线性互补约束规划问题的一种Filter算法

本文研究了求解线性互补约束规划问题的算法问题.首先基于广义互补函数和摄动技术将问题转化为带参数的非线性优化问题,利用SlQP-Filter算法方法,求解线性互补约束规划问题的一种Filter算法.在适当条件下,证明了该算法的全局收敛性.     

求解特定线性互补问题的牛顿KKT内点法

利用线性互补问题与二次规划之间的关系,推广了求解二次规划的KKT内点法,并用于线性互补问题,分析了推广算法的全局收敛性和局部收敛性.数值实验表明,算法对求解几类线性互补问题是有效的.     

线性互补问题的宽邻域预估校正算法

对线性互补问题提出了一种新的宽邻域预估校正算法,算法是基于经典线性规划路径跟踪算法的思想,将Maziar Salahi关于线性规划预估校正算法推广到线性互补问题中,给出了算法的具体迭代步骤并讨论了算法迭代复杂性,最后证明了算法具有多项式复杂性为O(ηlog(X~0)~Ts~0/ε)。     

非线性均衡问题一个超线性收敛的光滑逼近SQP算法

研究非线性均衡问题,引入一个磨光算子将原问题转化为光滑问题,并用此光滑问题来逼近原来的问题而求解.在每步迭代中,通过转轴运算,求解一个线性约束二次规划问题和显式修正方向来得到主方向,并通过一个显式公式来得到高阶修正方向使得算法避免Maratos效应.在不需要上层互补条件下证明了算法具有全局收敛性和强收敛性且具有超线性收敛速度.     

线性互补问题的一类新的带参数价值函数的阻尼牛顿法

本文给出了线性互补问题LCP(q ,M)的一类新的带参数光滑价值函数 ,基此价值函数提出了一种阻尼牛顿类算法 ,并证明了当M为P 矩阵时 ,该算法全局收敛且有限步终止 .通过数值实验说明了该算法高效可靠 .与互补问题的磨光方程组中所采用的带参数价值函数不同 ,这里的参数最终并不趋向于零 ,而是趋向于被称作解的乘子向量 (与凸非线性极小极大问题的Lagrange乘子完全一致 ) ,这一思想是本文作者首次提出来的 ,同时本文中所采用的阻尼牛顿类方法也有其独到之处 ,在互补问题的研究中有进一步发展的潜力     

基于核函数求解LCPs的全-Newton步不可行内点算法

本文对P_*(κ)线性互补问题设计了一种基于核函数的全-Newton步不可行内点算法,是对Mansouri等人提出的单调线性互补问题全-Newton步不可行内点算法的改进与推广.算法的主迭代由一个可行步和几个中心步构成且可行步采用小步校正.通过建立和应用一些新的技术性结果,证明了算法的多项式复杂性为O((1+2κ)~(3/2)(1og_2log_264(1+2κ))nlogmax{(x0)Ts0,||r0||}/ε),当k=0时,与当前单调线性互补问题的不可行内点算法最好的迭代复杂性界一致.最后,用Matlab数值实验验证了算法的可行性.     

二阶锥线性互补问题的广义模系矩阵分裂迭代算法

通过将二阶锥线性互补问题转化为等价的不动点方程,介绍了一种广义模系矩阵分裂迭代算法,并研究了该算法的收敛性.进一步,数值结果表明广义模系矩阵分裂迭代算法能够有效地求解二阶锥线性互补问题.     

二阶锥线性互补问题的低阶罚函数算法

本文研究了二阶锥线性互补问题的低阶罚函数算法.利用低阶罚函数算法将二阶锥线性互补问题转化为低阶罚函数方程组,获得了低阶罚函数方程组的解序列在特定条件下以指数速度收敛于二阶锥线性互补问题解的结果,推广了二阶锥线性互补问题的幂罚函数算法.数值实验结果验证了算法的有效性.     

一个解不等式约束优化问题的初始点任意而不需罚函数的SQP算法(英文)

本文构造了一解不等式约束优化问题的非单调SQP方法 ,与类似的算法比较 ,它有以下特点 :( 1 )初始点任意 ,并不用罚函数 ;( 2 )有限步后必产生可行点 ;( 3)在每次迭代 ,只需解一个二次规划子问题 ;( 4)不需要严格互补条件 ,在较弱的条件下 ,算法超线性收敛 .     

线性互补问题在宽邻域下的局部二次收敛算法

艾文宝(2004)的宽邻域算法弥补了内点法在理论和实践表现之间的差异.基于这个算法的优越性,将其推广到线性互补问题中.新算法在一次迭代中,采用两个方向的线性组合作为新方向,并以满步长到达下一个点.可以证明,该算法具有O(n~(1/2)L)的理论复杂度,这是迄今为止最好的复杂度结果.同时,在假设线性互补问题存在严格互补解的前提下,证明算法具有局部二次收敛性.最后,数值实验说明算法是有效的.     

基于PCP特征的和弦识别研究与探讨

讨论了音乐识别领域中和弦的四种不同识别方法,给出了基于PCP特征的和弦识别算法.使用PCP作为和弦的特征作为输入送至隐马尔可夫模型中训练,利用Baum-Welch算法估计模型参数,通过Viterbi算法得到正确和弦.通过实验获得了76%的识别率,验证了该算法的可行性.     

