基于Nie-Tan算法的区间二型模糊集质心计算:改变主变量采样个数

计算区间二型模糊集的质心(也称降型)是区间二型模糊逻辑系统中的一个重要模块。Karnik-Mendel(KM)迭代算法通常被认为是计算区间二型模糊集质心的标准算法。尽管如此,KM算法涉及复杂的计算过程,不利于实时应用。在各种改进类算法中,非迭代的Nie-Tan(NT)算法可节省计算消耗。此外,连续版本NT(CNT,continuous version of NT)算法被证明是计算质心的准确算法。本文比较了离散版本NT算法中求和运算和连续版本NT算法中求积分运算,通过四个计算机仿真例子证实了当适度增加区间二型模糊集主变量采样个数时,NT算法的计算结果可以精确地逼近CNT算法。     

An experimental evaluation of the efficacy of four multi-objective linear programming algorithms

Four multi-objective linear programming algorithms are implemented on microcomputer software packages and a large field experiment is conducted using the implemented algorithms. Two new algorithms which incorporate formal models of decision maker behavior are tested along with two established algorithms which include no formal models of decision maker behavior. The new algorithms are shown to outperform the established algorithms.     

论并行算法的分类

§1.引言利用并行计算系统求解数值计算问题或非数值计算问题,需根据系统的类型设计并行算法。自然,并行算法设计的主要基础是求解问题所涉及的学科领域中的原理和方法,或直接揭示某些已有方法中潜在的并行性,或推广某些原理发展显含并行性的新方法。但是,要建立可行(能在一个并行计算系统中实现)和有效(能充分发挥系统性能使并行度与     

最小树的一般算法

本文根据设计并行算法的基本原则,给出了最小树的两个对偶定理.在此基础上,建立了两种对偶的同步并行算法的雏型.这两种算法恰恰在对偶的意义下,概括了以往的最小树算法.     

Neighborhood-following algorithms for linear programming

In this paper, we present neighborhood-following algorithms for linear programming. When the neighborhood is a wide neighborhood, our algorithms are wide neighborhood primal-dual interior point algorithms. If the neighborhood degenerates into the central path, our algorithms also degenerate into path-following algorithms. We prove that our algorithms maintain the O(n~(1/2)L)-iteration complexity still, while the classical wide neighborhood primal-dual interior point algorithms have only the O(nL)-iteration complexity. We also proved that the algorithms are quadratic convergence if the optimal vertex is nondegenerate. Finally, we show some computational results of our algorithms.     

非线性Galerkin算法的收敛性和复杂性

本文给出了数值求解非线性发展方程的全离散非线性Ｇａｌｅｒｋｉｎ算法,即将空间离散时的谱非线性Ｇａｌｅｒｋｉｎ算法和时间离散的Ｅｕｌｅｒ差分格式相结合,得到了显式和隐式两种全离散数值格式,相应地也考虑了显式和隐式的Ｇａｌｅｒｋｉｎ全离散格式,并分别分析了上述四种全离散格式的收敛性和复杂性,经过比较得出结论;在某些约束条件下,非线性Ｇａｌｅｒｋｉｎ算法和Ｇａｌｅｒｋｉｎ算法具有相同阶的收敛速度,然而前     

Pseudo-conjugate directions for the solution of the nonlinear unconstrained optimization problem on a parallel computer

Parallel algorithms and test case results for the solution of the unconstrained optimization problem are presented. The algorithms involve the use of pseudo-conjugate directions (directions which tend to become conjugate as the solution is approached). It is shown that the algorithms are both fast and robust. Although all the algorithms of this paper involve the parallel execution of linear search procedures, a critical differentiation can be made among them, depending on whether the linear searches are performed along the same direction (parallel unidirectional algorithms) or different directions (parallel multidirectional algorithms).     

Fast Decoding Algorithms for First Order Reed-Muller and Related Codes

Fast decoding algorithms for short codes based on modifications of maximum likelihood decoding algorithms of first order Reed-Muller codes are described. Only additions-subtractions, comparisons and absolute value calculations are used in the algorithms. Soft and hard decisions maximum likelihood decoding algorithms for first order Reed-Muller and the Nordstrom-Robinson codes with low complexity are proposed.     

New variable-metric algorithms for nondifferentiable optimization problems

This paper deals with new variable-metric algorithms for nonsmooth optimization problems, the so-called adaptive algorithms. The essence of these algorithms is that there are two simultaneously working gradient algorithms: the first is in the main space and the second is in the space of the matrices that modify the main variables. The convergence of these algorithms is proved for different cases. The results of numerical experiments are also given.     

Approximation algorithms for nonnegative polynomial optimization problems over unit spheres

We consider approximation algorithms for nonnegative polynomial optimization problems over unit spheres. These optimization problems have wide applications e.g., in signal and image processing, high order statistics, and computer vision. Since these problems are NP-hard, we are interested in studying on approximation algorithms. In particular, we propose some polynomial-time approximation algorithms with new approximation bounds. In addition, based on these approximation algorithms, some efficient algorithms are presented and numerical results are reported to show the efficiency of our proposed algorithms.     

