Euclid算法、Guass消元法以及Buchberger算法研究[英文]

本文给出计算多个正整数的最大公因子的算法,该算法是Euclid算法的推广,基于该算法可再次发现Guass消元法,而且不必使用多元除算法来简化Buchberger算法.     

最小连通顶点覆盖问题的降阶回溯算法

本文从最小连通顶点覆盖问题的求解算法出发,提出一种基于该问题本身的数学性质的降阶回溯算法来求解。通过基于问题的数学性质来设计精确算法,不仅能够克服使用启发式算法求解该问题在一般情形下都无法求得最优解的缺点,也改善了该问题使用传统精确算法时最坏时间复杂度高的缺点。本文首先研究该问题的数学性质,部分数学性质可成批确定某些顶点在或不在最小连通顶点覆盖集中,从而降低该问题的规模,提高精确算法的求解速度。其次,在数学性质的基础上,设计出上下界子算法、降阶子算法、回溯子算法来求解该问题的最优解。最后,时间复杂度分析以及无线网络设计的实例分析表明,该算法不仅能求得该问题的最优解,且相对一般精确算法,本文算法的时间复杂度更低。     

协作供应条件下成本优化模型及其算法

在保证供应不间断的前提下,讨论了多供应点、多时间需求的协作供应问题,建立了使包括运输成本、购货成本以及库存成本在内的总成本最小的数学模型.设计了一个求解该模型的算法,证明了该算法的可行性和最优性,并给出了该算法的算法复杂度.最后给出了一个算例,和相关算法相比较可知该算法更具有实际应用价值.     

基于乌鸦喝水的元启发式优化算法

启发式优化算法已成为求解复杂优化问题的一种有效方法,可用于解决传统的优化方法难以求解的问题.受乌鸦喝水寓言故事启发,提出一种新型元启发式优化算法—乌鸦喝水算法,首先建立了乌鸦喝水算法数学模型;其次,给出实现该算法的详细步骤;最后,将该算法用于基准函数优化,并将该算法与乌鸦搜索算法、粒子群优化算法、多元宇宙优化算法、花授...     

一类网络最优路径的双向模拟扩散算法

基于模拟扩散算法的基本原理,文中提出了一种双向寻求网络最优路径的扩散算法,并介绍了该算法原理和具体计算过程,验证了该算法的正确性和合理性。该算法具有并行计算的能力,适合于分布式计算机,寻求大型复杂网络的最优路径。     

求解Sylvester矩阵方程的一种改进的梯度方法

提出了一种改进的梯度迭代算法来求解Sylvester矩阵方程和Lyapunov矩阵方程.该梯度算法是通过构造一种特殊的矩阵分裂,综合利用Jaucobi迭代算法和梯度迭代算法的求解思路.与已知的梯度算法相比,提高了算法的迭代效率.同时研究了该算法在满足初始条件下的收敛性.数值算例验证了该算法的有效性.     

一元非线性系统的BP算法研究

详细研究了一种一元非线性系统的BP算法,提出并证明了该算法的收敛性定理,给出了该算法的应用实例.计算机仿真结果表明:对于随机给定的初始点,该算法都能稳定收敛到它的一个实根,而且计算精度可控,因此,该算法是有效的.与传统的计算方法相比,本文算法不仅具有收敛速度快,而且计算精度可控以及初始点随机给定的集中优点.     

解P_0非线性互补问题的光滑Levenberg-Marquardt方法

本文研究了一个P0非线性互补问题.利用信赖域技术获得了求解该问题的光滑Levenberg-Marquardt算法,该算法在一定条件下具有全局性.利用局部误差界还获得了该算法的超线性和二次收敛.数值结果表明该算法是有效的.     

r-循环线性系统求解的快速算法

本文给出r-循环线性系统求解的一种快速算法.当r-循环矩阵非奇异时,该快速算法求出该线性系统的唯一解;当r-循环矩阵奇异时,该快速算法求出该线性系统的通解.     

