求解线性不等式组的方法

本提出了一个新的求解线性不等式组可行解的方法－－无约束极值方法。通过在线性不等式组的非空可行域的相对内域上建立一个非线性极值问题，根据对偶关系，得到了一个对偶空间的无约束极值及原始，对偶变量之间的简单线性映射关系，这样将原来线性不等式组问题的求解转化为一个无约束极值问题。中主要讨论了求解无约束极值问题的共轭梯度算法。同时，在寻找不等式组可行解的过程中，定义了穿越方向，这样大大减少计算量。中最后数值实验结果表明此算法是有效的。     

基于动力系统的线性不等式组的解法

本文提出了一种新的求解线性不等式组可行解的方法-动力系统方法.假设线性不等式组的可行域为非空,在可行域的相对内域上建立一个非线性极值问题,根据对偶关系,得到一个对偶空间的无约束极值问题以及原始、对偶变量之间的简单线性映射关系,进而得到了一个结构简单的动力系统模型.文中主要讨论了动力系统的隐式格式,通过证明模型具有较好的计算稳定性.同时,在寻找不等式组可行解的过程中,定义了穿越方向,这样可以减少计算量.数值实验结果表明此算法是有效的.     

具有线性等式和不等式约束的非线性规划的一个算法

§1.引言既约梯度法是求解非线性规划的一类方法.我们目前只看到约束为线性等式或非线性等式的既约梯度法,对于线性不等式或非线性不等式约束的情形还没有相应的既约梯度法.如果通过松驰变量把线性不等式约束化成线性等式的情形处理,则要增加变量的维数,而这是与既约梯度法的思想背道而驰的.在本文中,我们结合既约梯度法与 Ritter在文献[3]中的思想,对具有线性等式和不等式约束的非线性规划问题给出了一种算法,它保留了既约梯度法降低维数的优点,又简化了 Ritter 在[3]中给出的算法.另外,我们还证明了算法的收敛性.     

LMI优化的一种原对偶中心路径算法

系统和控制理论中许多重要的问题,都可转化为具有线性目标函数、线性矩阵不等式约束的LMI优化问题,从而使其在数值上易于求解.本文给出一种求解LMI优化问题的原对偶中心路径算法,该算法利用牛顿方法求解中心路径方程得到牛顿系统,并将该牛顿系统对称化以避免得到非对称化的搜索方向.文章详细分析了算法的计算复杂性.     

一个求线性代数方程组非负解的算法及其在线性规划中的应用

1.引言关于线性规划的多项式算法,哈奇扬于1979年首先把一个线性规划问题化成一个线性不等式组的求解问题,然后用椭球方法求解线性不等式组,并证明是多项式时间可解的。Karmarkar于1984年也给出了一个求解线性规划的多项式时间解法,他     

关于非线性不等式组Levenberg-Marquardt算法的收敛性(英文)

本文研究了一类非线性不等式组的求解问题.利用一列目标函数两次可微的参数优化问题来逼近非线性不等式组的解,光滑Levenberg-Marquardt方法来求解参数优化问题,在一些较弱的条件下证明了文中算法的全局收敛性,数值实例显示文中算法效果较好.     

第一阶段原有单纯形和对偶单纯形算法的计算比较

线性最优化广泛应用于经济与管理的各个领域.在线性规划问题的求解中,如果一个初始基本可行解没有直接给出,则常采用经典的两阶段法求解.对含有"≥"不等式约束的线性规划问题,讨论了第一阶段原有单纯形法和对偶单纯形法两种算法形式,并根据第一阶段问题的特点提出了改进的对偶单纯形枢轴准则.最后,通过大规模数值试验对两种算法进行计算比较,结果表明,改进后的对偶单纯形算法在计算效率上明显优于原有单纯形算法.     

线性规划的改进行旋转算法

行旋转算法是求解线性不等式组的一种直接、有效的算法,为大数据时代众多与线性不等式组紧密相关的问题提供了统一、高效的解决思路.求解线性规划问题本质上是求解线性不等式组,因而行旋转算法可以作为基础算法直接应用于求解线性规划.不同于以单纯形法为代表的列旋转算法,线性规划的行旋转算法以行几何(或行向量)为基础,其核心思想是在保证最优性条件始终成立的前提下求解约束条件对应的线性不等式组.改进的行旋转算法保持了原算法的所有特色.该算法的改进之处在于利用约束条件变量的部分系数构成的非奇异矩阵的逆矩阵(称为特征逆矩阵)和原始数据计算出枢轴行和枢轴列,从而完成一次旋转运算.特征逆矩阵的阶数一般要比约束的数目和变量的数目小很多,在每次迭代过程中只需要计算原算法算表中的小部分必要元素,因而能够显著提高计算效率.     

