基于部分基变量的LP问题矩阵算法

基于部分基变量提出了LP问题的矩阵算法. 该算法以最优基矩阵的一个充分必要条件为基础,首先将一个初始矩阵转化为右端项和检验数均满足要求的矩阵,再转为检验数满足要求的基矩阵,最后转化为最优基矩阵.该算法具有使用范围广、计算规模小、计算过程简化、计算机易于实现的优势.矩阵算法的核心运算是求逆矩阵的运算,提出了矩阵算法的求逆问题,讨论并给出了求逆快速算法,该算法充分利用了矩阵算法迭代过程中提供的原来的逆矩阵的信息经过简单的变换得到新的逆矩阵,该算法比直接求逆法计算效率更高.     

伪谱计算的增广块Householder Arnoldi算法（英文）

伪谱是解释非正规矩阵或算子行为的一个有用工具.矩阵伪谱计算的一个常用方法是grid-SVD算法,实现这个算法需要在每一个网格点处作奇异值分解(SVD);另外一个计算方法是基于Schur分解的逆Lanczos算法.由于上述方法的计算量比较大,通常只适用于中小型矩阵.近些年,有些学者探讨了大规模矩阵伪谱计算的Krylov子空间投影方法.在探讨了Householder Arnoldi(HA)算法块情形的计算行为和实用性能的基础上,提出了计算大规模矩阵伪谱的增广块HA(ABHA)算法,并对一些典型测试矩阵进行了一系列的数值试验.数值结果表明,增广块HA(ABHA)算法比HA算法,块隐式重启Arnoldi(BLIRA)算法和逆Lanczos算法的计算效率更高,更具优越性.     

AHP判断矩阵权向量的改进最小二乘求解

提出了基于最小二乘法计算判断矩阵权向量的新方法.固定AHP判断矩阵权向量中的一个值为常量,利用判断矩阵的上三角部分元素,设计了一种计算判断矩阵权向量的新算法,算法简单,计算容易,与特征向量排序方法导出标度相同,并且能够证明存在唯一解.实验表明该算法具有有效性和可行性.     

一类Riccati矩阵方程广义自反解的双迭代算法

本文研究了一类Riccati矩阵方程广义自反解的数值计算问题.利用牛顿算法将Riccati矩阵方程的广义自反解问题转化为线性矩阵方程的广义自反解或者广义自反最小二乘解问题,再利用修正共轭梯度法计算后一问题,获得了求Riccati矩阵方程的广义自反解的双迭代算法.拓宽了求解非线性矩阵方程的迭代算法.数值算例表明双迭代算法是有效的.     

大深度任意剖面形状光栅的模式理论和RTCM递推算法 *

采用模式理论和反射透射系数阵 (简记为RTCM)的递推算法 ,计算了任意偏振态的平面波从任意方向入射到大深度任意剖面形状的光栅中的衍射 .该方法物理概念清楚 ,算法简洁 ,计算速度快 ,算法稳定收敛 .把这种算法与R -矩阵算法和增强透射矩阵算法在计算准确度、计算稳定性和计算速度方面进行了比较 .     

含高次逆幂的矩阵方程对称解的双迭代算法

本文研究了在控制理论和随机滤波等领域中遇到的一类含高次逆幂的矩阵方程的等价矩阵方程对称解的数值计算问题.采用牛顿算法求等价矩阵方程的对称解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程的对称解或者对称最小二乘解,建立了求这类矩阵方程对称解的双迭代算法,数值算例验证了双迭代算法是有效的.     

利用分解校正矩阵确定搜索方向的BFGS算法

本文把正定矩阵关于向量的等内积分解算法应用于改进BFGS算法中搜索方向的计算.通过建立不依赖于搜索方式的用分解矩阵表达的校正公式,给出了用Hesse近似矩阵的等内积分解矩阵确定搜索方向的BFGS算法.     

实对称区间矩阵特征值确界的交错定理及其应用

本文将实对称矩阵特征值的交错定理推广到实对称区间矩阵,给出了实对称区间矩阵特征值确界的交错定理,并应用该定理构造了估计实对称三对角区间矩阵特征值界的算法.文中数值例子表明,本文所给算法与一些现有算法相比在使用范围、计算精度和计算量等方面都具有一定的优越性.     

