Elimination and resultants.2. Multivariate resultants

In part 1 we explained how resultants apply to problems in computer graphics and geometric modeling. This tutorial concerns methods for constructing multivariate resultants, including methods for bidegree and equal-degree polynomial equations. It also describes u-resultants, a powerful use of standard resultants     

Cyclic resultants

We characterize polynomials having the same set of nonzero cyclic resultants. Generically, for a polynomial f of degree d, there are exactly 2^d−1 distinct degree d polynomials with the same set of cyclic resultants as f. However, in the generic monic case, degree d polynomials are uniquely determined by their cyclic resultants. Moreover, two reciprocal (“palindromic”) polynomials giving rise to the same set of nonzero cyclic resultants are equal. In the process, we also prove a unique factorization result in semigroup algebras involving products of binomials. Finally, we discuss how our results yield algorithms for explicit reconstruction of polynomials from their cyclic resultants.     

Fast computation of special resultants

We propose fast algorithms for computing composed products and composed sums, as well as diamond products of univariate polynomials. These operations correspond to special multivariate resultants, that we compute using power sums of roots of polynomials, by means of their generating series.     

Rational Univariate Reduction via toric resultants

We describe algorithms for solving a given system of multivariate polynomial equations via the Rational Univariate Reduction (RUR). We compute the RUR from the toric resultant of the input system. Our algorithms derandomize several of the choices made in similar prior algorithms. We also propose a new derandomized algorithm for solving an overdetermined system. Finally, we analyze the computational complexity of the algorithm, and discuss its implementation and performance.     

Comparing acceleration techniques for the Dixon and Macaulay resultants

The Bezout–Dixon resultant method for solving systems of polynomial equations lends itself to various heuristic acceleration techniques, previously reported by the present author, which can be extraordinarily effective. In this paper we will discuss how well these techniques apply to the Macaulay resultant. In brief, we find that they do work there with some difficulties, but the Dixon method is greatly superior.     

Linear complete differential resultants and the implicitization of linear DPPEs

The linear complete differential resultant of a finite set of linear ordinary differential polynomials is defined. We study the computation by linear complete differential resultants of the implicit equation of a system of n$n$ linear differential polynomial parametric equations in n−1$n-1$ differential parameters. We give necessary conditions to ensure properness of the system of differential polynomial parametric equations.     

Some applications of resultants to problems in computational geometry

Resultants were originally developed in the 18th and 19th centuries to solve certain simple algebraic problems. Here we shall present some applications of resultants to several important problems in computational geometry, including the implicitization, inversion, and intersection of parametric rational polynomial curves and surfaces.This paper was first delivered orally at the International Conference on Engineering and Computer Graphics in Beijing, China, held in August 1984     

Some applications of resultants to problems in computational geometry

Resultants were originally developed in the 18th and 19th centuries to solve certain simple algebraic problems. Here we shall present some applications of resultants to several important problems in computational geometry, including the implicitization, inversion, and intersection of parametric rational polynomial curves and surfaces.     

Single-lifting Macaulay-type formulae of generalized unmixed sparse resultants

Resultants are defined in the sparse (or toric) context in order to exploit the structure of the polynomials as expressed by their Newton polytopes. Since determinantal formulae are not always possible, the most efficient general method for computing resultants is rational formulae. This is made possible by Macaulay’s famous determinantal formula in the dense homogeneous case, extended by D’Andrea to the sparse case. However, the latter requires a lifting of the Newton polytopes, defined recursively on the dimension. Our main contribution is a single-lifting function of the Newton polytopes, which avoids recursion, and yields a simpler method for computing Macaulay-type formulae of sparse resultants. We focus on the case of generalized unmixed systems, where all Newton polytopes are scaled copies of each other, and sketch how our approach may extend to mixed systems of up to four polynomials, as well as those whose Newton polytopes have a sufficiently different face structure. In the mixed subdivision used to construct the matrices, our algorithm defines significantly fewer cells than D’Andrea’s, though the matrix formulae are same. We discuss asymptotic complexity bounds and illustrate our results by fully studying a bivariate example.     

