补偿随机规划的一种新数值方法

本文给出解决两阶段求援随机规划的一种新的数值方法.由于引进了新的逼近技术,该方法具有全局收敛性和局部超线性收敛性.     

Partitioned quasi-Newton methods for sparse nonlinear equations

In this paper, we present two partitioned quasi-Newton methods for solving partially separable nonlinear equations. When the Jacobian is not available, we propose a partitioned Broyden’s rank one method and show that the full step partitioned Broyden’s rank one method is locally and superlinearly convergent. By using a well-defined derivative-free line search, we globalize the method and establish its global and superlinear convergence. In the case where the Jacobian is available, we propose a partitioned adjoint Broyden method and show its global and superlinear convergence. We also present some preliminary numerical results. The results show that the two partitioned quasi-Newton methods are effective and competitive for solving large-scale partially separable nonlinear equations.     

A regularization smoothing method for second-order cone complementarity problem

In this paper, the second-order cone complementarity problem is studied. Based on the Fischer–Burmeister function with a perturbed parameter, which is also called smoothing parameter, a regularization smoothing Newton method is presented for solving the sequence of regularized problems of the second-order cone complementarity problem. Under proper conditions, the global convergence and local superlinear convergence of the proposed algorithm are obtained. Moreover, the local superlinear convergence is established without strict complementarity conditions. Preliminary numerical results suggest the effectiveness of the algorithm.     

一个新的共轭投影梯度算法及其超线性收敛性

利用共轭投影梯度技巧，结合SQP算法的思想，建立了一个具有显示搜索方向的新算法，在适当的条件下，证明算法是全局收敛和强收敛的，且具有超线性收敛性，最后数值实验表明算法是有效的。     

A nonmonotone globalization algorithm with preconditioned gradient path for unconstrained optimization

The aim of this paper is to incorporate the preconditioned gradient path in a nonmonotone stabilization algorithm for unconstrained optimization. The global convergence and locally superlinear convergence are established for this class of algorithms. Finally, we report in details the numerical results which show the effectiveness of the proposed algorithm.     

一个带有线搜索的新信赖域方法及其收敛性

本文利用一个修正的BFGS公式,提出了一个结合Armijo线搜索条件技术的BFGS信赖域方法,并在一定条件下证明了该方法的全局收敛性和超线性收敛性.初步的数值实验结果表明该方法是有效的.     

线性方程组逼近双障碍问题

针对双障碍问题,本文提出了与其等价的B可微方程的类Broyden算法,并 在一定条件下证明了该算法的全局收敛性和超线性收敛性.经数值实验表明该算法是 有效的.     

一类全局收敛的共轭投影梯度法及其超线性收敛性

一类全局收敛的共轭投影梯度法及其超线性收敛性时贞军（曲阜师范大学运筹学研究所）ＡＣＬＡＳＳＯＦＧＬＯＢＡＬＣＯＮＶＥＲＧＥＮＴＣＯＮＪＵＧＡＴＥＰＲＯＪＥＣＴＩＯＮＧＲＡＤＩＥＮＴＭＥＴＨＯＤＡＮＤＩＴＳＳＵＰＥＲＬＩＮＥＡＲＣＯＮＶＥＲＧＥＮＣＥＲ...     

求解非线性互补问题的一类光滑Broyden-like方法

非线性互补问题可以等价地转换为光滑方程组来求解. 基于一种新的非单调线搜索准则, 提出了求解非线性互补问题等价光滑方程组的一类新的非单调光滑 Broyden-like 算法.在适当的假设条件下, 证明了该算法的全局收敛性与局部超线性收敛性. 数值实验表明所提出的算法是有效的.     

二阶锥规划一个超线性收敛的非内部连续化算法

基于非光滑向量值最小函数的一个新光滑函数, 建立了二阶锥规划一个超线性收敛的非内部连续化算法. 该算法的特点如下: 首先, 初始点任意; 其次, 每次迭代只需求解一个线性方程组即可得到搜索方向; 最后, 在无严格互补假设下, 获得算法的全局收敛性、强收敛性和超线性收敛性. 数值结果表明算法是有效的.     

