二维格的覆盖半径

求格的覆盖半径是一个经典的困难问题,当格的维数不固定时,这个问题还没有非确定性的多项式时间的算法.已知的算法都是通过求Voronoi cell来计算覆盖半径,对于二维格,文章利用高斯算法给出了一个确定性的多项式时间的算法来求覆盖半径以及deep holes.     

基于基底合并的分片稀疏恢复

如我们所知,诸如视频和图像等信号可以在某些框架下被表示为稀疏信号,因此稀疏恢复(或稀疏表示)是信号处理、图像处理、计算机视觉、机器学习等领域中被广泛研究的问题之一.通常大多数在稀疏恢复中的有效快速算法都是基于求解$l^0$或者$l^1$优化问题.但是,对于求解$l^0$或者$l^1$优化问题以及相关算法所得到的理论充分性条件对信号的稀疏性要求过严.考虑到在很多实际应用中,信号是具有一定结构的,也即,信号的非零元素具有一定的分布特点.在本文中,我们研究分片稀疏恢复的唯一性条件和可行性条件.分片稀疏性是指一个稀疏信号由多个稀疏的子信号合并所得.相应的采样矩阵是由多个基底合并组成.考虑到采样矩阵的分块结构,我们引入了子矩阵的互相干性,由此可以得到相应$l^0$或者$l^1$优化问题可精确恢复解的稀疏度的新上界.本文结果表明.通过引入采样矩阵的分块结构信息.可以改进分片稀疏恢复的充分性条件.以及相应$l^0$或者$l^1$优化问题整体稀疏解的可靠性条件.     

高阶导数切触有理插值算法

已有关于高阶导数有理插值方法的研究大都是基于广义范德蒙逆矩阵的思想,计算复杂度较高.本文利用埃米特插值基函数的方法和多项式插值的误差性质,给出一种满足高阶导数插值条件的切触有理插值算法,并且适用于向量值切触有理插值及插值重度不相等的情形,解决切触有理插值函数的存在性及算法复杂性问题.较之其他算法,具有计算复杂度较低,便于实际应用等特点.最后通过数值例子说明该算法的有效性.     

一类半正定多项式的配平方和算法

以牛顿多胞型技术为基础,根据牛顿多胞型中的点与点之间的相关性,给出了直接搜索多项式配平方和所需的最基本的项集Xs的算法,利用精确的符号算法PCAD,可将一类半正定多项式配成平方和,并编写了Maple程序"ASSOS",实现了多项式配平方和的自动生成.由多项式结构的稀疏性,此算法更能有效处理稀疏多项式.这一算法提高了多项式配平方和的效率,从而促进了一类代数不等式可读性证明的自动生成.除此之外,还给出了多项式不能表示为平方和的一个充分条件.     

赋权树状网络中r-控制集问题和k-中心问题

图G=(V, E;f,ω)是顶点和边都赋权的树,f:V→R+,ω:E→R+.本文给出了顶点u与v之间距离的一种新的定义.在顶点和边都赋权的树中,研究在新距离条件下的r-控制集问题与k-中心问题.对于r-控制集问题,设计出了复杂性为Ο(n)的多项式时间算法;对于k-中心问题,设计出了Ο(n2log n)的多项式时间算法.     

关于微分多项式的亏量

本文研究了微分多项式的值分布问题,得出了关于微分多项式的亏量的几个结果,改进了C.C.Yang,W.K.Hayman,L.R.Sons and W.Doeringer等人的有关定理,还用例子证明了本文定理的结果是最佳的。     

一个约束乘积最大问题的强多项式时间算法

本文讨论了约束乘积最大问题最优解的结构特征,在此基础上给出了一个计算时间为O(n2)的强多项式时间算法,并且对于单边约束的情形给出了复杂度更低(O(nlnn))的强多项式时间算法.     

基于GMRES的多项式预处理广义极小残差法

求解大型稀疏线性方程组一般采用迭代法,其中GMRES(m)算法是一种非常有效的算法,然而该算法在解方程组时,可能发生停滞．为了克服算法GMRES(m)解线性系统Ax=b过程中可能出现的收敛缓慢或不收敛,本文利用GMRES本身构造出一种有效的多项式预处理因子pk(z),该多项式预处理因子非常简单且易于实现．数值试验表明,新算法POLYGMRES(m)较好地克服了GMRES(m)的缺陷．     

一类LFSR序列簇的2-adic复杂度

线性复杂度和2-adic复杂度是衡量序列伪随机性的两个重要指标.文章研究这两个指标之间的关系,证明了由不可约多项式生成的LFSR序列簇的极小连接数达到最大可能值,即2~T-1,其中T为不可约多项式的周期,进而该序列簇的2-adic复杂度与对称2-adic复杂度均达到最大可能取值.特别地,当限定不可约多项式是本原多项式时,即可得到m-序列的相应结论.     

线性比式和优化问题的完全多项式时间近似算法

本文针对线性比式和优化问题提出一个完全多项式时间近似算法,该算法主要利用原问题的等价问题及网格结点参数获得有限个与结点参数相关的线性规划问题,通过求解这些线性规划问题获得原问题的近似最优解.最终证明算法的收敛性,并给出了算法的计算复杂度,通过计算结果呈现算法的有效性与可行性.     

A lifting and recombination algorithm for rational factorization of sparse polynomials

We propose a new lifting and recombination scheme for rational bivariate polynomial factorization that takes advantage of the Newton polytope geometry. We obtain a deterministic algorithm that can be seen as a sparse version of an algorithm of Lecerf, with a polynomial complexity in the volume of the Newton polytope. We adopt a geometrical point of view, the main tool being derived from some algebraic osculation criterion in toric varieties.     