On obtaining higher order convergence for smooth periodic functions

We present two algorithms for multivariate numerical integration of smooth periodic functions. The cubature rules on which these algorithms are based use fractional parts of multiples of irrationals in combination with certain weights. Previous work led to algorithms with quadratic and cubic error convergence. We generalize these algorithms so that one can use them to obtain general higher order error convergence. The algorithms are open in the sense that extra steps can easily be taken in order to improve the result. They are also linear in the number of steps and their memory cost is low.     

Zur bestimmung aller elementarkreise in gerichteten graphen

New algorithms for enumerating all circuits of a directed graph are presented. These algorithms are backtrack algorithms, by which we intended to avoid fruitless computations. The best results give algorithms involving heuristic rules.

To receive numerical comparisons additionally the algorithms of DÖRFLER/MÜHL-BACHER, TIERNAN/SYSLO and BJELKINA were programmed and tested in FORTRAN (CDC 3300) and (or) ALGOL (ICL 4130).     

New extragradient-like algorithms for strongly pseudomonotone variational inequalities

The paper considers two extragradient-like algorithms for solving variational inequality problems involving strongly pseudomonotone and Lipschitz continuous operators in Hilbert spaces. The projection method is used to design the algorithms which can be computed more easily than the regularized method. The construction of solution approximations and the proof of convergence of the algorithms are performed without the prior knowledge of the modulus of strong pseudomonotonicity and the Lipschitz constant of the cost operator. Instead of that, the algorithms use variable stepsize sequences which are diminishing and non-summable. The numerical behaviors of the proposed algorithms on a test problem are illustrated and compared with those of several previously known algorithms.     

A new iterative Monte Carlo approach for inverse matrix problem

A new approach of iterative Monte Carlo algorithms for the well-known inverse matrix problem is presented and studied. The algorithms are based on a special techniques of iteration parameter choice, which allows to control the convergence of the algorithm for any column (row) of the matrix using different relaxation parameters. The choice of these parameters is controlled by a posteriori criteria for every Monte Carlo iteration. The presented Monte Carlo algorithms are implemented on a SUN Sparkstation. Numerical tests are performed for matrices of moderate in order to show how work the algorithms. The algorithms under consideration are well parallelized.     

Weak and strong convergence theorems for variational inequality problems

In this paper, we study the weak and strong convergence of two algorithms for solving Lipschitz continuous and monotone variational inequalities. The algorithms are inspired by Tseng’s extragradient method and the viscosity method with Armijo-like step size rule. The main advantages of our algorithms are that the construction of solution approximations and the proof of convergence of the algorithms are performed without the prior knowledge of the Lipschitz constant of cost operators. Finally, we provide numerical experiments to show the efficiency and advantage of the proposed algorithms.     

The multilevel principle applied to sorting

Sorting algorithms are developed in the setting of iterative multilevel methods. These algorithms borrow aggregation techniques from algorithms used for the numerical solution of elliptic partial differential equations which are of optimal order in running time and storage space for structured problems. A computationally inexpensive preconditioner drives random data chosen from known distributions towards a special case for which the new sorting algorithms are of optimal order.     

Some multilevel decoupled algorithms for a mixed navier-stokes/darcy model

In this work, several multilevel decoupled algorithms are proposed for a mixed Navier-Stokes/Darcy model. These algorithms are based on either successively or parallelly solving two linear subdomain problems after solving a coupled nonlinear coarse grid problem. Error estimates are given to demonstrate the approximation accuracy of the algorithms. Experiments based on both the first order and the second order discretizations are presented to show the effectiveness of the decoupled algorithms.     

A brief review of some application driven fast algorithms for elliptic partial differential equations

Some application driven fast algorithms developed by the author and his collaborators for elliptic partial differential equations are briefly reviewed here. Subsequent use of the ideas behind development of these algorithms for further development of other algorithms some of which are currently in progress is briefly mentioned. Serial and parallel implementation of these algorithms and their applications to some pure and applied problems are also briefly reviewed.     

非线性延迟积分微分方程连续Runge-Kutta方法的稳定性分析

本文主要研究了一般形式的延迟积分微分方程,将连续Runge-Kutt,a方法用于求解该类问题,并讨论了方法的稳定性,证明了(k,l)-代数稳定的Runge-Kutta方法当0k1时对应的连续Runge-Kutta方法是渐近稳定的.最后我们通过数值试验验证了方法的有效性及所获结论的正确性.     

Approximate nonlinear programming algorithms for solving stochastic programs with recourse

This paper summarizes the main results on approximate nonlinear programming algorithms investigated by the author. These algorithms are obtained by combining approximation and nonlinear programming algorithms. They are designed for programs in which the evaluation of the objective functions is very difficult so that only their approximate values can be obtained. Therefore, these algorithms are particularly suitable for stochastic programming problems with recourse.Project supported by the National Natural Science Foundation of China.