求解障碍问题自由边界的一种非精确光滑方法

利用差分原理将一类数学物理障碍问题转化为线性互补问题.给出了求解大规模线性互补问题的一种非精确光滑算法,证明了该算法的适定性和全局收敛性.数值试验表明该方法能很好地求解此类障碍问题.     

求解一类逆鲁棒优化问题的牛顿型扰动方法

逆优化问题是指通过调整目标函数和约束中的某些参数使得已知的一个解成为参数调整后的优化问题的最优解.本文考虑求解一类逆鲁棒优化问题.首先,我们将该问题转化为带有一个线性等式约束,一个二阶锥互补约束和一个线性互补约束的极小化问题;其次,通过一类扰动方法来对转化后的极小化问题进行求解,然后利用带Armijo线搜索的非精确牛顿法求解每一个扰动问题.最后,通过数值实验验证该方法行之有效.     

二次规划逆问题的牛顿方法

针对二次规划逆问题,将其表达为带有互补约束的锥约束优化问题.借助于对偶理论,将问题转化为变量更少的线性互补约束非光滑优化问题.通过扰动的方法求解转化后的问题并证明了收敛性.采用非精确牛顿法求解扰动问题,给出了算法的全局收敛性与局部二阶收敛速度.最后通过数值实验验证了该算法的可行性.     

求解线性互补问题的Levenberg-Marquardt型算法

本文考虑了线性互补问题的求解算法,利用一类新的广义互补函数,把线性互补问题转化为非线性方程问题,并且利用Levenberg-Marquardt型算法对转化的问题进行了求解.在一般的假设条件下,给出了所给算法的收敛性分析.最后相关的数值结果表明所给的算法十分有效.     

一类广义多项式互补问题的光滑化共轭梯度法

本文研究了一类广义多项式互补问题,在一定条件下,证明了其有唯一解.通过极大极小转化技术,将此类广义多项式互补问题转化为光滑化无约束优化问题进行求解,并提出了一种新的光滑化共轭梯度法.在一定假设条件下,证明了该方法的全局收敛性.最后相关的数值实验表明了算法可以有效求解广义多项式互补问题.     

下层为凸标量优化的二层多目标规划问题的光滑化方法

以下层问题的最优性条件代替下层问题,将下层为凸标量优化的一类二层多目标规划问题转化为带互补约束的不可微多目标规划问题,采用扰动的Fischer-Burmeister函数对互补约束光滑化,得到了相应的光滑化多目标规划问题,分析了原问题的有效解与光滑化多目标规划问题有效解的关系,设计了求解该类二层多目标规划问题的光滑化算法,并分析了算法的收敛性.数值结果表明该光滑化方法是可行的.     

线性互补问题均衡解的存在形式与识别方法

研究了线性互补问题均衡解的存在形式与判定方法,给出了线性互补问题有解的充要条件,得到了带有几类特殊系数矩阵的线性互补问题的解的性质.在此基础上设计了求解线性互补问题均衡解的直接算法.     

解线性约束优化问题的新锥模型信赖域法

本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法.论文采用零空间技术消除了新锥模型子问题中的线性等式约束,用折线法求解转换后的子问题,并给出了解线性等式约束优化问题的信赖域方法.论文提出并证明了该方法的全局收敛性,并给出了该方法解线性等式约束优化问题的数值实验.理论和数值实验结果表明新锥模型信赖域方法是有效的,这给出了用新锥模型进一步研究非线性优化的基础.     

不确定信息多目标线性优化的鲁棒方法

研究不确定信息的多目标线性优化问题,其数据不能精确给出但是属于一个给定的集合.首先,采用鲁棒方法把该问题转化为一个确定的多目标优化问题.然后,给出此问题解存在的充分条件.最后,通过实例验证了用鲁棒方法解决不确定信息的多目标线性优化问题的有效性.     