Homogeneous Self-dual Algorithms for Stochastic Semidefinite Programming

Ariyawansa and Zhu have proposed a new class of optimization problems termed stochastic semidefinite programs to handle data uncertainty in applications leading to (deterministic) semidefinite programs. For stochastic semidefinite programs with finite event space, they have also derived a class of volumetric barrier decomposition algorithms, and proved polynomial complexity of certain members of the class. In this paper, we consider homogeneous self-dual algorithms for stochastic semidefinite programs with finite event space. We show how the structure in such problems may be exploited so that the algorithms developed in this paper have complexity similar to those of the decomposition algorithms mentioned above.     

On solving sparse algebraic equations over finite fields

A system of algebraic equations over a finite field is called sparse if each equation depends on a small number of variables. In this paper new deterministic algorithms for solving such equations are presented. The mathematical expectation of their running time is estimated. These estimates are at present the best theoretical bounds on the complexity of solving average instances of the above problem. In characteristic 2 the estimates are significantly lower the worst case bounds provided by SAT solvers.     

Improved algorithms for computing determinants and resultants

Our first contribution is a substantial acceleration of randomized computation of scalar, univariate, and multivariate matrix determinants, in terms of the output-sensitive bit operation complexity bounds, including computation modulo a product of random primes from a fixed range. This acceleration is dramatic in a critical application, namely solving polynomial systems and related studies, via computing the resultant. This is achieved by combining our techniques with the primitive-element method, which leads to an effective implicit representation of the roots. We systematically examine quotient formulae of Sylvester-type resultant matrices, including matrix polynomials and the u-resultant. We reduce the known bit operation complexity bounds by almost an order of magnitude, in terms of the resultant matrix dimension. Our theoretical and practical improvements cover the highly important cases of sparse and degenerate systems.     

Recognizing graphs without asteroidal triples

We consider the problem of recognizing AT-free graphs. Although there is a simple O(n³) algorithm, no faster method for solving this problem had been known. Here we give three different algorithms which have a better time complexity for graphs which are sparse or have a sparse complement; in particular we give algorithms which recognize AT-free graphs in , , and O(n^2.82+nm). In addition we give a new characterization of graphs with bounded asteroidal number by the help of the knotting graph, a combinatorial structure which was introduced by Gallai for considering comparability graphs.     

A Sparse Singular Value Decomposition Method for High-Dimensional Data

We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations that produce the singular vectors simultaneously, rather than successively as in competing proposals. We introduce novel ways to estimate thresholding parameters, which obviate the need for computationally expensive cross-validation. We also introduce a way to sparsely initialize the algorithm for computational savings that allow our algorithm to outperform the vanilla singular value decomposition (SVD) on the full data table when the signal is sparse. A comparison with two existing sparse SVD methods suggests that our algorithm is computationally always faster and statistically always at least comparable to the better of the two competing algorithms. Supplementary materials for the article are available in an online appendix. An R package ssvd implementing the algorithms introduced in this article is available on CRAN.     

A black-box rational Arnoldi variant for Cauchy–Stieltjes matrix functions

Rational Arnoldi is a powerful method for approximating functions of large sparse matrices times a vector. The selection of asymptotically optimal parameters for this method is crucial for its fast convergence. We present and investigate a novel strategy for the automated parameter selection when the function to be approximated is of Cauchy–Stieltjes (or Markov) type, such as the matrix square root or the logarithm. The performance of this approach is demonstrated by numerical examples involving symmetric and nonsymmetric matrices. These examples suggest that our black-box method performs at least as well, and typically better, as the standard rational Arnoldi method with parameters being manually optimized for a given matrix.     

On the choice of a multiplication algorithm for polynomials and polynomial matrices

We investigate multiplication algorithms for dense and sparse polynomials and polynomial matrices over different numerical domains and obtain expressions for the complexity of multiplication of polynomials and polynomial matrices understood as the expectation of the number of arithmetic operations. These expressions for a set of parameters of practical interest are tabulated. The results of experiments with the corresponding programs are presented. Bibliography: 8 titles.     

Time complexity of single machine scheduling with stochastic precedence constraints

This paper deals with single machine scheduling problems with stochastic precedence relations (so calledGERT networks). Until now most investigations on such problems, dealt with algorithms running in polynomial time. On the other hand, for scheduling problems with deterministic precedence relations exist a lot of results about time complexity. Therefore, the object of this paper is to consider time complexity of scheduling problems with stochastic precedence constraints and to describe the boundary between theNP-hard problems and those which can be solved in polynomial time.     

A Gröbner Free Alternative for Polynomial System Solving

Given a system of polynomial equations and inequations with coefficients in the field of rational numbers, we show how to compute a geometric resolution of the set of common roots of the system over the field of complex numbers. A geometric resolution consists of a primitive element of the algebraic extension defined by the set of roots, its minimal polynomial, and the parametrizations of the coordinates. Such a representation of the solutions has a long history which goes back to Leopold Kronecker and has been revisited many times in computer algebra. We introduce a new generation of probabilistic algorithms where all the computations use only univariate or bivariate polynomials. We give a new codification of the set of solutions of a positive dimensional algebraic variety relying on a new global version of Newton's iterator. Roughly speaking the complexity of our algorithm is polynomial in some kind of degree of the system, in its height, and linear in the complexity of evaluation of the system. We present our implementation in the Magma system which is called Kronecker in homage to his method for solving systems of polynomial equations. We show that the theoretical complexity of our algorithm is well reflected in practice and we exhibit some cases for which our program is more efficient than the other available software.