This paper gives various (positive and negative) results on the complexity of the problem of computing and approximating mixed volumes of polytopes and more general convex bodies in arbitrary dimension. On the negativ...
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This paper gives various (positive and negative) results on the complexity of the problem of computing and approximating mixed volumes of polytopes and more general convex bodies in arbitrary dimension. On the negative side, we present several # P-hardness results that focus on the difference of computing mixed volumes versus computing the volume of polytopes. We show that computing the volume of zonotopes is # P-hard (while each corresponding mixed volume can be computed easily) but also give examples showing that computing mixed volumes is hard even when computing the volume is easy. On the positive side, we derive a randomized algorithm for computing the mixed volumes [GRAPHICS] of well-presented convex bodies K(1),...,K(s), where m(1),...,m(s) is an element of N(0) and m(1) greater than or equal to n - psi(n) with psi(n) = o(log n/log log n). The algorithm is an interpolation method based on polynomial-time randomized log algorithms for computing the volume of convex bodies. This paper concludes with applications of our results to various problems in discrete mathematics, combinatorics, computational convexity, algebraic geometry, geometry of numbers, and operations research.
We provide a new algorithm (called the grid algorithm) designed to generate the image of the attractor of a generalized iterated function system on a finite dimensional space and we compare it with the deterministic a...
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We provide a new algorithm (called the grid algorithm) designed to generate the image of the attractor of a generalized iterated function system on a finite dimensional space and we compare it with the deterministic algorithm regarding generalized iterated function systems presented by Jaros et al. (Numer. algorithms 73, 477-499, 2016).
This article present a branch and bound algorithm for globally solving generalized linear multiplicative programming problems with coefficients. The main computation involve solving a sequence of linear relaxation pro...
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This article present a branch and bound algorithm for globally solving generalized linear multiplicative programming problems with coefficients. The main computation involve solving a sequence of linear relaxation programming problems, and the algorithm economizes the required computations by conducting the branch and bound search in R-p, rather than in R-n, where p is the number of rank and n is the dimension of decision variables. The proposed algorithm will be convergent to the global optimal solution by means of the subsequent solutions of the series of linear relaxation programming problems. Numerical results are given to show the feasibility and effectiveness of the proposed algorithm.
Motivated by some algorithmic problems, we give lower bounds on the size of the multiplicative groups containing rational function images of low-dimensional affine subspaces of a finite field F-qn considered as a line...
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Motivated by some algorithmic problems, we give lower bounds on the size of the multiplicative groups containing rational function images of low-dimensional affine subspaces of a finite field F-qn considered as a linear space over a subfield F-q. We apply this to the recently introduced algorithmic problem of identity testing of "hidden" polynomials f and g over a high degree extension of a finite field, given oracle access to f(x)(e) and g(x)(e).
In this comment on the discussion paper The power of monitoring: how to make the most of a contaminated multivariate sample by A. Cerioli, M. Riani, A. Atkinson and A. Corbellini, we describe how the hard rejection pr...
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In this comment on the discussion paper The power of monitoring: how to make the most of a contaminated multivariate sample by A. Cerioli, M. Riani, A. Atkinson and A. Corbellini, we describe how the hard rejection property of the MCD method can be mimicked by an S-estimator with appropriate rho-function. We also point the reader to fast and deterministic algorithms for the MCD, S- and MM-estimators that are specifically suited for monitoring experiments. They were made available a few years ago and successfully used for monitoring in our papers. Finally, the question is raised how monitoring can be applied or extended for increasing numbers of cases, variables and tuning parameters.
A deterministic algorithm for calculating the roots of polynomials in one variable with coefficients in the ring of polynomials in several variables over an arbitrary integral domain is constructed. An estimate for th...
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A deterministic algorithm for calculating the roots of polynomials in one variable with coefficients in the ring of polynomials in several variables over an arbitrary integral domain is constructed. An estimate for the arithmetic complexity of the algorithm in the worst case is obtained.
Let is an element of > 0. In this article we will present a deterministic algorithm which does the following. The input is a hyperelliptic curve C of genus g over a finite field k of cardinality q given by y(2) + h...
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Let is an element of > 0. In this article we will present a deterministic algorithm which does the following. The input is a hyperelliptic curve C of genus g over a finite field k of cardinality q given by y(2) + h(x)y = f (x) such that the x-coordinate map is ramified at infinity. In time 0(g(2+is an element of) q(1/2+is an element of)) the algorithm outputs a set of generators of the Picard group Pic(k)(0)(C). This extends results which others have obtained when g = 1. In this article we introduce a combinatorial tool, the shape parameter, which we use together with character sum estimates from class field theory to deduce the statement. (C) 2015 Elsevier Inc. All rights reserved.
In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a b...
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In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet, Sigma. A motif G = g(1)g(2) center dot center dot center dot g(m) is a string of m characters. In each background sequence is implanted a probabilistically-generated approximate copy of G. For a probabilistically-generated approximate copy b(1)b(2) center dot center dot center dot b(m) of G, every character, b(i), is probabilistically generated, such that the probability for b(i) not equal = g(i) is at most alpha. We develop two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is a lower bound of four. This gives them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other software.
A deterministic method is proposed for the computation of zeros within a complex plane by using Cauchy's theorem. The integrals have been calculated for different numbers of integration points by the Gaussian inte...
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A deterministic method is proposed for the computation of zeros within a complex plane by using Cauchy's theorem. The integrals have been calculated for different numbers of integration points by the Gaussian integration formula. As an example, several eigenmodes of a TE/TM mode splitter have been determined by the proposed method.
Heat exchanger network synthesis (HENS) has been one of the most extensively studied synthesis problem in chemical engineering. The methodologies commonly used to solve a heat exchanger network (HEN) problem are based...
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Heat exchanger network synthesis (HENS) has been one of the most extensively studied synthesis problem in chemical engineering. The methodologies commonly used to solve a heat exchanger network (HEN) problem are based on heuristic methods instead of deterministic methods which, theoretically, only with feasible initial HEN configuration enumeration, can find global optima. But enumeration always leads to unacceptable computational time. This paper presents a principle of process stream arrangement and the related deterministic algorithm for synthesizing HEN. The implementation of the principle based on the uniformity factor which proposed to describe the degree of the energy distribution of HEN configuration under different process stream arrangement is aimed to decrease the space of configuration enumeration. Thus, through the part of smaller uniformity factor network structure are optimized, deterministic methods are effectively suitable for medium-size problem with global optima in a reasonable brief computational time. Notably, using examples of 8SP1, 10SP, 10SP1, 14SP1, we demonstrate that our approach finds superior networks compared to those known in the literature. (C) 2013 Published by Elsevier Ltd.
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