Deepest point of a polyhedron and linear programming

Abstract

The problem of finding a deepest point (a ball centre) of a polyhedron is studied. A finite combinatorial interior point method is presented for this problem which yields an algorithm for linear programming. We conjecture that this is a strongly polynomial algorithm. Meanwhile developing the algorithm, several auxiliary results were found; among others, Gorokh and Werner’s algorithm for linear inequalities is slightly extended. Our numerical experiments with the problem detected bugs in a linear interior point solver used in MATLAB 6 Optimization Toolbox.     

An Extension of Least Angle Regression Based on the Information Geometry of Dually Flat Spaces

We extend the least angle regression algorithm using the information geometry of dually flat spaces. The extended least angle regression algorithm is used for estimating parameters in generalized linear regression, and it can be also used for selecting explanatory variables. We use the fact that a model manifold of an exponential family is a dually flat space. In estimating parameters, curves corresponding to bisectors in the Euclidean space play an important role. Originally, the least angle regression algorithm is used for estimating parameters and selecting explanatory variables in linear regression. It is an efficient algorithm in the sense that the number of iterations is the same as the number of explanatory variables. We extend the algorithm while keeping this efficiency. However, the extended least angle regression algorithm differs significantly from the original algorithm. The extended least angle regression algorithm reduces one explanatory variable in each iteration while the original algorithm increases one explanatory variable in each iteration. We show results of the extended least angle regression algorithm for two types of datasets. The behavior of the extended least angle regression algorithm is shown. Especially, estimates of parameters become smaller and smaller, and vanish in turn.     

Nonmonotone Inexact Newton Method for the Extended Linear Complementarity Problem

This paper presents a nonmonotone inexact Newton-type method for the extended linear complementarity problem (ELCP). We first reformulate the optimization system of the ELCP problem into a system of smoothed equations. Then we solve this system by a nonmonotone inexact Newton-type algorithm. The global convergence is obtained and numerical tests for some classes of ELCP include linear complementarity, horizontal linear complementarity, and generalized linear complementarity problems are also given to show the e?ciency of the proposed algorithm.     

一类非单调线性互补问题的高阶仿射尺度算法

In this paper, a new interior point algorithm-high-order atone scaling for a class of nonmonotonic linear complementary problems is developed. On the basis of idea of primal-dual affine scaling method for linear programming , the search direction of our algorithm is obtained by a linear system of equation at each step . We show that, by appropriately choosing the step size, the algorithm has polynomial time complexity. We also give the numberical results of the algorithm for two test problems.     

求解一类广义线性规划的分裂算法

1　引　　言我们知道,描述常义线性规划问题的数学模型为:mincTxs.tAx=bx≥0　　在经济问题中,线性规划中的向量c往往表示为价格,而在许多实际规划问题中价格向量c往往会在一定范围内扰动.这时,我们可以考虑这样一类广义线性规划问题:minx{maxy∈YyTx}s.tAx=b　x∈X(1)其中,A∈Rm×n,b∈Rm,X={x∈Rn|x≥0},Y是Rn中的一个凸闭子集.有关广义线性规划问题的求解,何在文献[1]中作过一些讨论.我们通过对线性约束Ax=b引入乘子可得到广义线性规划问题(1)定义在X×Y×Rm上的Lagrange函数为:L(x,y,η)=yTx-ηT(Ax-b)(2)　　如果x*是(1)式的…     

求解一类非单调线性互补问题的宽邻域内点方法及其计算复杂性

对于一类非单调线性互补问题给出了一种新的算法——宽邻域内点算法，并讨论了其计算复杂性。     

Resolution method for mixed integer bi-level linear problems based on decomposition technique

In this article, we propose a new algorithm for the resolution of mixed integer bi-level linear problem (MIBLP). The algorithm is based on the decomposition of the initial problem into the restricted master problem (RMP) and a series of problems named slave problems (SP). The proposed approach is based on Benders decomposition method where in each iteration a set of variables are fixed which are controlled by the upper level optimization problem. The RMP is a relaxation of the MIBLP and the SP represents a restriction of the MIBLP. The RMP interacts in each iteration with the current SP by the addition of cuts produced using Lagrangian information from the current SP. The lower and upper bound provided from the RMP and SP are updated in each iteration. The algorithm converges when the difference between the upper and lower bound is within a small difference ε. In the case of MIBLP Karush–Kuhn–Tucker (KKT) optimality conditions could not be used directly to the inner problem in order to transform the bi-level problem into a single level problem. The proposed decomposition technique, however, allows the use of KKT conditions and transforms the MIBLP into two single level problems. The algorithm, which is a new method for the resolution of MIBLP, is illustrated through a modified numerical example from the literature. Additional examples from the literature are presented to highlight the algorithm convergence properties.     

单调线性互补问题的Mehrotra型预估-校正算法的迭代复杂性

Mehrotra型预估-校正算法是很多内点算法软件包的算法基础,但它的多项式迭代复杂性直到2007年才被Salahi等人证明.通过选择一个固定的预估步长及与Salahi文中不同的校正方向,本文把Salahi等人的算法拓展到单调线性互补问题,使得新算法的迭代复杂性为O(n log((x0)T s0/ε)),同时,初步的数值实验证明了新算法是有效的.     

Linear complementarity and discounted switching controller stochastic games

The class of discounted switching controller stochastic games can be solved in one step by a linear complementarity program (LCP). Following the proof of this technical result is a discussion of a special formulation and initialization of a standard LCP pivoting algorithm which has, in numerical experiments, always terminated in a complementary solution. That the LCP algorithm as formulated always finds a complementary solution has not yet been proven, but these theoretical and experimental results have the potential to provide an alternative proof of the ordered field property for these games. Numerical experimentation with the reformulated LCP is reviewed.