改进的分块模方法求解对角占优线性互补问题

为了高效求解中小型线性互补问题,本文提出了改进的分块模方法,并证明了关于严格对角占优（对角元素均为正数）线性互补问题的收敛性.对于广义对角占优线性互补问题,先将其转化为严格对角占优线性互补问题,再采用改进的分块模方法求解.数值结果表明,改进的分块模方法在求解广义对角占优线性互补问题时在内迭代次数和计算时间上均明显优于分块模方法.     

Ellipsoids that contain all the solutions of a positive semi-definite linear complementarity problem

This paper deals with the LCP (linear complementarity problem) with a positive semi-definite matrix. Assuming that a strictly positive feasible solution of the LCP is available, we propose ellipsoids each of which contains all the solutions of the LCP. We use such an ellipsoid for computing a lower bound and an upper bound for each coordinate of the solutions of the LCP. We can apply the lower bound to test whether a given variable is positive over the solution set of the LCP. That is, if the lower bound is positive, we know that the variable is positive over the solution set of the LCP; hence, by the complementarity condition, its complement is zero. In this case we can eliminate the variable and its complement from the LCP. We also show how we efficiently combine the ellipsoid method for computing bounds for the solution set with the path-following algorithm proposed by the authors for the LCP. If the LCP has a unique non-degenerate solution, the lower bound and the upper bound for the solution, computed at each iteration of the path-following algorithm, both converge to the solution of the LCP.Supported by Grant-in-Aids for General Scientific Research (63490010) of The Ministry of Education, Science and Culture.Supported by Grant-in-Aids for Young Scientists (63730014) and for General Scientific Research (63490010) of The Ministry of Education, Science and Culture.     

多目标规划求解中修正权系数的方法

我们利用 p级数方法求解多目标规划问题 MOP,并用分层法的思想确定权系数 .求解多目标规划问题 MOP就相当于求解分层的多目标规划问题 L SP.这样 ,我们就可以确定这个函数的目标函数解 ,如果这个解不是满足决策者要求的 Pareto有效解 ,就改变原 MOP问题的权系数。我们就用这个迭代的方法求解多目标规划问题 MOP。     

一个求解水平线性互补问题的高阶可行内点算法的多项式复杂性

最近,Zhao和Sun提出了一个求解sufficient线性互补问题的高阶不可行内点算法.不需要严格互补解条件,他们的算法获得了高阶局部收敛率,但他们的文章没有报告多项式复杂性结果.本文我们考虑他们所给算法的一个简化版本,即考虑求解单调水平线性互补问题的一个高阶可行内点算法.我们证明了算法的迭代复杂性是     

A matrix-splitting method for symmetric affine second-order cone complementarity problems

The affine second-order cone complementarity problem (SOCCP) is a wide class of problems that contains the linear complementarity problem (LCP) as a special case. The purpose of this paper is to propose an iterative method for the symmetric affine SOCCP that is based on the idea of matrix splitting. Matrix-splitting methods have originally been developed for the solution of the system of linear equations and have subsequently been extended to the LCP and the affine variational inequality problem. In this paper, we first give conditions under which the matrix-splitting method converges to a solution of the affine SOCCP. We then present, as a particular realization of the matrix-splitting method, the block successive overrelaxation (SOR) method for the affine SOCCP involving a positive definite matrix, and propose an efficient method for solving subproblems. Finally, we report some numerical results with the proposed algorithm, where promising results are obtained especially for problems with sparse matrices.     

A Smoothing-Type Algorithm for Solving Linear Complementarity Problems with Strong Convergence Properties

In this paper, we construct an augmented system of the standard monotone linear complementarity problem (LCP), and establish the relations between the augmented system and the LCP. We present a smoothing-type algorithm for solving the augmented system. The algorithm is shown to be globally convergent without assuming any prior knowledge of feasibility/infeasibility of the problem. In particular, if the LCP has a solution, then the algorithm either generates a maximal complementary solution of the LCP or detects correctly solvability of the LCP, and in the latter case, an existing smoothing-type algorithm can be directly applied to solve the LCP without any additional assumption and it generates a maximal complementary solution of the LCP; and that if the LCP is infeasible, then the algorithm detect correctly infeasibility of the LCP. To the best of our knowledge, such properties have not appeared in the existing literature for smoothing-type algorithms. This work was partially supported by the National Natural Science Foundation of China (Grant No. 10571134), the Natural Science Foundation of Tianjin (Grant No. 07JCYBJC05200), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.     

