for enumerating all of them, thereby also improving on the Guruswami-Sudan algorithm. Although the improvement is modest, this provides evidence for the first time that the bound is not sacrosanct for such a high rate.We apply our method to obtain sharper bounds on a list recovery problem introduced by Guruswami and Rudra [Venkatesan Guruswami, Atri Rudra, Limits to list decoding Reed-Solomon codes, IEEE Transactions on Information Theory 52 (8) (2006) 3642-3649] where they establish super-polynomial lower bounds on the output size when the list size exceeds . We show that even for larger list sizes the problem can be solved in polynomial time for certain values of k.     

Convex interval interpolation using a three-term staircase algorithm

Motivated by earlier considerations of interval interpolation problems as well as a particular application to the reconstruction of railway bridges, we deal with the problem of univariate convexity preserving interval interpolation. To allow convex interpolation, the given data intervals have to be in (strictly) convex position. This property is checked by applying an abstract three-term staircase algorithm, which is presented in this paper. Additionally, the algorithm provides strictly convex ordinates belonging to the data intervals. Therefore, the known methods in convex Lagrange interpolation can be used to obtain interval interpolants. In particular, we refer to methods based on polynomial splines defined on grids with additional knots. Received September 22, 1997 / Revised version received May 26, 1998     

On the Key Equation Over a Commutative Ring

We define alternant codes over a commutative ring R and a corresponding key equation. We show that when the ring is a domain, e.g. the p-adic integers, the error-locator polynomial is the unique monic minimal polynomial (equivalently, the unique shortest linear recurrence) of the finite sequence of syndromes and that it can be obtained by Algorithm MR of Norton.WhenR is a local ring, we show that the syndrome sequence may have more than one (monic) minimal polynomial, but that all the minimal polynomials coincide modulo the maximal ideal ofR . We characterise the set of minimal polynomials when R is a Hensel ring. We also apply these results to decoding alternant codes over a local ring R: it is enough to find any monic minimal polynomial over R and to find its roots in the residue field. This gives a decoding algorithm for alternant codes over a finite chain ring, which generalizes and improves a method of Interlando et. al. for BCH and Reed-Solomon codes over a Galois ring.     

Parameter choices and a better bound on the list size in the Guruswami–Sudan algorithm for algebraic geometry codes

Given an algebraic geometry code C_L(D, aP){C_{\mathcal L}(D, \alpha P)}, the Guruswami–Sudan algorithm produces a list of all codewords in C_L(D, aP){C_{\mathcal L}(D, \alpha P)} within a specified distance of a received word. The initialization step in the algorithm involves parameter choices that bound the degree of the interpolating polynomial and hence the length of the list of codewords generated. In this paper, we use simple properties of discriminants of polynomials over finite fields to provide improved parameter choices for the Guruswami–Sudan list decoding algorithm for algebraic geometry codes. As a consequence, we obtain a better bound on the list size as well as a lower degree interpolating polynomial.     

Computing the matrix sign and absolute value functions

We present two algorithms for the computation of the matrix sign and absolute value functions. Both algorithms avoid a complete diagonalisation of the matrix, but they however require some informations regarding the eigenvalues location. The first algorithm consists in a sequence of polynomial iterations based on appropriate estimates of the eigenvalues, and converging to the matrix sign if all the eigenvalues are real. Convergence is obtained within a finite number of steps when the eigenvalues are exactly known. Nevertheless, we present a second approach for the computation of the matrix sign and absolute value functions, when the eigenvalues are exactly known. This approach is based on the resolution of an interpolation problem, can handle the case of complex eigenvalues and appears to be faster than the iterative approach. To cite this article: M. Ndjinga, C. R. Acad. Sci. Paris, Ser. I 346 (2008).     

On Ordinary Words of Standard Reed-Solomon Codes over Finite Fields

Reed-Solomon codes are widely used to establish a reliable channel to transmit information in digital communication which has a strong error correction capability and a variety of efficient decoding algorithm.Usually we use the maximum likelihood decoding(MLD) algorithm in the decoding process of Reed-Solomon codes.MLD algorithm relies on determining the error distance of received word.Dür,Guruswami,Wan,Li,Hong,Wu,Yue and Zhu et al.got some results on the error distance.For the Reed-Solomon code C,the received word u is called an ordinary word of C if the error distance d(u,C) =n-deg u(x) with u(x) being the Lagrange interpolation polynomial of u.We introduce a new method of studying the ordinary words.In fact,we make use of the result obtained by Y.C.Xu and S.F.Hong on the decomposition of certain polynomials over the finite field to determine all the ordinary words of the standard Reed-Solomon codes over the finite field of q elements.This completely answers an open problem raised by Li and Wan in[On the subset sum problem over finite fields,Finite Fields Appl.14 (2008) 911-929].     