A geometric programming approach for bivariate cubic L1 splines

Bivariate cubic L₁ splines provide C¹-smooth, shape-preserving interpolation of arbitrary data, including data with abrupt changes in spacing and magnitude. The minimization principle for bivariate cubic L₁ splines results in a nondifferentiable convex optimization problem. This problem is reformulated as a generalized geometric programming problem. A geometric dual with a linear objective function and convex cubic constraints is derived. A linear system for dual-to-primal conversion is established. The results of computational experiments are presented.     

Semi-implicit representations of surfaces in P3, resultants and applications

In this paper we introduce an intermediate representation of surfaces that we call semi-implicit. We give a general definition in the language of projective complex algebraic geometry, and we begin its systematic study with an effective view-point. Our last section will apply this representation to investigate the intersection of two bi-cubic surfaces; these surfaces are widely used in Computer Aided Geometric Design.     

Smooth semiparametric and nonparametric Bayesian estimation of bivariate densities from bivariate histogram data

Penalized B-splines combined with the composite link model are used to estimate a bivariate density from a histogram with wide bins. The goals are multiple: they include the visualization of the dependence between the two variates, but also the estimation of derived quantities like Kendall’s tau, conditional moments and quantiles. Two strategies are proposed: the first one is semiparametric with flexible margins modeled using B-splines and a parametric copula for the dependence structure; the second one is nonparametric and is based on Kronecker products of the marginal B-spline bases. Frequentist and Bayesian estimations are described. A large simulation study quantifies the performances of the two methods under different dependence structures and for varying strengths of dependence, sample sizes and amounts of grouping. It suggests that Schwarz’s BIC is a good tool for classifying the competing models. The density estimates are used to evaluate conditional quantiles in two applications in social and in medical sciences.     

Using resultants for SAGBI basis verification in the univariate polynomial ring

A resultant-type identity for univariate polynomials is proved and used to characterise SAGBI bases of subalgebras generated by two polynomials. A new equivalent condition, expressed in terms of the degree of a field extension, for a pair of univariate polynomials to form a SAGBI basis is derived.     

Computing resultants on Graphics Processing Units: Towards GPU-accelerated computer algebra

In this article we report on our experience in computing resultants of bivariate polynomials on Graphics Processing Units (GPU). Following the outline of Collins’ modular approach [6], our algorithm starts by mapping the input polynomials to a finite field for sufficiently many primes m$m$. Next, the GPU algorithm evaluates the polynomials at a number of fixed points x∈_Zm$x\in {\mathbb{Z}}_m$, and computes a set of univariate resultants for each modular image. Afterwards, the resultant is reconstructed using polynomial interpolation and Chinese remaindering. The GPU returns resultant coefficients in the form of Mixed Radix (MR) digits. Finally, large integer coefficients are recovered from the MR representation on the CPU. All computations performed by the algorithm (except for, partly, Chinese remaindering) are outsourced to the graphics processor thereby minimizing the amount of work to be done on the host machine. The main theoretical contribution of this work is the modification of Collins’ modular algorithm using the methods of matrix algebra to make an efficient realization on the GPU feasible. According to the benchmarks, our algorithm outperforms a CPU-based resultant algorithm from 64-bit Maple 14 by a factor of 100.     

Recurrence relations for bivariate and extended skew- distributions and an application to order statistics from bivariate

In this paper, we derive recurrence relations for cumulative distribution functions (cdf’s) of bivariate t and extended skew-t distributions. These recurrence relations are over ν (the degrees of freedom), and starting from the known results for ν=1 and ν=2, they will allow for the recursive evaluation of the distribution function for any other positive integral value of ν. Then, we consider a linear combination of order statistics from a bivariate t distribution with an arbitrary mean vector and show that its cdf is a mixture of cdf’s of the extended skew-t distributions. This mixture form, along with the explicit expressions of the cdf’s of the extended skew-t distributions, enables us to derive explicit expressions for the cdf of the linear combination for any positive integral value of ν.     