求解约束规划的一个非线性Lagrange函数

本文提出了一个求解不等式约束优化问题的非线性Lagrange函数,并构造了基于该函数的对偶算法.证明了当参数σ小于某一阈值σ_0时,由算法生成的原始-对偶点列是局部收敛的,并给出了原始-对偶解的误差估计.此外,建立了基于该函数的对偶理论.最后给出了算法的数值结果.     

凸函数的若干新性质及应用

本文证明了凸函数的若干新性质 ,讨论了这些性质在求解线性与非线性不等式组和线性规划中的应用 ,为线性与非线性不等式组、线性规划的求解提供了一种新方法 .     

基于动力系统的线性不等式组的解法

本文提出了一种新的求解线性不等式组可行解的方法-基于动力系统的方法．假设线性不等式组的可行域为非空,在可行域的相对内域上建立一个非线性关系表达式,进而得到一个结构简单的动力系统模型．同时,定义了穿越方向。文章最后的数值实验结果表明此算法是有效的．     

Mathematical programming via the least-squares method

The least-squares method is used to obtain a stable algorithm for a system of linear inequalities as well as linear and nonlinear programming. For these problems the solution with minimal norm for a system of linear inequalities is found by solving the non-negative least-squares (NNLS) problem. Approximate and exact solutions of these problems are discussed. Attention is mainly paid to finding the initial solution to an LP problem. For this purpose an NNLS problem is formulated, enabling finding the initial solution to the primal or dual problem, which may turn out to be optimal. The presented methods are primarily suitable for ill-conditioned and degenerate problems, as well as for LP problems for which the initial solution is not known. The algorithms are illustrated using some test problems.     

Duality for minmax programs

A duality theory is derived for minimizing the maximum of a finite set of convex functions subject to a convex constraint set generated by both linear and nonlinear inequalities. The development uses the theory of generalised geometric programming. Further, a particular class of minmax program which has some practical significance is considered and a particularly simple dual program is obtained.     

The Γ-algorithm and some applications

In this paper the power of the Γ-algorithm for obtaining the dual of a given cone and some of its multiple applications is discussed. The meaning of each sequential tableau appearing during the process is interpreted. It is shown that each tableau contains the generators of the dual cone of a given cone and that the algorithm updates the dual cone when new generators are incorporated. This algorithm, which is based on the duality concept, allows one to solve many problems in linear algebra, such as determining whether or not a vector belongs to a cone, obtaining the minimal representations of a cone in terms of a linear space and an acute cone, obtaining the intersection of two cones, discussing the compatibility of linear systems of inequalities, solving systems of linear inequalities, etc. The applications are illustrated with examples.     

正定几何规划的相关退化问题

n_k-1,k=0,1,…,r-1;n_r=n,n为所有 f_k(t)(k=0,1,…,r)包含的总项数,n_k-m_k 1为f_k(t)包含的项数. 它的目标函数取对数形式的对偶规划D:     

Bounding a class of nonconvex linearly-constrained resource allocation problems via the surrogate dual

We consider a class of problems of resource allocation under economies of scale, namely that of minimizing a lower semicontinuous, isotone, and explicitly quasiconcave cost function subject to linear constraints. An important class of algorithms for the linearly constrained minimization of nonconvex cost functions utilize the branch and bound approach, using convex underestimating cost functions to compute the lower bounds.We suggest instead the use of the surrogate dual problem to bound subproblems. We show that the success of the surrogate dual in fathoming subproblems in a branch and bound algorithm may be determined without directly solving the surrogate dual itself, but that a simple test of the feasibility of a certain linear system of inequalities will suffice. This test is interpreted geometrically and used to characterize the extreme points and extreme rays of the optimal value function's level sets.Research partially supported by NSF under grant # ENG77-06555.     

On the proximal point method in Hadamard spaces

Proximal point method is one of the most influential procedure in solving nonlinear variational problems. It has recently been introduced in Hadamard spaces for solving convex optimization, and later for variational inequalities. In this paper, we study the general proximal point method for finding a zero point of a maximal monotone set-valued vector field defined on a Hadamard space and valued in its dual. We also give the relation between the maximality and Minty’s surjectivity condition, which is essential for the proximal point method to be well-defined. By exploring the properties of monotonicity and the surjectivity condition, we were able to show under mild assumptions that the proximal point method converges weakly to a zero point. Additionally, by taking into account the metric subregularity, we obtained the local strong convergence in linear and super-linear rates.