二维非线性对流扩散方程的非振荡特征差分方法

１．引言 近十几年来,双曲守恒律问题的高分辨率格式已取得很大发展,具有局部自适应选取节点的非振荡插值算法（如 ＵＮＯ［１］, ＥＮＯ［２］等）在这些格式的构造中起着重要的作用．特征差分法是求解对流扩散问题的一种较为有效方法,但在求解具有陡峭前线问题时,也会产生非物理振荡阻（见４）．本文将把特征差分法与非振荡插值算法相结合构造对流扩散问题的高分辨率差分格式． ［１］中的 ＵＮＯ及［２］中的 ＥＮＯ插值都是一维的,有关讨论二维 ＵＮＯ及ＥＮＯ插值的文章还不多见,本文将构造二维基于六节点的二次非振荡插值以及…     

奇异M─矩阵的图相容弱正则分裂

设Ａ是奇异Ｍ－矩阵，Ａ＝Ｍ－Ｎ是Ａ的图相容弱正则分裂．本文研究迭代矩阵Ｍ－１Ｎ的谱性质，得到与迭代矩阵的指数有关的一个定理：ｉｎｄ０（Ａ）＝ｉｎｄ１（Ｍ－１Ｎ）．它推广了Ｈ．Ｓｃｈｎｅｉｄｅｒ和作者的结果．     

一类大规模二次规划问题的可行下降分解算法

本文对一类大规模二次规划问题，提出了矩阵剖分的概念和方法，并将问题转化为求解一系列容易求解的小规模二次规划子问题．另外，通过施加某些约束机制，使子问题所产生的迭代点均为可行下降点．在通常的假定下，证明算法具有全局收敛性，大量数值实验表明，本文所提出的新算法是有效的。     

奇异M—矩阵的图相容弱正则分裂

设Ａ是奇异Ｍ－矩阵，Ａ＝Ｍ－Ｎ是Ａ的图相容弱正则分裂。本文研究迭代矩阵Ｍ＾－１Ｎ的谱性质，得到与迭代矩阵的指数有关的一个定理：ｉｎｄ０（Ａ）＝ｉｎｄ１（Ｍ＾－１Ｎ）．它推广了Ｈ．Ｓｃｈｎｅｉｄｅｒ和作者的结果。     

矩阵方程X＋A^TX^—1A=Q存在可稳定化解的充分必要条件

１引言一般的时离散代数Ｒｉｃｃａｔｉ方程具有下面的形式：这里如果方程（１）中的系数矩阵满足：（ｎ＝ｍ）则方程（１）变为当Ｑ＝ＱＴ＞０时,Ｅｎｇｗｅｒｄａ,詹兴致等人研究了方程（２）存在正定解的充分必要条件［１］［２］［３］．本章利用方程（２）与（１）的关系,从另一角度讨论了Ｑ为对称矩阵时,方程（２）存在可稳定化解的充分必要条件．２基本概念与记号首先我们简单回顾一下以前的概念与记号．矩阵束Ｍ—Ｎ,Ｍ,Ｎ为正则的,也就是说ｄｅｔ（λＭ－Ｎ）＝０;如果λ０为ｄｅｔ（λＭ－Ｎ）的ｋ重根,则称λ０为它的ｋ…     

关于一些二次数域的Tame核的2-Sylow子群

本文主要研究二次数域Ｆ＝Ｑ（ｄ）的 ２－Ｓｙｌｏｗ于群,其中ｄ只有两个不同奇素数和２ ∈ＮＦ／Ｑ（Ｆ）．建立了 Ｅ＝Ｑ（－ｄ）的类群和具有平凡的４－秩或８－秩的Ｋ２ＯＦ两者之间等价关系．     

求解约束优化问题的一个对偶算法

１．引言 考虑下述形式的不等式约束优化问题：其中 ＝０,１,…,ｍ,是连续可微函数．求解（１．１）的数值方法有很多,传统方法有乘子法,序列一次规划方法,等等（见 Ｂｅｒｔｓｅｋａｓ（１９８２）, Ｈａｎ（１９７６, １９７７））．近年来对求解（１．１）的原始－对偶算法的研究已成为非线性规划领域的新的热点,如ＥＩ－Ｂａｋｒｙ,Ｔａｐｉａ,Ｔｓｕｃｈｉｙａ　＆　Ｚｈａｎｇ（１９９６）,Ｙａｍａｓｈｉｔａ（１９９２,１９９６,１９９７）等;尽管这些原始－对偶算法具有好的收敛性质和计算效果,但其算法结构相对…     