Semiparametric bivariate Archimedean copulas

While parametric copulas often lack expressive capacity to capture the complex dependencies that are usually found in empirical data, non-parametric copulas can have poor generalization performance because of overfitting. A semiparametric copula method based on the family of bivariate Archimedean copulas is introduced as an intermediate approach that aims to provide both accurate and robust fits. The Archimedean copula is expressed in terms of a latent function that can be readily represented using a basis of natural cubic splines. The model parameters are determined by maximizing the sum of the log-likelihood and a term that penalizes non-smooth solutions. The performance of the semiparametric estimator is analyzed in experiments with simulated and real-world data, and compared to other methods for copula estimation: three parametric copula models, two semiparametric estimators of Archimedean copulas previously introduced in the literature, two flexible copula methods based on Gaussian kernels and mixtures of Gaussians and finally, standard parametric Archimedean copulas. The good overall performance of the proposed semiparametric Archimedean approach confirms the capacity of this method to capture complex dependencies in the data while avoiding overfitting.     

Approximate greatest common divisor of many polynomials, generalised resultants, and strength of approximation

The computation of the greatest common divisor (GCD) of many polynomials is a nongeneric problem. Techniques defining “approximate GCD” solutions have been defined, but the proper definition of the “approximate” GCD, and the way we can measure the strength of the approximation has remained open. This paper uses recent results on the representation of the GCD of many polynomials, in terms of factorisation of generalised resultants, to define the notion of “approximate GCD” and define the strength of any given approximation by solving an optimisation problem. The newly established framework is used to evaluate the performance of alternative procedures which have been used for defining approximate GCDs.     

Unsupervised learning algorithm for time series using bivariate AR(1) model

Currently, there is an increased interest in time series clustering research, particularly for finding useful similar time series in various applied areas such as speech recognition, environmental research, finance and medical imaging. Clustering and classification of time series has the potential to analyze large volumes of data. Most of the traditional time series clustering and classification algorithms deal only with univariate time series data. In this paper, we develop an unsupervised learning algorithm for bivariate time series. The initial clusters are found using K-means algorithm and the model parameters are estimated using the EM algorithm. The learning algorithm is developed by utilizing component maximum likelihood and Bayesian Information Criteria (BIC). The performance of the developed algorithm is evaluated using real time data collected from a pollution centre. A comparative study of the proposed algorithm is made with the existing data mining algorithm that uses univariate autoregressive process of order 1 (AR(1)) model. It is observed that the proposed algorithm out performs the existing algorithms.     

PDTDFB变换和双变量模型在图像去噪中的应用

对金字塔复方向滤波器组和贝叶斯最大后验估计理论架构下的双变量模型进行研究的基础上,结合二者的优点,提出一种新的图像去噪算法。PDTDFB（Pyramidal Dual-Tree Directional Filter Bank）变换具有近似时移不变性、多尺度、多方向选择性好的特点;双变量模型充分突出图像分解后系数的尺度内和尺度间的双重相关性;对噪声估计方法做出了详细阐述。仿真实验表明,与已有的多尺度理论（如：轮廓波等）和一些典型的图像去噪算法相比较,该算法的客观评价指标PSNR以及去噪后图像的主观视觉效果都有明显的提高和改善,能有效地保留原始图像的纹理和细节信息。     

System reliability using trivariate and bivariate integrals

Probabilistic safety analysis of civil engineering structures (system reliability analysis) requires the evaluation of multi-normal integrals and these multi-normal integrals can be estimated either from upper and lower bounds on these probabilities or by first-order second-moment approximate methods or by simulation methods. In this paper a new method based on conditional probabilities for the evaluation of series and parallel structural systems reliabilities is presented. Examples are studied to compare the accuracy of this new approach with other approximate methods.