非线性极大极小系统全局优化算法的分析

非线性极大极小系统的全局优化可用于柔性制造和智能交通的决策与控制.实现了非线性极大极小系统的全局优化算法的仿真,并进行了计算时间分析.数值实验表明了全局优化算法的可行性.算法的计算时间主要由系统的优化极大射影矩阵数目决定,而优化极大射影矩阵数目与系统解析式中单极大式的系数紧密相关,系数取值越分散,简约极大射影矩阵的效果越好,计算效率越高.     

基于Taylor级数的矩阵双曲余弦函数的数值算法

提出了一种基于Taylor级数的矩阵双曲余弦函数的数值逼近算法,为减少计算量使用了Paterson-Stockmeyer方法来计算矩阵Taylor多项式,对逼近误差进行了绝对后向误差分析以减少误差,并设计了算法可以较为快速且准确地求解矩阵双曲余弦函数,最后进行了数值实验,验证了算法的有效性.     

A linear programming-based optimization algorithm for solving nonlinear programming problems

In this paper a linear programming-based optimization algorithm called the Sequential Cutting Plane algorithm is presented. The main features of the algorithm are described, convergence to a Karush–Kuhn–Tucker stationary point is proved and numerical experience on some well-known test sets is showed. The algorithm is based on an earlier version for convex inequality constrained problems, but here the algorithm is extended to general continuously differentiable nonlinear programming problems containing both nonlinear inequality and equality constraints. A comparison with some existing solvers shows that the algorithm is competitive with these solvers. Thus, this new method based on solving linear programming subproblems is a good alternative method for solving nonlinear programming problems efficiently. The algorithm has been used as a subsolver in a mixed integer nonlinear programming algorithm where the linear problems provide lower bounds on the optimal solutions of the nonlinear programming subproblems in the branch and bound tree for convex, inequality constrained problems.     

A contraction algorithm for finding small cycle cutsets

We consider the problem of finding a minimum size cutset in a directed graph G = (V, E), i.e., a vertex set that cuts all cycles in G. Since the general problem is NP-complete we concentrate on finding small cutsets. The algorithm we suggest uses contraction operations to reduce the graph size and to identify candidates for the cutset; the complexity of the algorithm is O(|E|log|V|). This contraction algorithm is compared to Shamir-Rosen algorithm. It is shown that the class of graphs for which the contraction algorithm finds a minimum cutset (completely contractible graphs) properly contains the class of graphs for which Shamir-Rosen algorithm finds a minimum cutset (quasi-reducible graphs) and thus that the contraction algorithm is more powerful. As a by-product of this analysis we construct a hierarchy of the classes of graphs for which minimum cutsets can be found efficiently. The class of quasi-reducible graphs lies, in this hierarchy, between two classes which are closely related. This result illuminates the nature of the quasi-reducible graphs. The hierarchy constructed allows us also to compare the Wang-Lloyd-Soffa algorithm to the Shamir-Rosen algorithm and to the contraction algorithm.     

A general extrapolation procedure revisited

TheE-algorithm is the most general extrapolation algorithm actually known. The aim of this paper is to provide a new approach to this algorithm. This approach gives a deeper insight into theE-algorithm, and allows one to obtain new properties and to relate it to other algorithms. Some extensions of the procedure are discussed.     

A stochastic steepest-descent algorithm

A stochastic steepest-descent algorithm for function minimization under noisy observations is presented. Function evaluation is done by performing a number of random experiments on a suitable probability space. The number of experiments performed at a point generated by the algorithm reflects a balance between the conflicting requirements of accuracy and computational complexity. The algorithm uses an adaptive precision scheme to determine the number of random experiments at a point; this number tends to increase whenever a stationary point is approached and to decrease otherwise. Two rules are used to determine the number of random experiments at a point; one, in the inner loop of the algorithm, uses the magnitude of the observed gradient of the function to be minimized; and the other, in the outer-loop, uses a measure of accumulated errors in function evaluations at past points generated by the algorithm. Once a stochastic approximation of the function to be minimized is obtained at a point, the algorithm proceeds to generate the next point by using the steepest-descent deterministic methods of Armijo and Polak (Refs. 3, 4). Convergence of the algorithm to stationary points is demonstrated under suitable assumptions.     

