Generating Functions for Computing the Myerson Value

The complexity of a computational problem is the order of computational resources which are necessary and sufficient to solve the problem. The algorithm complexity is the cost of a particular algorithm. We say that a problem has polynomial complexity if its computational complexity is a polynomial in the measure of input size. We introduce polynomial time algorithms based in generating functions for computing the Myerson value in weighted voting games restricted by a tree. Moreover, we apply the new generating algorithm for computing the Myerson value in the Council of Ministers of the European Union restricted by a communication structure.     

The Complexity of Computing the Hilbert Polynomial of Smooth Equidimensional Complex Projective Varieties

We continue the study of counting complexity begun in [13], [14], [15] by proving upper and lower bounds on the complexity of computing the Hilbert polynomial of a homogeneous ideal. We show that the problem of computing the Hilbert polynomial of a smooth equidimensional complex projective variety can be reduced in polynomial time to the problem of counting the number of complex common zeros of a finite set of multivariate polynomials. The reduction is based on a new formula for the coefficients of the Hilbert polynomial of a smooth variety. Moreover, we prove that the more general problem of computing the Hilbert polynomial of a homogeneous ideal is polynomial space hard. This implies polynomial space lower bounds for both the problems of computing the rank and the Euler characteristic of cohomology groups of coherent sheaves on projective space, improving the #P-lower bound in Bach [1].     

The Complexity of Factors of Multivariate Polynomials

The existence of string functions, which are not polynomial time computable, but whose graph is checkable in polynomial time, is a basic assumption in cryptography. We prove that in the framework of algebraic complexity, there are no such families of polynomial functions of polynomially bounded degree over fields of characteristic zero. The proof relies on a polynomial upper bound on the approximative complexity of a factor g of a polynomial f in terms of the (approximative) complexity of f and the degree of the factor g. This extends a result by Kaltofen. The concept of approximative complexity allows us to cope with the case that a factor has an exponential multiplicity, by using a perturbation argument. Our result extends to randomized (two-sided error) decision complexity.     

On the complexity of finding paths in a two‐dimensional domain I: Shortest paths

The computational complexity of finding a shortest path in a two‐dimensional domain is studied in the Turing machine‐based computational model and in the discrete complexity theory. This problem is studied with respect to two formulations of polynomial‐time computable two‐dimensional domains: (A) domains with polynomialtime computable boundaries, and (B) polynomial‐time recognizable domains with polynomial‐time computable distance functions. It is proved that the shortest path problem has the polynomial‐space upper bound for domains of both type (A) and type (B); and it has a polynomial‐space lower bound for the domains of type (B), and has a #P lower bound for the domains of type (A). (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The application of the Ehrlich-Aberth method to structured polynomial eigenvalue problems

An algorithm for the application of the Ehrlich-Aberth method to polynomial eigenvalue problems (PEPs) is proposed. The computational complexity of the algorithm is only quadratic with respect to the degree of the polynomial, where customary matrix methods have cubic complexity. For the case of some structured PEPs a strategy is given in order to exploit the structure and obtain the induced coupling in the spectrum. Numerical experiments are provided for the particular case of even/odd PEPs. This communication is based on joint work with Dario A. Bini and Luca Gemignani. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Spherical Minimax Location Problem

This paper presents an algorithm for determining a minimax location to service demand points that are equally weighted and distributed over a sphere. The norm under consideration is geodesic. The algorithm presented here is based on enumeration and has a polynomial time complexity.     

基于局部self-concordant障碍函数的全牛顿步多项式时间算法

由Nesterov和Nemirovski[4]创立的self-concordant障碍函数理论为解线性和凸优化问题提供了多项式时间内点算法.根据self-concordant障碍函数的参数,就可以分析内点算法的复杂性.在这篇文章中,我们介绍了基于核函数的局部self-concordant障碍函数,它在线性优化问题的中心路径及其邻域内满足self-concordant性质.通过求解此障碍函数的局部参数值,我们得到了求解线性规划问题的基于此局部Self-concordant障碍函数的纯牛顿步内点算法的理论迭代界.此迭代界与目前已知的最好的理论迭代界是一致的.     

基于自适应参数校正策略求解SDP的Mehrotra型内点算法

最近,Salahi对线性规划提出了一个基于新的自适应参数校正策略的Mehrotra型预估-校正算法,该策略使其在不使用安全策略的情况下,证明了算法的多项式迭代复杂界.本文将这一算法推广到半定规划的情形.通过利用Zhang的对称化技术,得到了算法的多项式迭代复杂界,这与求解线性规划的相应算法有相同的迭代复杂性阶.     

Solution of a stochastic Darcy equation by polynomial chaos expansion

This paper deals with solving a boundary value problem for the Darcy equation with a random hydraulic conductivity field.We use an approach based on polynomial chaos expansion in a probability space of input data.We use a probabilistic collocation method to calculate the coefficients of the polynomial chaos expansion. The computational complexity of this algorithm is determined by the order of the polynomial chaos expansion and the number of terms in the Karhunen–Loève expansion. We calculate various Eulerian and Lagrangian statistical characteristics of the flow by the conventional Monte Carlo and probabilistic collocation methods. Our calculations show a significant advantage of the probabilistic collocation method over the directMonte Carlo algorithm.     

Strongly polynomial algorithm for solving the general problem of least modules

An algorithm of polynomial algebraic complexity (a strongly polynomial algorithm) for solving the classical problem of mathematical programming on minimizing the weighted sum of modules of part of variables with linear constraints (equalities imposed on all variables) is substantiated. The algorithm is given in its explicit form. The complexity of the algorithm is estimated. A simulation is performed.     