一类改进的非凸二次规划有效集方法

一类改进的非凸二次规划有效集方法修乃华（河北师范学院数学系）ＡＣＬＡＳＳＯＦＩＭＰＲＯＶＥＤＡＣＴＩＶＥＳＥＴＭＥＴＨＯＤＳＦＯＲＮＯＮＣＯＮＶＥＸＱＵＡＤＲＡＴＩＣＰＲＯＧＲＡＭＭＩＮＧＰＲＯＢＬＥＭ￥ＸｉｕＮａｉ－ｈｕａ（Ｄｅｐｔ．ｏｆＭａｔｈ．...     

计算股市的基本方程,理论和原理（Ⅲ）：基本理论

由基本方程导出两个理论：１．股票的价值理论３ｖ＊（ｔ）＝Ｖ＊（０）ｅｘｐ（ａｒ２＊ｔ）．２。股能守恒理论,将股能定义为股价ｖ及其导数ｖ的二次函数Х＝Ａｖ＾２＋Ｂｖｖ＋Ｃｖ＾２＋Ｄｖ,在基本方程约束下,将问题归结为沿最优路径的约束优化问题,应用Ｌａｇｒａｎｇｅ乘数,变分法Ｅｕｌｅｒ方程可证Х对任何ｖ、ｖ问题归结为沿最优路径的约束优化问题。应用Ｌａｇｒａｎｇｅ乘数,谱分法Ｅｕｌｅｒ方程可证Х对任何ｖ、     

某类等式约束二次规划问题的一个共轭方向法

本文提出了一种求解某类等式约束二次规划问题的一个共轭方向迭代法，并给出了算法的有限终止性证明．同时我们把此算法推广到不等式约束二次规划问题中，从而得到了一种求解不等式约束二次规划问题的算法．     

An SQP algorithm for extended linear-quadratic problems in stochastic programming

Extended Linear-Quadratic Programming (ELQP) problems were introduced by Rockafellar and Wets for various models in stochastic programming and multistage optimization. Several numerical methods with linear convergence rates have been developed for solving fully quadratic ELQP problems, where the primal and dual coefficient matrices are positive definite. We present a two-stage sequential quadratic programming (SQP) method for solving ELQP problems arising in stochastic programming. The first stage algorithm realizes global convergence and the second stage algorithm realizes superlinear local convergence under a condition calledB-regularity.B-regularity is milder than the fully quadratic condition; the primal coefficient matrix need not be positive definite. Numerical tests are given to demonstrate the efficiency of the algorithm. Solution properties of the ELQP problem underB-regularity are also discussed.Supported by the Australian Research Council.     

A New Quadratic Semi-infinite Programming Algorithm Based on Dual Parametrization

The so called dual parameterization method for quadratic semi-infinite programming (SIP) problems is developed recently. A dual parameterization algorithm is also proposed for numerical solution of such problems. In this paper, we present and improved adaptive algorithm for quadratic SIP problems with positive definite objective and multiple linear infinite constraints. In each iteration of the new algorithm, only a quadratic programming problem with a limited dimension and a limited number of constraints is required to be solved. Furthermore, convergence result is given. The efficiency of the new algorithm is shown by solving a number of numerical examples.     

带非凸二次约束的二次比式和问题的全局优化算法(英文)

对带非凸二次约束的二次比式和问题(P)给出分枝定界算法,首先将问题(P)转化为其等价问题(Q),然后利用线性化技术,建立了(Q)松弛线性规划问题(RLP),通过对(RLP)可行域的细分及求解一系列线性规划问题,不断更新(Q)的上下界,从理论上证明了算法的收敛性,数值实验表明了算法的可行性和有效性.     

On active-set methods for the quadratic programming problem

The active-set Newton method developed earlier by the authors for mixed complementarity problems is applied to solving the quadratic programming problem with a positive definite matrix of the objective function. A theoretical justification is given to the fact that the method is guaranteed to find the exact solution in a finite number of steps. Numerical results indicate that this approach is competitive with other available methods for quadratic programming problems.     

