基于全牛顿步长求解凸二次规划问题的不可行内点算法

借助于全牛顿步长对凸二次规划问题提出了一种新的不可行内点算法.算法主要迭代由可行迭代步和中心路径邻域迭代步组成.其优点是线性搜寻方向是不需要的.最后证明算法迭代复杂性为O(nlogn/ε),与目前最好的不可行内点算法复杂性一致.     

线性权互补问题的新全牛顿步可行内点算法

基于一个连续可微函数,通过等价变换中心路径,给出求解线性权互补问题的一个新全牛顿步可行内点算法.该算法每步迭代只需求解一个线性方程组,且不需要进行线搜索.通过适当选取参数,分析了迭代点的严格可行性,并证明算法具有线性优化最好的多项式时间迭代复杂度.数值结果验证了算法的有效性.     

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.     

凸二次半定规划一个长步原始对偶路径跟踪算法

本文基于Nesterov-Todd方向,并引进中心路径测量函数以及原始对偶对数障碍函数,建立了一个求解凸二次半定规划的长步路径跟踪法.算法保证当迭代点落在中心路径附近时步长1被接受.算法至多迭代O(n|lnε|)次可得到一个ε最优解.论文最后报告了初步的数值试验结果.     

基于局部self-concordant障碍函数的全牛顿步多项式时间算法

由Nesterov和Nemirovski[4]创立的self-concordant障碍函数理论为解线性和凸优化问题提供了多项式时间内点算法.根据self-concordant障碍函数的参数,就可以分析内点算法的复杂性.在这篇文章中,我们介绍了基于核函数的局部self-concordant障碍函数,它在线性优化问题的中心路径及其邻域内满足self-concordant性质.通过求解此障碍函数的局部参数值,我们得到了求解线性规划问题的基于此局部Self-concordant障碍函数的纯牛顿步内点算法的理论迭代界.此迭代界与目前已知的最好的理论迭代界是一致的.     

基于A-极大单调算子的三步迭代算法

介绍了用三步迭代算法求解A-极大单调算子的不动点问题和用预解算子研究包含问题的解.同时给出了在某些条件下,三步迭代算法的收敛性.该文中的结论是在Noor,Huang的算法及Ram U.Verma的背景下启发得到.     

一类新的曲线搜索下的多步下降算法

提出一类新的曲线搜索下的多步下降算法,在较弱条件下证明了算法具有全局收敛性和线性收敛速率.算法利用前面多步迭代点的信息和曲线搜索技巧产生新的迭代点,收敛稳定,不用计算和存储矩阵,适于求解大规模优化问题.数值试验表明算法是有效的.     

基于(A,η)-极大单调算子的强收敛定理

采用三步迭代算法研究(A,η)-极大单调算子的不动点问题及用预解算子研究包含问题的解.同时给出了在相关条件下,由三步迭代算法迭代产生出的数列的强收敛性.文中的算法是在Noor,Huang[1]的两步算法产生的弱收敛定理及Ram U Verma[2]的关于解决包含问题解的方法的启发下得到.     

一个带固定步长的ODE型算法

提出了一种新的求解无约束优化问题的ODE型方法,其特点是:它在每次迭代时仅求解一个线性方程组系统来获得试探步;若该试探步不被接受,算法就沿着该试探步的方向求得下一个迭代点,其中步长通过固定公式计算得到.这样既避免了传统的ODE型算法中为获得可接受的试探步而重复求解线性方程组系统,又不必执行线搜索,从而减少了计算量.在适当的条件下,还证明了新算法的整体收敛性和局部超线性收敛性.数值试验结果表明:提出的算法是有效的.     

实子矩阵约束下矩阵方程AX=B的共轭梯度迭代解法

本文研究了实子矩阵约束下矩阵方程AX=B及其最佳逼近的共轭梯度迭代解法.首先运用矩阵分块将原方程AX=B转换为2个低阶方程,利用共轭梯度的思想构造迭代算法;然后证明了算法的有限步终止性;最后给出数值实例验证算法的有效性.     

A Strongly and Superlinearly Convergent SQP Algorithm for Optimization Problems with Linear Complementarity Constraints

This paper discusses a kind of optimization problem with linear complementarity constraints, and presents a sequential quadratic programming (SQP) algorithm for solving a stationary point of the problem. The algorithm is a modification of the SQP algorithm proposed by Fukushima et al. [Computational Optimization and Applications, 10 (1998), 5-34], and is based on a reformulation of complementarity condition as a system of linear equations. At each iteration, one quadratic programming and one system of equations needs to be solved, and a curve search is used to yield the step size. Under some appropriate assumptions, including the lower-level strict complementarity, but without the upper-level strict complementarity for the inequality constraints, the algorithm is proved to possess strong convergence and superlinear convergence. Some preliminary numerical results are reported.     

The largest step path following algorithm for monotone linear complementarity problems

Path-following algorithms take at each iteration a Newton step for approaching a point on the central path, in such a way that all the iterates remain in a given neighborhood of that path. This paper studies the case in which each iteration uses a pure Newton step with the largest possible reduction in complementarity measure (duality gap). This algorithm is known to converge superlinearly in objective values. We show that with the addition of a computationally trivial safeguard it achieves Q-quadratic convergence, and show that this behaviour cannot be proved by usual techniques for the original method. Research done while visiting Delft University of Technology, and supported in part by CAPES-Brazil.     