A Generalized Rational Interpolation Problem and the Solution of the Welch–Berlekamp Key Equation

We show that an algorithm designed to solve the Welch–Berlekamp key equation may also be used to solve a more general problem, which can be regarded as a finite analogue of a generalized rational interpolation problem. As a consequence, we show that a single algorithm exists which can solve both Berlekamp's classical key equation (usually solved by the Berlekamp–Massey algorithm) and the Welch–Berlekamp key equation which arise in the decoding of Reed–Solomon codes.     

一类非线性矩阵方程对称解的双迭代算法

利用逆矩阵的Neumann级数形式,将在Schur插值问题中遇到的含未知矩阵二次项之逆的非线性矩阵方程转化为高次多项式矩阵方程,然后采用牛顿算法求高次多项式矩阵方程的对称解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程的对称解或者对称最小二乘解,建立求非线性矩阵方程的对称解的双迭代算法.双迭代算法仅要求非线性矩阵方程有对称解,不要求它的对称解唯一,也不对它的系数矩阵做附加限定.数值算例表明,双迭代算法是有效的.     

The Neville-like form of the Fitzpatrick algorithm for rational interpolation

The Fitzpatrick algorithm, which seeks a Gr?bner basis for the solution of a system of polynomial congruences, can be applied to compute a rational interpolant. Based on the Fitzpatrick algorithm and the properties of an Hermite interpolation basis, we present a Neville-like algorithm for multivariate osculatory rational interpolation. It may be used to compute the values of osculatory rational interpolants at some points directly without computing the rational interpolation function explicitly.     

An improvement on Nourein's method for the simultaneous determination of the zeroes of a polynomial (An algorithm)

In a recent paper [2], Nourein derived an iteration formula, which exhibited cubic convergence for the simultaneous determination of the zeroes of a polynomial. In this paper - following quite a different appraoch - we derive a method which can be viewed as an improvement on that of [2]. The derivation is based on the approximation of the polynomial in question by a Lagrange interpolation formula. We give the algorithm in ALGOL 60. For a given real polynomial, the algorithm caters for the general case of complex zeroes.     

Arc Length Estimation and the Convergence of Polynomial Curve Interpolation

When fitting parametric polynomial curves to sequences of points or derivatives we have to choose suitable parameter values at the interpolation points. This paper investigates the effect of the parameterization on the approximation order of the interpolation. We show that chord length parameter values yield full approximation order when the polynomial degree is at most three. We obtain full approximation order for arbitrary degree by developing an algorithm which generates more and more accurate approximations to arc length: the lengths of the segments of an interpolant of one degree provide parameter intervals for interpolants of degree two higher. The algorithm can also be used to estimate the length of a curve and its arc-length derivatives. AMS subject classification (2000) 65D05, 65D10     

Polynomial interpolation in several variables

This is a survey of the main results on multivariate polynomial interpolation in the last twenty-five years, a period of time when the subject experienced its most rapid development. The problem is considered from two different points of view: the construction of data points which allow unique interpolation for given interpolation spaces as well as the converse. In addition, one section is devoted to error formulas and another to connections with computer algebra. An extensive list of references is also included. This revised version was published online in June 2006 with corrections to the Cover Date.     

COMPUTATION OF VECTOR VALUED BLENDING RATIONAL INTERPOLANTS

As we know, Newton's interpolation polynomial is based on divided differences which can be calculated recursively by the divided-difference scheme while Thiele 's interpolating continued fractions are geared towards determining a rational function which can also be calculated recursively by so-called inverse differences. In this paper, both Newton's interpolation polynomial and Thiele's interpolating continued fractions are incorporated to yield a kind of bivariate vector valued blending rational interpolants by means of the Samelson inverse. Blending differences are introduced to calculate the blending rational interpolants recursively, algorithm and matrix-valued case are discussed and a numerical example is given to illustrate the efficiency of the algorithm.     

A sequential quadratic programming algorithm for equality-constrained optimization without derivatives

In this paper, we present a new model-based trust-region derivative-free optimization algorithm which can handle nonlinear equality constraints by applying a sequential quadratic programming (SQP) approach. The SQP methodology is one of the best known and most efficient frameworks to solve equality-constrained optimization problems in gradient-based optimization [see e.g. Lalee et al. (SIAM J Optim 8:682–706, 1998), Schittkowski (Optim Lett 5:283–296, 2011), Schittkowski and Yuan (Wiley encyclopedia of operations research and management science, Wiley, New York, 2010)]. Our derivative-free optimization (DFO) algorithm constructs local polynomial interpolation-based models of the objective and constraint functions and computes steps by solving QP sub-problems inside a region using the standard trust-region methodology. As it is crucial for such model-based methods to maintain a good geometry of the set of interpolation points, our algorithm exploits a self-correcting property of the interpolation set geometry. To deal with the trust-region constraint which is intrinsic to the approach of self-correcting geometry, the method of Byrd and Omojokun is applied. Moreover, we will show how the implementation of such a method can be enhanced to outperform well-known DFO packages on smooth equality-constrained optimization problems. Numerical experiments are carried out on a set of test problems from the CUTEst library and on a simulation-based engineering design problem.