解非线性多解边值问题的异步并行打靶法

在多处理机(MIMD)上用异步并行打靶法来数值求解两点边值问题是最为有效的。这是因为求解过程中可以采用分区搜索的方法,而这种搜索过程几乎是完全独立地进行的.另一方面,非线性的具有多个解的数学物理问题的求解是一个比较困难的问题.因为采用通常的迭代法计算,有时很难求出全部解来(参看[1]、[2]),尤其是遇到所谓“排斥性不动点”(repulsive fixed point)时,一般迭代算法往往失败,而采用打靶法则可能将全部解求出来,如果打靶过程是数值稳定的话.用打靶法计算两点边值问题的文献很多(例如参看[3]、[4]).H.B.Keller 和 A.W.Wolfe[5]1965年就成功地应用打靶法来计算非线性分歧问题,后来有了迅速的发展(可参看文献[6]、[7]、[8]).     

广义多目标数学规划非支配解的二阶条件

§1.引言在不等式约束规划中,解的二阶条件是十分重要的课题.关于解的二阶条件,在单目标规划中已经得到了一些很重要的结果,如文献[1—4]等,都从各个不同的方面,引进不同的约束规格来讨论单目标数学规划解的二阶条件.在多目标数学规划中,有关“有效解”、“弱有效解”及“真有效解”的性质及一阶条件,已在不少书及文章中出现,如文献[5—9]等.本文试图就广义多目标数学规划相对于一般凸锥及某个多面体锥的局部和整体非支配解的二阶条件进行讨论.     

非线性互补问题解的存在性检验

在数学规划研究领域中这是一个受到广泛关注的问题,并提出多种求解的迭代算法,但是几乎没有算法可以在计算数值解的同时自动给出解的误差界,Alefeld、Chen和Potra在[1],[2]中讨论了求解线性与非线性互补问题的可靠性算法,该算法是将非线性互补问题归结为：等价的非线性方程（经常被称为Pang方程[7]）     

矩阵方程AX-XB=C的连分式解法

矩阵方程AX—XB=C是一个众所周知的基本问题,在代数和应用数学中起重要作用。本文是文献[1]的推广,使用与A,B有关的连分式,我们得到解X的标准的代数构造公式,与其它已知的结果相比,它能够简化X的数值计算当B=— A*为渐近稳定,C为正定Hermite矩阵时,以X为系数矩阵的H型函数可直接按此公式分解为若干个非负H型之和。     

关于二次域理论求解一类Diophantus方程的整数解

本文研究了两个典型Diophantus方程在实二次域中整数解的问题.利用二次域中的理论和二次代数整数环中算术基本定理,得到了该类方程的一般解法和在实二次域中的所有整数解的相关结论,推广了文献[1]和[2]的结果.     

一类多个下层的双层规划问题

本文研究了一类多个下层的双层规划问题.利用文[1]有关理论与方法,获得了该类多下层双层规划问题与一类广义纳什均衡问题的联系,然后通过寻找该广义纳什均衡问题的均衡点求解该双层规划问题.同时给出了一种求解此类广义纳什均衡问题的算法,并进行了一定的理论分析与数值计算.     

病态线性代数方程组的一种刚性问题数值解法

1．引言文［1,2］中提出的预估校正法是国内计算数学工作者研究刚性常微分方程数值解法的较早期的工作．并且作者将自己构造的算法用于解病态线性代数方程组卜个FORTRAN标准程序见[3]）．文[4,5］根据李雅普诺夫稳定性理论建立了病态线性代数方程组的解与对应刚性常微分方程组初值问题的解之间的关系并且采用Lambert提出的解刚性问题的非线性单步方法问给出了解病态线性代数方程组的非线性迭代法．但这个非线性方法有两大缺点：第一,数值解不能有零分量;第二,代数精确度较差．为此本文采用局部指数逼近法建立的解刚性问题的二阶显式…     

择一定理的推广

众所周知,择一定理是推导数学规划解的性质和最优性条件的主要工具.因此,讨论数学规划的一个重要途径就是推广择一定理,使它有更广泛的实用性.文献[1-3]就择一定理作过推广并就推广后的择一定理讨论了许多应用.文献[4-5]就[2-3]中给出的择一定理在多日标规划和向量极值问题上又找到了许多应用.在这篇文章里,我们提出广义次似凸映射概念,它是对[2-3]中次似凸映射的真     

无单调性集值变分不等式的一种投影算法

本文给出了求解无单调性集值变分不等式的一个新的投影算法,该算法所产生的迭代序列在Minty变分不等式解集非空且映射满足一定的连续性条件下收敛到解.对比文献[10]中的算法,本文中的算法使用了不同的线性搜索和半空间,在计算本文所引的两个数值例子时,该算法比文献[10]中的算法所需迭代步更少.     

