Towards toric absolute factorization

This article presents an algorithmic approach to study and compute the absolute factorization of a bivariate polynomial, taking into account the geometry of its monomials. It is based on algebraic criterions inherited from algebraic interpolation and toric geometry.     

Improved dense multivariate polynomial factorization algorithms

We present new deterministic and probabilistic algorithms that reduce the factorization of dense polynomials from several variables to one variable. The deterministic algorithm runs in sub-quadratic time in the dense size of the input polynomial, and the probabilistic algorithm is softly optimal when the number of variables is at least three. We also investigate the reduction from several to two variables and improve the quantitative version of Bertini’s irreducibility theorem.     

Performance of parallel Cholesky factorization algorithms using BLAS

This paper considers four parallel Cholesky factorization algorithms, including SPOTRF from the February 1992 release of LAPACK, each of which call parallel Level 2 or 3 BLAS, or both. A fifth parallel Cholesky algorithm that calls serial Level 3 BLAS is also described. The efficiency of these five algorithms on the CRAY-2, CRAY Y-MP/832, Hitachi Data Systems EX 80, and IBM 3090-600J is evaluated and compared with a vendor-optimized parallel Cholesky factorization algorithm. The fifth parallel Cholesky algorithm that calls serial Level 3 BLAS provided the best performance of all algorithms that called BLAS routines. In fact, this algorithm outperformed the Cray-optimized libsci routine (SPOTRF) by 13–44%;, depending on the problem size and the number of processors used.This work was supported by grants from IMSL, Inc., and Hitachi Data Systems. The first version of this paper was presented as a poster session at Supercomputing '90, New York City, November 1990.     

About the Newton iteration for spectral factorization

It is shown, that Theorem 1.2 of Willems (Operators, Systems and Linear Algebra (Kaiserslautern, 1997), European Consort. Math. Indust., Jeubner, Stuggart, 1997, pp. 214–223), is analog of the outcomes of the statement 5 (Aliev and Lorin, Systems Contr. Lett. 21 (1993) 485–491).     

一种适于硬件实现的快速模乘算法

RSA算法是目前应用最广泛的一种公钥加密算法,随着人们对加密安全性和加密速度要求的提高,硬件实现加密算法成了密码学应用的一个趋势。模乘算法是模幂算法的核心,基于Montgomery算法,结合Booth2算法的思想,文章给出了一种改进的高效算法,并且通过FPGA实现。对该算法和参考文献中算法的性能进行了比较,可以看出这一改进算法在速度和面积上优于现有的算法。     

A note on Gao’s algorithm for polynomial factorization

Shuhong Gao (2003) [6] has proposed an efficient algorithm to factor a bivariate polynomial f over a field F. This algorithm is based on a simple partial differential equation and depends on a crucial fact: the dimension of the polynomial solution space G associated with this differential equation is equal to the number r of absolutely irreducible factors of f. However, this holds only when the characteristic of F is either zero or sufficiently large in terms of the degree of f. In this paper we characterize a vector subspace of G for which the dimension is r, regardless of the characteristic of F, and the properties of Gao’s construction hold. Moreover, we identify a second vector subspace of G that leads to an analogous theory for the rational factorization of f.     

A Toeplitz algorithm for polynomial J-spectral factorization

A block Toeplitz algorithm is proposed to perform the J-spectral factorization of a para-Hermitian polynomial matrix. The input matrix can be singular or indefinite, and it can have zeros along the imaginary axis. The key assumption is that the finite zeros of the input polynomial matrix are given as input data. The algorithm is based on numerically reliable operations only, namely computation of the null-spaces of related block Toeplitz matrices, polynomial matrix factor extraction and linear polynomial matrix equations solving.     

Single-factor lifting and factorization of polynomials over local fields

Let f(x)$f\left(x\right)$ be a separable polynomial over a local field. The Montes algorithm computes certain approximations to the different irreducible factors of f(x)$f\left(x\right)$, with strong arithmetic properties. In this paper, we develop an algorithm to improve any one of these approximations, till a prescribed precision is attained. The most natural application of this “single-factor lifting” routine is to combine it with the Montes algorithm to provide a fast polynomial factorization algorithm. Moreover, the single-factor lifting algorithm may be applied as well to accelerate the computational resolution of several global arithmetic problems in which the improvement of an approximation to a single local irreducible factor of a polynomial is required.     

