基于模糊控制的ASIFT图像特征优化算法

ASIFT算法是图像特征匹配的有效工具,具有很强的仿射不变性.在ASIFT算法的基础上,利用Nelder-Mead单纯形方法优化采样点,并通过模糊控制策略实现单纯形参数的自适应调整.针对一组低空遥感图像的实验结果表明,本文方法保持了ASIFT算法对仿射变换的鲁棒性,并且能获得比ASIFT和SIFT算法更多的特征匹配对.     

相似于线规划中单纯形解法的Lemke-Howson 方法的改进

参考文献中对Lemke—Howson算法给出了相似于线性规划中的单纯形解法．我们在参考文献中给出一个反例．本文对文献中给出的相似于线性规划中的单纯形解法的Lemke—Howson算法作出改进，     

基于线性规划核心矩阵的单纯形算法

本文讨论了线性规划中的核心矩阵及其特性,探讨了利用核心矩阵实现单纯形算法的可能性,并进一步提出了一个基于核心矩阵的两阶段原始一对偶单纯形方法,该方法通过原始和对偶两个阶段的迭代,可以在有限次迭代中收敛到原问题的最优解或证明问题无解或无界．在试验的22个问题中,该算法的计算效率总体优于基于传统单纯形方法的MINOS软件．     

单纯形上Stancu算子对光滑函数的逼近误差的渐近展开

本文定义了ｍ维空间内一般单纯形上的Ｓｔａｎｃｕ算子，并给出了它对光滑函数的点态逼近误差的高阶渐近公式．     

基于最钝角规则的亏基对偶单纯形Ⅰ阶段算法

对偶单纯形算法或原始对偶单纯形算法都需要一个初始对偶可行基．就此目的而言，基于最钝角行主元规则的对偶Ⅰ阶段算法非常有效[15].本文将其思想应用于亏基情形，建立一个不含比值检验的新的亏基对偶Ⅰ价段算法．初步的数值实验表明，该算法可在总体上减少运行时间和迭代次数，极具竞争性．     

解带有二次约束二次规划的一个整体优化方法

在本文中，我们提出了一种解带有二次约束二次规划问题（QP）的新算法，这种方法是基于单纯形分枝定界技术，其中包括极小极大问题和线性规划问题作为子问题，利用拉格朗日松弛和投影次梯度方法来确定问题（QP）最优值的下界，在问题（QP）的可行域是n维的条件下，如果这个算法有限步后终止，得到的点必是问题（QP）的整体最优解；否则，该算法产生的点的序列{v^k}的每一个聚点也必是问题（QP）的整体最优解。     

线性规划流动等值面算法

对于线性规划问题，本文给出了基于流动等值面的等价模型，提出了一种不可行流动等值面算法．新算法保留了传统单纯形算法的优点并克服了它的不足。初步数值结果表明新算法比传统方法更为有效．     

避免引入人工变量求线性规划可行基的一个新方法

讨论了线性规划的单纯形解法,给出了不须加人工变量就可得到一个可行基的算法.通过大量的算例表明此法比传统的单纯形方法具有算法结构简单,计算量小的优点.     

单纯形调优法的收敛性质

在非线性规划中,单纯形调优法是一种可信用的算法,然而却缺乏理论分析,本文对单纯形调优法的理论进行了一些研究,所考虑的方法类似于Spendley,Hext,Himsworth的正规单纯形调优法,但采用了不同的反映条件,其中带有某种下山门槛或具有三点下降形式,这里的基本思想是证明:上述单纯形调优法是定步长下山法的特殊情形,所以,此研究紧密联系着作者关于定步长下山法收敛定理的工作。     

一类扩展线性规划问题及其算法

研究扩展线性规划问题（Ⅰ）ｍｉｎｚ＝∑ｎｊ＝１ｃｊ｜ｘｊ｜，ｓ．ｔ．Ａｘ＝ｂ证明了它与一类线性规划问题的等价性，给出其不扩展单纯形表的单纯形算法     

A Parallel Interior Point Method and Its Application to Facility Location Problems

We present a parallel interior point algorithm to solve block structured linear programs. This algorithm can solve block diagonal linear programs with both side constraints (common rows) and side variables (common columns). The performance of the algorithm is investigated on uncapacitated, capacitated and stochastic facility location problems. The facility location problems are formulated as mixed integer linear programs. Each subproblem of the branch and bound phase of the MIP is solved using the parallel interior point method. We compare the total time taken by the parallel interior point method with the simplex method to solve the complete problems, as well as the various costs of reoptimisation of the non-root nodes of the branch and bound. Computational results on two parallel computers (Fujitsu AP1000 and IBM SP2) are also presented in this paper.     