一种非增值型凸二次双层规划的有效算法

主要研究了非增值型凸二次双层规划的一种有效求解算法。首先利用数学规划的对偶理论,将所求双层规划转化为一个下层只有一个无约束凸二次子规划的双层规划问题.然后根据两个双层规划的最优解和最优目标值之间的关系,提出一种简单有效的算法来解决非增值型凸二次双层规划问题.并通过数值算例的计算结果说明了该算法的可行性和有效性。     

Some Test Problems on Applications of Wu＇s Method in Nonlinear Programming Problems

ＳｏｍｅＴｅｓｔＰｒｏｂｌｅｍｓｏｎＡｐｐｌｉｃａｔｉｏｎｓｏｆＷｕ＇ｓＭｅｔｈｏｄｉｎＮｏｎｌｉｎｅａｒＰｒｏｇｒａｍｍｉｎｇＰｒｏｂｌｅｍｓ吴天骄ＳｏｍｅＴｅｓｔＰｒｏｂｌｅｍｓｏｎＡｐｐｌｉｃａｔｉｏｎｓｏｆＷｕ＇ｓＭｅｔｈｏｄｉｎＮｏｎｌｉｎ...     

Singular Perturbation Analysis of Boundary Value Problems for Differential-Difference Equations. IV. A Nonlinear Example with Layer Behavior

A study is made of boundary value problems for a class of singularly perturbed nonlinear, second-order, differential-difference equations, i.e., where the highest-order derivative is multiplied by a small parameter. Depending on the region of parameter space, solutions of the nonlinear problem may not be unique, can exhibit extreme sensitivity to the values of the parameters, or may not exist. Typically, solutions exhibit layer behavior and/or exponentially large amplitudes. Approximate solutions of these boundary value problems are obtained by using singular perturbation methods and numerical computations and are then compared. Numerical computations of representative solutions illustrate the wide variety of possible behaviors.     

A Novel Numerical Simulations for Fornberg-Whitham and Modified Fornberg-Whitham Equations with Nonhomogeneous Boundary Conditions

In this study, the numerical solutions of the Fornberg-Whitham (FW) equation modeling the qualitative behavior of wave refraction and the modified Fornberg-Whitham (mFW) equation describing the solitary wave and peakon waves with a discontinuous first derivative at the peak have been obtained. To obtain numerical results, the collocation finite element method has been combined with quintic B-spline bases. Although there are solutions to these equations by semi-analytical and analytical methods in the literature, there are very few studies using numerical methods. The stability analysis of the applied method is examined by the von-Neumann Fourier series method. We have considered four test problems with nonhomogeneous boundary conditions that have analytical solutions to show the performance of the method. The numerical results of the two problems are compared with some studies in the literature. Additionally, peakon wave solutions and some new numerical results of the mFW equation, which are not available in the literature, are given in the last two problems. No comparison has been made since there are no numerical results in the literature for the last two problems. The error norms $L_{2}$ and $L_{\infty }$ are calculated to demonstrate the presented numerical scheme''s accuracy and efficiency. The advantage of the scheme is that it produces accurate and reliable solutions even for modest values of space and time step lengths, rather than small values that cause excessive data storage in the computation process. In general, large step lengths in the space and time directions result in smaller matrices. This means less storage on the computer and results in faster outcomes. In addition, the present method gives more accurate results than some methods given in the literature.     

On smooth reformulations and direct non-smooth computations for minimax problems

Minimax problems can be approached by reformulating them into smooth problems with constraints or by dealing with the non-smooth objective directly. We focus on verified enclosures of all globally optimal points of such problems. In smooth problems in branch and bound algorithms, interval Newton methods can be used to verify existence and uniqueness of solutions, to be used in eliminating regions containing such solutions, and point Newton methods can be used to obtain approximate solutions for good upper bounds on the global optimum. We analyze smooth reformulation approaches, show weaknesses in them, and compare reformulation to solving the non-smooth problem directly. In addition to analysis and illustrative problems, we exhibit the results of numerical computations on various test problems.     