矩阵的Doolittle递归分解算法及符号程序设计

将矩阵An×n的Doolittle分解推广到Am×n上，并在常规的迭代算法上加以创新，给出了递归的分解算法．在实现算法的过程中，对数据进行了巧妙处理，使中间数据及最终计算结果都具有分数形式，提高了结果的精确度，而且更符合人们阅读的习惯．经过运行测试，算法设计合理，程序运行高效准确．程序是对MathSoft公司的交互式的数学文字软件Mathcad的矩阵分解的数值计算扩充到符号运算．     

Parallel methods for absolute irreducibility testing

A heuristic algorithm for testing absolute irreducibility of multivariate polynomials over arbitrary fields using Newton polytopes was proposed in Gao and Lauder (Discrete Comput. Geom. 26:89–104, [2001]). A preliminary implementation by Gao and Lauder (2003) established a wide range of families of low degree and sparse polynomials for which the algorithm works efficiently and with a high success rate. In this paper, we develop a BSP variant of the absolute irreducibility testing via polytopes, with the aim of producing a more memory and run-time efficient method that can provide wider ranges of applicability, specifically in terms of the degrees of the input polynomials. In the bivariate case, we describe a balanced load scheme and a corresponding data distribution leading to a parallel algorithm whose efficiency can be established under reasonably realistic conditions. This is later incorporated in a doubly parallel algorithm in the multivariate case that achieves similar scalable performance. Both parallel models are analyzed for efficiency, and the theoretical analysis is compared to the performance of our experiments. In the empirical results we report, we achieve absolute irreducibility testing for bivariate and trivariate polynomials of degrees up to 30,000, and for low degree multivariate polynomials with more than 3,000 variables. To the best of our knowledge, this sets a world record in establishing absolute irreducibility of multivariate polynomials.     

Optimal and nearly optimal algorithms for approximating polynomial zeros

We substantially improve the known algorithms for approximating all the complex zeros of an n^th degree polynomial p(x). Our new algorithms save both Boolean and arithmetic sequential time, versus the previous best algorithms of Schönhage [1], Pan [2], and Neff and Reif [3]. In parallel (NC) implementation, we dramatically decrease the number of processors, versus the parallel algorithm of Neff [4], which was the only NC algorithm known for this problem so far. Specifically, under the simple normalization assumption that the variable x has been scaled so as to confine the zeros of p(x) to the unit disc x : |x| ≤ 1, our algorithms (which promise to be practically effective) approximate all the zeros of p(x) within the absolute error bound 2^−b, by using order of n arithmetic operations and order of (b + n)n² Boolean (bitwise) operations (in both cases up to within polylogarithmic factors). The algorithms allow their optimal (work preserving) NC parallelization, so that they can be implemented by using polylogarithmic time and the orders of n arithmetic processors or (b + n)n² Boolean processors. All the cited bounds on the computational complexity are within polylogarithmic factors from the optimum (in terms of n and b) under both arithmetic and Boolean models of computation (in the Boolean case, under the additional (realistic) assumption that n = O(b)).     

基于ranking的深度张量分解群组推荐算法

针对当前群组推荐研究中,对于用户偏好建模时大多忽略了群组偏好与个人偏好之间的相互影响以及建模初始化问题,提出了一种基于ranking的混合深度张量分解群组推荐算法（R-HDTF）。该算法首先利用基于深度降噪自动编码器的混合神经网络对群组、个人和项目等信息进行初始化;然后提出基于成对张量分解模型来捕获群组、个人和项目之间的相关关系;最后,采用BPR标准优化张量分解的损失函数,学习提出算法的参数。在真实数据集上的实验结果表明,该算法性能优于传统的主流群组推荐算法。     

Faster algorithms for computing Hong’s bound on absolute positiveness

We show how to compute Hong’s bound for the absolute positiveness of a polynomial in d$d$ variables with maximum degree δ$\delta$ in O(nlog^dn)$O\left(n{log}^{d}n\right)$ time, where n$n$ is the number of non-zero coefficients. For the univariate case, we give a linear time algorithm. As a consequence, the time bounds for the continued fraction algorithm for real root isolation improve by a factor of δ$\delta$.     