A Non-Interior Continuation Algorithm for the P0 or P* LCP with Strong Global and Local Convergence Properties

We propose a non-interior continuation algorithm for the solution of the linear complementarity problem (LCP) with a P₀ matrix. The proposed algorithm differentiates itself from the current continuation algorithms by combining good global convergence properties with good local convergence properties under unified conditions. Specifically, it is shown that the proposed algorithm is globally convergent under an assumption which may be satisfied even if the solution set of the LCP is unbounded. Moreover, the algorithm is globally linearly and locally superlinearly convergent under a nonsingularity assumption. If the matrix in the LCP is a P_* matrix, then the above results can be strengthened to include global linear and local quadratic convergence under a strict complementary condition without the nonsingularity assumption.     

A FPTAS for computing a symmetric Leontief competitive economy equilibrium

In this paper, we consider a linear complementarity problem (LCP) arisen from the Nash and Arrow–Debreu competitive economy equilibria where the LCP coefficient matrix is symmetric. We prove that the decision problem, to decide whether or not there exists a complementary solution, is NP-complete. Under certain conditions, an LCP solution is guaranteed to exist and we present a fully polynomial-time approximation scheme (FPTAS) for approximating a complementary solution, although the LCP solution set can be non-convex or non-connected. Our method is based on approximating a quadratic social utility optimization problem (QP) and showing that a certain KKT point of the QP problem is an LCP solution. Then, we further show that such a KKT point can be approximated with a new improved running time complexity ${{O}((\frac{n^4}{\epsilon})\log\log(\frac{1}{\epsilon}))}$ arithmetic operation in accuracy ${\epsilon \in (0,1)}$ . We also report preliminary computational results which show that the method is highly effective. Applications in competitive market model problems with other utility functions are also presented, including global trading and dynamic spectrum management problems.     

A note on absolute value equations

In this note, we reformulate a system of absolute value equations (AVEs) as a standard linear complementarity problem (LCP) without any assumption. Utilizing some known results for the LCP, existence and convexity results for the solution set of the AVE are proposed.     

An interior penalty method for a finite-dimensional linear complementarity problem in financial engineering

In this work we study an interior penalty method for a finite-dimensional large-scale linear complementarity problem (LCP) arising often from the discretization of stochastic optimal problems in financial engineering. In this approach, we approximate the LCP by a nonlinear algebraic equation containing a penalty term linked to the logarithmic barrier function for constrained optimization problems. We show that the penalty equation has a solution and establish a convergence theory for the approximate solutions. A smooth Newton method is proposed for solving the penalty equation and properties of the Jacobian matrix in the Newton method have been investigated. Numerical experimental results using three non-trivial test examples are presented to demonstrate the rates of convergence, efficiency and usefulness of the method for solving practical problems.     

A continuation method for linear complementarity problems with P 0 matrix

In this article, we propose a new continuation method for solving the linear complementarity problem (LCP). The method solves one system of linear equations and carries out only a one-line search at each iteration. The continuation method is based on a modified smoothing function. The existence and continuity of a smooth path for solving the LCP with a P ₀ matrix are discussed. We investigate the boundedness of the iteration sequence generated by our continuation method under the assumption that the solution set of the LCP is nonempty and bounded. It is shown to converge to an LCP solution globally linearly and locally superlinearly without the assumption of strict complementarity at the solution under suitable assumption. In addition, some numerical results are also reported in this article.