SOLVING CONVEX QUADRATIC PROGRAMMING BY POTENTIAL-REDUCTION INTERIOR-POINT ALGORITHM

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凸二次规划问题基于核函数的全牛顿步内点算法

针对凸二次规划问题,构造了新的核函数.通过构造的核函数来确定搜索方向和逼近度量,接着给出了求解凸二次规划问题的全牛顿步内点算法,最后给出了算法的复杂性界.     

非线性凸半定规划的割平面算法

先把一般的非线性凸半定规划转化成目标函数是线性函数的非线性凸半定规划,然后用割平面算法求解转化后的半定规划．最后证明了割平面算法的收敛性．     

凸二次规划的预估—校正光滑算法

对于求解凸二次规划问题,基于尺度中心路径,我们提出了一个预估—校正光滑化方法.在适当的假设条件下,证明了该方法具有全局收敛性和局部二次收敛性.     

凸二次规划的一个新原-对偶内点算法

In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization (CQO) based on a kernel function is presented. The proposed function has some properties that are easy for checking. These properties enable us to improve the polynomial complexity bound of a large-update interior-point method (IPM) to O (√nlognlogn/ε), which is the currently best known polynomial complexity bound for the algorithm with the large-update method. Numerical tests were conducted to investigate the behavior of the algorithm with different parameters p, q and θ, where p is the growth degree parameter, q is the barrier degree of the kernel function and θ is the barrier update parameter.     

解特殊凸二次半定规划的正则法

本文将利用论文[4]中所讨论的用以解线性半定规划问题的Moreau-Yosida正则法来求解一类特殊的凸二次半定规划问题.进一步,本文还给出了这种方法的全局收敛性分析以及初步的数值试验结果.     

A predictor-corrector interior-point algorithm for monotone variational inequality problems

Mehrotra's recent suggestion of a predictor-corrector variant of primal-dual interior-point method for linear programming is currently the interior-point method of choice for linear programming. In this work the authors give a predictor-corrector interior-point algorithm for monotone variational inequality problems. The algorithm was proved to be equivalent to a level-1 perturbed composite Newton method. Computations in the algorithm do not require the initial iteration to be feasible. Numerical results of experiments are presented.     

线性约束凸规划的一个新原-对偶路径-跟踪内点算法

In this paper, a primal-dual path-following interior-point algorithm for linearly constrained convex optimization （LCCO） is presented. The algorithm is based on a new technique for finding a class of search directions and the strategy of the central path. At each iteration, only full-Newton steps are used. Finally, the favorable polynomial complexity bound for the algorithm with the small-update method is deserved, namely, O（√nlog n/ε）.     

Primal-dual interior-point algorithms with dynamic step-size based on kernel functions for linear programming

In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming (LP) problems. The algorithms are based on a few kernel functions, including both self-regular functions and non-self-regular ones. The dynamic step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that the algorithms with dynamic step size are more efficient than those with fixed step size. Project supported by Dutch Organization for Scientific Research (Grant No.613.000.010)     

基于对数变换的凸二次规划不可行内点算法

基于对数变换和不可行内点算法,对凸二次规划提出了一种新的迭代方向原始-对偶不可行内点算法,并证明了算法的全局收敛性和多项式复杂性,该算法可以看做近期Pan等人关于线性规划算法的推广.     

Global optimality conditions for quadratic 0–1 programming with inequality constraints

Quadratic 0-1 problems with linear inequality constraints are briefly considered in this paper.Global optimality conditions for these problems,including a necessary condition and some sufficient conditions,are presented.The necessary condition is expressed without dual variables.The relations between the global optimal solutions of nonconvex quadratic 0-1 problems and the associated relaxed convex problems are also studied.     

Global optimality conditions for quadratic 0-1 programming with inequality constraints

Quadratic 0-1 problems with linear inequality constraints are briefly considered in this paper.Global optimality conditions for these problems,including a necessary condition and some sufficient conditions,are presented.The necessary condition is expressed without dual variables.The relations between the global optimal solutions of nonconvex quadratic 0-1 problems and the associated relaxed convex problems are also studied.     

一个无约束二次规划的秩一算法

本文提出了一个无约束二次规划的秩一算法,该算法对Davidon算法进行了改进并赋以一维搜索,证明了迭代矩阵的正定性,从而搜索方向是下降方向。此外得到了该算法有限步收敛的结果。     

不等式约束优化问题的一个内点算法

利用所求非线性规划问题的KKT条件,提出一个牛顿法与同伦法相结合的算法,最后给出了该算法的全局线性收敛性的证明。     

基于尺度中心路径的预估—矫正光滑化算法

对于求解线性规划问题提出了一个基于尺度中心路径的预估-矫正光滑化方法.在适当的假设条件下,证明了方法的全局和局部二次收敛性.特别,在方法的局部二次收敛性分析中,不需要假定线性规划的解是唯一的.文中的方法可以推广到P0-线性互补问题和单调线性互补问题.     

用于线性优化的基于核函数的动态步长原-对偶内点算法

In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming （LP） problems. The algorithms are based on a few kernel functions, including both serf-regular functions and non-serf-regular ones. The dynamic step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that the algorithms with dynaraic step size are more efficient than those with fixed step size.     

实系数二次型标准化算法及编程

n阶实对称矩阵总可以化为与自身合同的对角矩阵,而且这种转化可以经过有限步操作完成据此,得到实系数二次型的标准化算法。     

框式约束凸二次规划问题的内点算法

目前已经有许多关于凸二次规划问题的研究,如文[1][2][5]等,文章对文[1]所给的原始-对偶内点算法理论上的某些缺陷加以更正,给出了框式约束凸二次规划问题的一个修正原始-对偶内点算法并进行了证明.     

C[a,b]中凸函数拟弱收敛性的补充

本证明C[a,b]中的有界凸函数序列，必须有拟弱收敛子列，并对有关结果和了进一步补充。     

一类非凸规划的数值解法

针对非凸规划，本引进一简单的惩罚函数将其局部凸化，然后用凸规划的方法求解。在此基础上，提出了一种数值解法，并证明它的收敛性。