A sparse nonnegative matrix factorization technique for graph matching problems

Graph matching problem that incorporates pairwise constraints can be cast as an Integer Quadratic Programming (IQP). Since it is NP-hard, approximate methods are required. In this paper, a new approximate method based on nonnegative matrix factorization with sparse constraints is presented. Firstly, the graph matching is formulated as an optimization problem with nonnegative and sparse constraints, followed by an efficient algorithm to solve this constrained problem. Then, we show the strong relationship between the sparsity of the relaxation solution and its effectiveness for graph matching based on our model. A key benefit of our method is that the solution is sparse and thus can approximately impose the one-to-one mapping constraints in the optimization process naturally. Therefore, our method can approximate the original IQP problem more closely than other approximate methods. Extensive and comparative experimental results on both synthetic and real-world data demonstrate the effectiveness of our graph matching method.     

Structure preserving non-negative matrix factorization for dimensionality reduction

The problem of dimensionality reduction is to map data from high dimensional spaces to low dimensional spaces. In the process of dimensionality reduction, the data structure, which is helpful to discover the latent semantics and simultaneously respect the intrinsic geometric structure, should be preserved. In this paper, to discover a low-dimensional embedding space with the nature of structure preservation and basis compactness, we propose a novel dimensionality reduction algorithm, called Structure Preserving Non-negative Matrix Factorization (SPNMF). In SPNMF, three kinds of constraints, namely local affinity, distant repulsion, and embedding basis redundancy elimination, are incorporated into the NMF framework. SPNMF is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm. The convergence of the proposed update solutions is proved. Extensive experiments on both synthetic data and six real world data sets demonstrate the encouraging performance of the proposed algorithm in comparison to the state-of-the-art algorithms, especially some related works based on NMF. Moreover, the convergence of the proposed updating rules is experimentally validated.     

RSA高速模乘单元的设计

论文分析了Montgomery算法,利用迭代加法之间的并行性提出了一种流水并行工作的硬件模乘结构。该结构具有时钟频率高,模幂运算时间短的优点,适合于RSA的模幂运算,可以极大提高RSA加密运算的效率,同时其体系结构适合于高阶Montgomery算法的实现。FPGA实现的结果表明,512位的高速模乘单元工作频率74.27MHZ;1024位的高速模乘单元工作频率73.94MHZ。模乘单元的面积与位宽成正比,而工作频率基本不变。基于此结构,512位的RSA运算时间为1.78ms,1024位的RSA运算时间为7.08ms。     

广义行（列）对称矩阵的QR分解及其算法

对广义行（列）对称矩阵的QR分解和性质进行了研究,给出了广义行（列）对称矩阵的QR分解的公式和快速算法,它们可有效减少广义行（列）对称矩阵的QR分解的计算量与存储量,并且不会丧失数值精度。同时讨论了系统参数估计,推广和丰富了两文（邹红星,王殿军,戴琼海,等.行(或列)对称矩阵的QR分解.中国科学：A辑,2002,32(9):842-849;蔺小林,蒋耀林.酉对称矩阵的QR分解及其算法.计算机学报,2005,28(5):817-822）的研究内容,拓宽了实际应用领域的范围, 并修正了后者的错误。     

The <Emphasis Type="Italic">p</Emphasis>-recursive piecewise polynomial sigmoid generators and first-order algorithms for multilayer tanh-like neurons

This paper demonstrates how the p-recursive piecewise polynomial (p-RPP) generators and their derivatives are constructed. The feedforward computational time of a multilayer feedforward network can be reduced by using these functions as the activation functions. Three modifications of training algorithms are proposed. First, we use the modified error function so that the sigmoid prime factor for the updating rule of the output units is eliminated. Second, we normalize the input patterns in order to balance the dynamic range of the inputs. And third, we add a new penalty function to the hidden layer to get the anti-Hebbian rules in providing information when the activation functions have zero sigmoid prime factor. The three modifications are combined with two versions of Rprop (Resilient propagation) algorithm. The proposed procedures achieved excellent results without the need for careful selection of the training parameters. Not only the algorithm but also the shape of the activation function has important influence on the training performance.