A new bidirectional search algorithm with shortened postprocessing

For finding a shortest path in a network bidirectional A^∗ is a widely known algorithm. This algorithm distinguishes between the main phase and the postprocessing phase. The version of bidirectional A^∗ that is considered the most appropriate in literature hitherto, uses a so-called balanced heuristic estimate. This type of heuristic is chosen, as it accounts for a short postprocessing phase. In this paper, we do not restrict ourselves any longer to balanced heuristics. First, we introduce an algorithm containing a new method for the postprocessing phase, reducing this phase considerably for non-balanced heuristics. For a balanced heuristic the new algorithm is nearly equivalent to the existing versions of bidirectional A^∗. An obvious choice for a non-balanced heuristic turns out to be superior in terms of storage space and computation time. Second, we show that the main phase on its own, when using this non-balanced heuristic estimate, is a useful algorithm, which provides us quickly with a feasible approximation.     

A successful algorithm for solving directed Hamiltonian path problems

In this paper we present a graph-theoretic polynomial algorithm which has positive probability of finding a Hamiltonian path in a given graph, if there is one; if the algorithm fails, it can be rerun with a randomly chosen starting solution, and there is again a positive probability it will find an answer. If there is no Hamiltonian path, the algorithm will always terminate with failure. We call this a Successful Algorithm because it has high (close to 1) empirical probability of success and it works in polynomial time. Some basic theoretical results concerning spanning arborescences of a graph are given. The concept of a ramification index is defined and it is shown that ramification index of a Hamiltonian path is zero. The algorithm starts with finding any spanning arborescence and by suitable pivots it endeavors to reduce the ramification index to zero. Probabilistic properties of the algorithm are discussed. Computational experience with graphs up to 30 000 nodes is included.     

A new algorithm for generalized fractional programs

A new dual problem for convex generalized fractional programs with no duality gap is presented and it is shown how this dual problem can be efficiently solved using a parametric approach. The resulting algorithm can be seen as “dual” to the Dinkelbach-type algorithm for generalized fractional programs since it approximates the optimal objective value of the dual (primal) problem from below. Convergence results for this algorithm are derived and an easy condition to achieve superlinear convergence is also established. Moreover, under some additional assumptions the algorithm also recovers at the same time an optimal solution of the primal problem. We also consider a variant of this new algorithm, based on scaling the “dual” parametric function. The numerical results, in case of quadratic-linear ratios and linear constraints, show that the performance of the new algorithm and its scaled version is superior to that of the Dinkelbach-type algorithms. From the computational results it also appears that contrary to the primal approach, the “dual” approach is less influenced by scaling. This research was carried out at the Econometric Institute, Erasmus University, Rotterdam, the Netherlands and was supported by J.N.I.C.T. (Portugal) under contract BD/707/90-RM.     

A practical algorithm for faster matrix multiplication

The purpose of this paper is to present an algorithm for matrix multiplication based on a formula discovered by Pan [7]. For matrices of order up to 10 000, the nearly optimum tuning of the algorithm results in a rather clear non‐recursive one‐ or two‐level structure with the operation count comparable to that of the Strassen algorithm [9]. The algorithm takes less workspace and has better numerical stability as compared to the Strassen algorithm, especially in Winograd's modification [2]. Moreover, its clearer and more flexible structure is potentially more suitable for efficient implementation on modern supercomputers. Copyright © 1999 John Wiley & Sons, Ltd.     

A parallel algorithm for constructing minimum spanning trees

The construction of minimum spanning trees (MSTs) of weighted graphs is a problem that arises in many applications. In this paper we will study a new parallel algorithm that constructs an MST of an N-node graph in time proportional to N lg N, on an $\text{N}\text{(}\text{lg}\text{N)-}\text{processor}$ computing system. The primary theoretical contribution of this paper is the new algorithm, which is an improvement over Sollin's parallel MST algorithm in several ways. On a more practical level, this algorithm is appropriate for implementation in VLSI technology.     

A mountain pass algorithm with projector

In this paper, we present an enhanced version of the minimax algorithm of Chen, Ni, and Zhou that offers the additional guarantee that the solution found is a fix-point of a projector on a cone. Positivity, negativity, and monotonicity can be expressed in this way. The convergence of the algorithm is proved by means of a “computational deformation lemma” instead of the usual deformation lemma used in the calculus of variations.