About computation of the linear complexity of generalized cyclotomic sequences with period p n+1

We propose a computation method for linear complexity of series of generalized cyclotomic sequences with period p ⁿ⁺¹. This method is based on using the polynomial of the classic cyclotomic sequences of period p. We found the linear complexity of generalized cyclotomic sequences corresponding to the classes of biquadratic residues and Hall sequences.     

On the complexity of curve fitting algorithms

We study a popular algorithm for fitting polynomial curves to scattered data based on the least squares with gradient weights. We show that sometimes this algorithm admits a substantial reduction of complexity, and, furthermore, find precise conditions under which this is possible. It turns out that this is, indeed, possible when one fits circles but not ellipses or hyperbolas.     

A quadratically convergent polynomial long-step algorithm for A class of nonlinear monotone complementarity problems *

Several interior point algorithms have been proposed for solving nonlinear monotone complementarity problems. Some of them have polynomial worst-case complexity but have to confine to short steps, whereas some of the others can take long steps but no polynomial complexity is proven. This paper presents an algorithm which is both long-step and polynomial. In addition, the sequence generated by the algorithm, as well as the corresponding complementarity gap, converges quadratically. The proof of the polynomial complexity requires that the monotone mapping satisfies a scaled Lipschitz condition, while the quadratic rate of convergence is derived under the assumptions that the problem has a strictly complementary solution and that the Jacobian of the mapping satisfies certain regularity conditions     

On the complexity of computing the handicap of a sufficient matrix

The class of sufficient matrices is important in the study of the linear complementarity problem (LCP)—some interior point methods (IPM’s) for LCP’s with sufficient data matrices have complexity polynomial in the bit size of the matrix and its handicap.In this paper we show that the handicap of a sufficient matrix may be exponential in its bit size, implying that the known complexity bounds of interior point methods are not polynomial in the input size of the LCP problem. We also introduce a semidefinite programming based heuristic, that provides a finite upper bond on the handicap, for the sub-class of P{\mathcal{P}} -matrices (where all principal minors are positive).     

Applying sieving to the computation of quadratic class groups

We present a new algorithm for computing the ideal class group of an imaginary quadratic order which is based on the multiple polynomial version of the quadratic sieve factoring algorithm. Although no formal analysis is given, we conjecture that our algorithm has sub-exponential complexity, and computational experience shows that it is significantly faster in practice than existing algorithms.

     

The Computational Complexity of the Chow Form

We present a bounded probability algorithm for the computation of the Chowforms of the equidimensional components of an algebraic variety. In particular, this gives an alternative procedure for the effective equidimensional decomposition of the variety, since each equidimensional component is characterized by its Chow form. The expected complexity of the algorithm is polynomial in the size and the geometric degree of the input equation system defining the variety. Hence it improves (or meets in some special cases) the complexity of all previous algorithms for computing Chow forms. In addition to this, we clarify the probability and uniformity aspects, which constitutes a further contribution of the paper. The algorithm is based on elimination theory techniques, in line with the geometric resolution algorithm due to M. Giusti, J. Heintz, L. M. Pardo, and their collaborators. In fact, ours can be considered as an extension of their algorithm for zero-dimensional systems to the case of positive-dimensional varieties. The key element for dealing with positive-dimensional varieties is a new Poisson-type product formula. This formula allows us to compute the Chow form of an equidimensional variety from a suitable zero-dimensional fiber. As an application, we obtain an algorithm to compute a subclass of sparse resultants, whose complexity is polynomial in the dimension and the volume of the input set of exponents. As another application, we derive an algorithm for the computation of the (unique) solution of a generic overdetermined polynomial equation system.     

Polynomial time approximation schemes and parameterized complexity

In this paper, we study the relationship between the approximability and the parameterized complexity of NP optimization problems. We introduce a notion of polynomial fixed-parameter tractability and prove that, under a very general constraint, an NP optimization problem has a fully polynomial time approximation scheme if and only if the problem is polynomial fixed-parameter tractable. By enforcing a constraint of planarity on the W-hierarchy studied in parameterized complexity theory, we obtain a class of NP optimization problems, the planar W-hierarchy, and prove that all problems in this class have efficient polynomial time approximation schemes (EPTAS). The planar W-hierarchy seems to contain most of the known EPTAS problems, and is significantly different from the class introduced by Khanna and Motwani in their efforts in characterizing optimization problems with polynomial time approximation schemes.     

On factoring parametric multivariate polynomials

This paper presents a new algorithm for the absolute factorization of parametric multivariate polynomials over the field of rational numbers. This algorithm decomposes the parameters space into a finite number of constructible sets. The absolutely irreducible factors of the input parametric polynomial are given uniformly in each constructible set. The algorithm is based on a parametric version of Hensel's lemma and an algorithm for quantifier elimination in the theory of algebraically closed field in order to reduce the problem of finding absolute irreducible factors to that of representing solutions of zero-dimensional parametric polynomial systems. The complexity of this algorithm is single exponential in the number n of the variables of the input polynomial, its degree d w.r.t. these variables and the number r of the parameters.     

Polynomial reductions between the Limited Packing and Tuple Domination problems in graphs

The Limited Packing and Tuple Domination problems in graphs have closely-related definitions and the same computational complexity on several graph classes. In this work we present two polynomial reductions between these problems. Thus, by considering graph classes which are closed under these transformations, computational complexity results that are valid for one of the problems give rise to results for the other. The question concerning the existence of a class where one of the problems is polynomial and the other NP-complete is still open.     

P*(κ)线性互补问题的一个大步校正内点算法的迭代复杂性

本文基于一个带参数的函数,为P*(κ)线性互补问题设计出了一个大步校正内点算法.算法讨论沿用了Peng等在文[9]对互补问题基于自正则函数的讨论模式.但是,与Peng的算法不同的是,我们所考虑的带参数的函数是非自正则的.算法最终被证明具有较好的多项式复杂性.