An Adaptive Dual Parametrization Algorithm for Quadratic Semi-infinite Programming Problems

The so called dual parametrization method for quadratic semi-infinite programming (SIP) problems is developed recently for quadratic SIP problems with a single infinite constraint. A dual parametrization algorithm is also proposed for numerical solution of such problems. In this paper, we consider quadratic SIP problems with positive definite objective and multiple linear infinite constraints. All the infinite constraints are supposed to be continuously dependent on their index variable on a compact set which is defined by a number equality and inequalities. We prove that in the multiple infinite constraint case, the minimu parametrization number, just as in the single infinite constraint case, is less or equal to the dimension of the SIP problem. Furthermore, we propose an adaptive dual parametrization algorithm with convergence result. Compared with the previous dual parametrization algorithm, the adaptive algorithm solves subproblems with much smaller number of constraints. The efficiency of the new algorithm is shown by solving a number of numerical examples.     

Equivalence of some quadratic programming algorithms

We formulate a general algorithm for the solution of a convex (but not strictly convex) quadratic programming problem. Conditions are given under which the iterates of the algorithm are uniquely determined. The quadratic programming algorithms of Fletcher, Gill and Murray, Best and Ritter, and van de Panne and Whinston/Dantzig are shown to be special cases and consequently are equivalent in the sense that they construct identical sequences of points. The various methods are shown to differ only in the manner in which they solve the linear equations expressing the Kuhn-Tucker system for the associated equality constrained subproblems. Equivalence results have been established by Goldfarb and Djang for the positive definite Hessian case. Our analysis extends these results to the positive semi-definite case. This research was supported by the Natural Sciences and Engineering Research Council of Canada under Grant No. A8189.     

Some generalizations of the criss-cross method for quadratic programming

Three generalizations of the criss-cross method for quadratic programming are presented here. Tucker’s, Cottle’s and Dantzig’s principal pivoting methods are specialized as diagonal and exchange pivots for the linear complementarity problem obtained from a convex quadratic program

A finite criss-cross method, based on least-index resolution, is constructed for solving the LCP. In proving finiteness, orthogonality properties of pivot tableaus and positive semidefiniteness of quadratic matrices are used

In the last section some special cases and two further variants of the quadratic criss-cross method are discussed. If the matrix of the LCP has full rank, then a surprisingly simple algorithm follows, which coincides with Murty’s ‘Bard type schema’ in the P matrix case     

A numerically stable least squares solution to the quadratic programming problem

The strictly convex quadratic programming problem is transformed to the least distance problem — finding the solution of minimum norm to the system of linear inequalities. This problem is equivalent to the linear least squares problem on the positive orthant. It is solved using orthogonal transformations, which are memorized as products. Like in the revised simplex method, an auxiliary matrix is used for computations. Compared to the modified-simplex type methods, the presented dual algorithm QPLS requires less storage and solves ill-conditioned problems more precisely. The algorithm is illustrated by some difficult problems.      

A parameterized hessian quadratic programming problem

We present a general active set algorithm for the solution of a convex quadratic programming problem having a parametrized Hessian matrix. The parametric Hessian matrix is a positive semidefinite Hessian matrix plus a real parameter multiplying a symmetric matrix of rank one or two. The algorithm solves the problem for all parameter values in the open interval upon which the parametric Hessian is positive semidefinite. The algorithm is general in that any of several existing quadratic programming algorithms can be extended in a straightforward manner for the solution of the parametric Hessian problem. This research was supported by the Natural Sciences and Engineering Research Council under Grant No. A8189 and under a Postgraduate Scholarship, by an Ontario Graduate Scholarship, and by the University of Windsor Research Board under Grant No. 9432.     

A parameterized hessian quadratic programming problem

We present a general active set algorithm for the solution of a convex quadratic programming problem having a parametrized Hessian matrix. The parametric Hessian matrix is a positive semidefinite Hessian matrix plus a real parameter multiplying a symmetric matrix of rank one or two. The algorithm solves the problem for all parameter values in the open interval upon which the parametric Hessian is positive semidefinite. The algorithm is general in that any of several existing quadratic programming algorithms can be extended in a straightforward manner for the solution of the parametric Hessian problem.This research was supported by the Natural Sciences and Engineering Research Council under Grant No. A8189 and under a Postgraduate Scholarship, by an Ontario Graduate Scholarship, and by the University of Windsor Research Board under Grant No. 9432.