基于一类新方向的宽邻域路径跟踪内点算法

基于一类带有参数theta的新方向, 提出了求解单调线性互补问题的宽邻 域路径跟踪内点算法, 且当theta=1时即为经典牛顿方向. 当取theta为与问题规模 n无关的常数时, 算法具有O(nL)迭代复杂性, 其中L是输入数据的长度, 这与经典宽邻 域算法的复杂性相同; 当取theta=\sqrt{n/\beta\tau}时, 算法具有O(\sqrt{n}L)迭代复杂性, 这里的\beta, \tau是邻域参数, 这与窄邻域算法的复杂性相同. 这是首次研究包括经典宽邻域路径跟踪算法的一类内点算法, 给出了统一的算法框架和收敛性分析方法.     

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.     

An O（rL） Infeasible Interior-point Algorithm for Symmetric Cone LCP via CHKS Function

In this paper, we propose a theoretical framework of an infeasible interior-point algorithm for solving monotone linear cornplementarity problems over symmetric cones （SCLCP）. The new algorithm gets Newton-like directions from the Chen-Harker-Kanzow-Smale （CHKS） smoothing equation of the SCLCP. It possesses the following features： The starting point is easily chosen; one approximate Newton step is computed and accepted at each iteration; the iterative point with unit stepsize automatically remains in the neighborhood of central path; the iterative sequence is bounded and possesses （9（rL） polynomial-time complexity under the monotonicity and solvability of the SCLCP.     

An Interior Point Algorithm for Mixed Complementarity Nonlinear Problems

Nonlinear complementarity and mixed complementarity problems arise in mathematical models describing several applications in Engineering, Economics and different branches of physics. Previously, robust and efficient feasible directions interior point algorithm was presented for nonlinear complementarity problems. In this paper, it is extended to mixed nonlinear complementarity problems. At each iteration, the algorithm finds a feasible direction with respect to the region defined by the inequality conditions, which is also monotonic descent direction for the potential function. Then, an approximate line search along this direction is performed in order to define the next iteration. Global and asymptotic convergence for the algorithm is investigated. The proposed algorithm is tested on several benchmark problems. The results are in good agreement with the asymptotic analysis. Finally, the algorithm is applied to the elastic–plastic torsion problem encountered in the field of Solid Mechanics.     

A predictor-corrector interior-point algorithm for P ∗ ( κ ) -horizontal linear complementarity problem

We present a predictor-corrector path-following interior-point algorithm for \(P_*(\kappa )\) horizontal linear complementarity problem based on new search directions. In each iteration, the algorithm performs two kinds of steps: a predictor (damped Newton) step and a corrector (full Newton) step. The full Newton-step is generated from an algebraic reformulation of the centering equation, which defines the central path and seeks directions in a small neighborhood of the central path. While the damped Newton step is used to move in the direction of optimal solution and reduce the duality gap. We derive the complexity for the algorithm, which coincides with the best known iteration bound for \(P_*(\kappa )\) -horizontal linear complementarity problems.     

求解一类隐线性互补问题的投影MAOR算法

用MAOR迭代算法求解一类L-矩阵的隐线性互补问题.证明了由此算法产生的迭代序列的聚点是隐线性互补问题的解.并且当问题中的矩阵是M-矩阵时,算法产生的迭代序列单调收敛于隐互补问题的解.     

垂直线性互补问题的一步全局线性和局部二次收敛光滑Newton法

基于凝聚函数,提出一个求解垂直线性互补问题的光滑Newton法.该算法具有以下优点:(i)每次迭代仅需解一个线性系统和实施一次线性搜索;(ⅱ)算法对垂直分块P0矩阵的线性互补问题有定义且迭代序列的每个聚点都是它的解.而且,对垂直分块P₀+R₀矩阵的线性互补问题,算法产生的迭代序列有界且其任一聚点都是它的解;(ⅲ)在无严格互补条件下证得算法即具有全局线性收敛性又具有局部二次收敛性.许多已存在的求解此问题的光滑Newton法都不具有性质(ⅲ).     

An infeasible interior-point algorithm based on modified Nesterov and Todd directions for symmetric linear complementarity problem

We present an infeasible interior-point algorithm for symmetric linear complementarity problem based on modified Nesterov–Todd directions by using Euclidean Jordan algebras. The algorithm decreases the duality gap and the feasibility residual at the same rate. In this algorithm, we construct strictly feasible iterates for a sequence of perturbations of the given problem. Each main iteration of the algorithm consists of a feasibility step and a number of centring steps. The starting point in the first iteration is strictly feasible for a perturbed problem. The feasibility steps lead to a strictly feasible iterate for the next perturbed problem. By using centring steps for the new perturbed problem, a strictly feasible iterate is obtained to be close to the central path of the new perturbed problem. Furthermore, giving a complexity analysis of the algorithm, we derive the currently best-known iteration bound for infeasible interior-point methods.