A Finite Smoothing Algorithm for Quantile Regression

This article introduces a new method for computing regression quantile functions. This method applies a finite smoothing algorithm based on smoothing the nondifferentiable quantile regression objective function ρ_τ. The smoothing can be done for all τ ∈ (0, 1), and the convergence is finite for any finite number of τ_i ∈ (0, 1), i = 1,…,N. Numerical comparison shows that the finite smoothing algorithm outperforms the simplex algorithm in computing speed. Compared with the powerful interior point algorithm, which was introduced in an earlier article, it is competitive overall; however, it is significantly faster than the interior point algorithm when the design matrix in quantile regression has a large number of covariates. Additionally, the new algorithm provides the same accuracy as the simplex algorithm. In contrast, the interior point algorithm gives only the approximate solutions in theory, and rounding may be necessary to improve the accuracy of these solutions in practice.     

Warm Start and ε-Subgradients in a Cutting Plane Scheme for Block-Angular Linear Programs

This paper addresses the issues involved with an interior point-based decomposition applied to the solution of linear programs with a block-angular structure. Unlike classical decomposition schemes that use the simplex method to solve subproblems, the approach presented in this paper employs a primal-dual infeasible interior point method. The above-mentioned algorithm offers a perfect measure of the distance to optimality, which is exploited to terminate the algorithm earlier (with a rather loose optimality tolerance) and to generate -subgradients. In the decomposition scheme, subproblems are sequentially solved for varying objective functions. It is essential to be able to exploit the optimal solution of the previous problem when solving a subsequent one (with a modified objective). A warm start routine is described that deals with this problem. The proposed approach has been implemented within the context of two optimization codes freely available for research use: the Analytic Center Cutting Plane Method (ACCPM)—interior point based decomposition algorithm and the Higher Order Primal-Dual Method (HOPDM)—general purpose interior point LP solver. Computational results are given to illustrate the potential advantages of the approach applied to the solution of very large structured linear programs.     

一类涉及两个单形的三角不等式及其应用

应用解析方法和几何不等式理论研究了n维欧氏空间En中涉及两个n维单形的几何不等式问题,建立了涉及两个单形的一类三角不等式.作为其应用,获得了涉及两个单形及其内点的几何不等式,特别,获得了n维单形与其垂足单形的体积的一类关系式,改进了关于垂足单形体积的几类几何不等式.     

n维欧氏空间En中Child不等式的推广

利用几何不等式理论与解析方法,研究了n维欧氏空间En中n维单形的内点到各顶点的距离与到各侧面距离之间的关系,获得相关的几个几何不等式,推广了Child不等式．     

n维欧氏空间E~n中Child不等式的推广

利用几何不等式理论与解析方法,研究了n维欧氏空间En中n维单形的内点到各顶点的距离与到各侧面距离之间的关系,获得相关的几个几何不等式,推广了Child不等式.     

Two direct methods in linear programming

In this paper, we introduce two direct methods for solving some classes of linear programming problems. The first method produces the extreme vertex or a neighboring vertex with respect to the extreme point. The second method is based on the game theory. Both these methods can be used in the preparation of the starting point for the simplex method. The efficiency of the improved simplex method, whose starting point is constructed by these introduced methods, is compared with the original simplex method and the interior point methods, and illustrated by examples. Also, we investigate the elimination of excessive constraints.     

An Interior Multiple Objective Primal-dual Linear Programming Algorithm Using Efficient Anchoring Points

We present an interior Multiple Objective Linear Programming (MOLP) algorithm based on the path-following primal-dual algorithm. In contrast to the simplex algorithm, which generates a solution path on the exterior of the constraints polytope by following its vertices, the path-following primal-dual algorithm moves through the interior of the polytope. Interior algorithms lend themselves to modifications capable of addressing MOLP problems in a way that is quite different from current solution approaches. In addition, moving through the interior of the polytope results in a solution approach that is less sensitive to problem size than simplex-based MOLP algorithms. The modification of the interior single-objective algorithm to MOLP problems, as presented here, is accomplished by combining the step direction vectors generated by applying the single-objective algorithm to each of the cost vectors into a combined direction vector along which we step from the current iterate to the next iterate.     

线性规划基线算法的基本概念

１．运算表格线性规划的基线算法是单纯形法（基点算法）的发展,因为每张运算表格对应着一条基线而得名．它象单纯形法一样好学易用,操作简便,而解题速度比单纯形法快．考虑标准型线性规划问题（ＬＰ）：：其中ｃ,ｘｅＲ"＋",Ａ是。ｘ（佩十。）矩阵,ｂｅＲ"。是（ＬＰ）的维数,。是约束个数．Ｘ＝｛ＸＥＲ""叫ＡＸ＝ｂ,Ｘ三０｝是（＊利的可行集．Ｘ是一个多面凸集．本文假定Ｃ４０．并且原点不是最优解．把Ｘ看作参数．方程组０．】Ｘ＝０,】的系数表称为母表（表１）．恒假设矩阵０－１－＼Ａｊ＼ｈｉ一"-一'-－"－"'一'-"-一"…