Regularization techniques in interior point methods

Regularization techniques, i.e., modifications on the diagonal elements of the scaling matrix, are considered to be important methods in interior point implementations. So far, regularization in interior point methods has been described for linear programming problems, in which case the scaling matrix is diagonal. It was shown that by regularization, free variables can be handled in a numerically stable way by avoiding column splitting that makes the set of optimal solutions unbounded. Regularization also proved to be efficient for increasing the numerical stability of the computations during the solutions of ill-posed linear programming problems. In this paper, we study the factorization of the augmented system arising in interior point methods. In our investigation, we generalize the methods developed and used in linear programming to the case when the scaling matrix is positive semidefinite, but not diagonal. We show that regularization techniques may be applied beyond the linear programming case.     

Error analysis for hybrid Trefftz methods coupling traction conditions in linear elastostatics

For linear elastostatics, the Lagrange multiplier to couple the displacement (i.e., Dirichlet) condition is well known in mathematics community, but the Lagrange multiplier to couple the traction (i.e., Neumann) condition is popular for elasticity problems by the Trefftz method in engineering community, which is called the Hybrid Trefftz method (HTM). However, there has not been any analysis for these Lagrange multipliers to couple the traction condition so far. New error analysis of the HTM for elasticity problems is explored in this paper, to derive error bounds with the optimal convergence rates. Numerical experiments are reported to support this analysis. The error analysis of the HTM for linear elastostatics is the main aim of this paper. In this paper, the collocation Trefftz method (CTM) without a multiplier is also introduced, accompanied with error analysis. Numerical comparisons are made for HTM and CTM using fundamental solutions (FS) and particular solutions (PS). The error analysis and numerical computations show that the accuracy of the HTM is equivalent to that of the CTM, but the stability of the CTM is good. For elasticity and other complicated problems, the simplicity of algorithms and programming grants the CTM a remarkable advantage. More numerical comparisons show that using PS is more efficient than using FS in both HTM and CTM. However, since the optimal convergence rates are the most important criterion in evaluation of numerical methods, the global performance of the HTM is as good as that of the CTM. The comparisons of HTM and CTM using FS and PS are the next aim of this article. © 2011 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2011     

Bee colony optimization for scheduling independent tasks to identical processors

The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.     

A parallel version of a quasigradient methoid In stochastic control theory ∗

We solve an optimal control problem for controlled parabolic Ito equations by a stochastic quasigradient method. Because of high amounts of computation time required by numerical solution of such problems we investigate the parallelization of the algorithm. We distribute the computations of space stages over several processor nodes of a parallel computer. We obtain an efficient algorithm with low communication cost by using a ring topology     

A Chebyshev spectral collocation method for solving Burgers’-type equations

In this paper, we elaborated a spectral collocation method based on differentiated Chebyshev polynomials to obtain numerical solutions for some different kinds of nonlinear partial differential equations. The problem is reduced to a system of ordinary differential equations that are solved by Runge–Kutta method of order four. Numerical results for the nonlinear evolution equations such as 1D Burgers’, KdV–Burgers’, coupled Burgers’, 2D Burgers’ and system of 2D Burgers’ equations are obtained. The numerical results are found to be in good agreement with the exact solutions. Numerical computations for a wide range of values of Reynolds’ number, show that the present method offers better accuracy in comparison with other previous methods. Moreover the method can be applied to a wide class of nonlinear partial differential equations.     

Solving Fractional Multicriteria Optimization Problems with Sum of Squares Convex Polynomial Data

This paper focuses on the study of finding efficient solutions in fractional multicriteria optimization problems with sum of squares convex polynomial data. We first relax the fractional multicriteria optimization problems to fractional scalar ones. Then, using the parametric approach, we transform the fractional scalar problems into non-fractional problems. Consequently, we prove that, under a suitable regularity condition, the optimal solution of each non-fractional scalar problem can be found by solving its associated single semidefinite programming problem. Finally, we show that finding efficient solutions in the fractional multicriteria optimization problems is tractable by employing the epsilon constraint method. In particular, if the denominators of each component of the objective functions are same, then we observe that efficient solutions in such a problem can be effectively found by using the hybrid method. Some numerical examples are given to illustrate our results.