一类MPEC问题的SQP算法

本文研究带线性互补约束规划问题的 SQP算法 .该算法不要求精确计算初值 ,是针对初值非精确计算情形的新算法 ,证明了该算法的收敛性 .     

A descent algorithm for nonsmooth convex optimization

This paper presents a new descent algorithm for minimizing a convex function which is not necessarily differentiable. The algorithm can be implemented and may be considered a modification of the ε-subgradient algorithm and Lemarechal's descent algorithm. Also our algorithm is seen to be closely related to the proximal point algorithm applied to convex minimization problems. A convergence theorem for the algorithm is established under the assumption that the objective function is bounded from below. Limited computational experience with the algorithm is also reported.     

一种新的双模式盲均衡算法研究

针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率.     

A rank-one algorithm for unconstrained function minimization

A rank-one algorithm is presented for unconstrained function minimization. The algorithm is a modified version of Davidon's variance algorithm and incorporates a limited line search. It is shown that the algorithm is a descent algorithm; for quadratic forms, it exhibits finite convergence, in certain cases. Numerical studies indicate that it is considerably superior to both the Davidon-Fletcher-Powell algorithm and the conjugate-gradient algorithm.     

A fraction free Matrix Berlekamp/Massey algorithm

We describe a fraction free version of the Matrix Berlekamp/Massey algorithm. The algorithm computes a minimal matrix generator of linearly generated square matrix sequences in an integral domain. The algorithm performs all operations in the integral domain, so all divisions performed are exact. For scalar sequences, the matrix algorithm specializes to a different algorithm than the algorithm currently in the literature. This new scalar algorithm has smaller intermediate values than the known fraction free Berlekamp/Massey algorithm.     

含有等式约束非线性规划的全局优化算法

针对含有多个等式约束的非线性规划问题,提出一个全局优化算法.该方法基于可行集策略把改进的模拟退火方法与确定的局部算法方法相结合.对算法的收敛性进行了证明,数值结果表明算法的有效性及正确性.     

Long-Step Primal Path-Following Algorithm for Monotone Variational Inequality Problems

In this paper, we present a long-step primal path-following algorithm and prove its global convergence under usual assumptions. It is seen that the short-step algorithm is a special case of the long-step algorithm for a specific selection of the parameters and the initial solution. Our theoretical result indicates that the long-step algorithm is more flexible. Numerical results indicate that the long-step algorithm converges faster than the short-step algorithm.     

A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. This work was supported by the University of Colorado at Colorado Springs.     

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Mehrotra’s algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra’s algorithm. This paper describes a proposal and implementation of an alternative algorithm, an arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by the arc-search infeasible interior-point algorithm and Mehrotra’s algorithm, that the proposed arc-search infeasible interior-point algorithm is a more reliable and efficient algorithm than Mehrotra’s algorithm.     

Nonlinear Smoothing and the EM Algorithm for Positive Integral Equations of the First Kind

We study a modification of the EMS algorithm in which each step of the EMS algorithm is preceded by a nonlinear smoothing step of the form , where S is the smoothing operator of the EMS algorithm. In the context of positive integral equations (à la positron emission tomography) the resulting algorithm is related to a convex minimization problem which always admits a unique smooth solution, in contrast to the unmodified maximum likelihood setup. The new algorithm has slightly stronger monotonicity properties than the original EM algorithm. This suggests that the modified EMS algorithm is actually an EM algorithm for the modified problem. The existence of a smooth solution to the modified maximum likelihood problem and the monotonicity together imply the strong convergence of the new algorithm. We also present some simulation results for the integral equation of stereology, which suggests that the new algorithm behaves roughly like the EMS algorithm. Accepted 1 April 1997     

Hybrid Flow-Shop: a Memetic Algorithm Using Constraint-Based Scheduling for Efficient Search

The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound algorithm to be employed as the local search engine of the memetic algorithm. Next, we present the new memetic algorithm. We lastly explain the computational experiments carried out to evaluate the performance of three algorithms (genetic algorithm, constraint programming based branch-and-bound algorithm, and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better quality solutions and that it is very efficient.