Minimax问题的一个滤子算法

本文提出一个求解不等式约束的Minimax问题的滤子算法,结合序列二次规划方法,并利用滤子以避免罚函数的使用.在适当的条件下,证明了此方法的全局收敛性及超线性收敛性.数值实验表明算法是有效的.     

Conjugate gradient path method without line search technique for derivative-free unconstrained optimization

In this paper, we propose conjugate gradient path method for solving derivative-free unconstrained optimization. The iterative direction is obtained by constructing and solving quadratic interpolation model of the objective function with conjugate gradient methods. The global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, the numerical results are reported to show the effectiveness of the proposed algorithm.     

超线性与二次收敛的序列方程组可行方法

本文讨论不等式约束规划问题,给出一个线性方程组与辅助方向相结合的新可行算法,算法用一种新型的直线搜索产生步长.在一定条件下,当k充分大后,求方向dk每次只需解一个线性方程组.文中证明了算法的全局收敛性与超线性的收敛速度以及二次收敛性,并给出了方法初步的数值试验.     

非线性不等式约束最优化快速收敛的可行信赖域算法

In this paper,by combining the trust region technique with the generalized gradient projection.a new trust region algorithm with feasible iteration points is presented for nonlinear inequality constrained optimization,and its trust region is a general compact set containing the origion as an inteior point.No penalty function is used in the algorithm,and it is feasible descent .Under suitable assumptions,the algorithm is proved to possess global and strong convergence as well as superlinear and quadratic convergence.Some numerical results are reported.     

A smoothing inexact Newton method for nonlinear complementarity problems

In this article, we propose a new smoothing inexact Newton algorithm for solving nonlinear complementarity problems (NCP) base on the smoothed Fischer-Burmeister function. In each iteration, the corresponding linear system is solved only approximately. The global convergence and local superlinear convergence are established without strict complementarity assumption at the NCP solution. Preliminary numerical results indicate that the method is effective for large-scale NCP.     

一类求解非凸函数极小的修正Broyden算法

本文研究了求解非凸函数极小的数值方法,提出了一类求解非凸函数极小的修正Broyden算法,并证明了所提出的修正Broyden算法是全局收敛和q-超线性收敛的.     

A parallel inexact newton method for stochastic programs with recourse

A parallel inexact Newton method with a line search is proposed for two-stage quadratic stochastic programs with recourse. A lattice rule is used for the numerical evaluation of multi-dimensional integrals, and a parallel iterative method is used to solve the quadratic programming subproblems. Although the objective only has a locally Lipschitz gradient, global convergence and local superlinear convergence of the method are established. Furthermore, the method provides an error estimate which does not require much extra computation. The performance of the method is illustrated on a CM5 parallel computer.This work was supported by the Australian Research Council and the numerical experiments were done on the Sydney Regional Centre for Parallel Computing CM5.     

不等式约束优化一个具有超线性收敛的可行序列二次规划算法

建立了一个新的SQP算法,提出了一阶可行条件这一新概念．对已有SQP型算法进行改进,减少计算工作量,证明了算法具有全局收敛及超线性收敛性．数值实验表明算法是有效的．     

A ROBUST SQP METHOD FOR OPTIMIZATION WITH INEQUALITY CONSTRAINTS

A new algorithm for inequality constrained optimization is presented, which solves a linear programming subproblem and a quadratic subproblem at each iteration. The algorithm can circumvent the difficulties associated with the possible inconsistency of QP subproblem of the original SQP method. Moreover, the algorithm can converge to a point which satisfies a certain first-order necessary condition even if the original problem is itself infeasible. Under certain condition, some global convergence results are proved and local superlinear convergence results are also obtained. Preliminary numerical results are reported.     

A new trust region method for nonlinear equations

In this paper, a new trust region method for the system of nonlinear equations is presented in which the determining of the trust region radius incorporates the information of its natural residual. The global convergence is obtained under mild conditions. Unlike traditional trust region method, the superlinear convergence of the method is proven under the local error bound condition. This condition is weaker than the nondegeneracy assumption which is necessary for superlinear convergence of traditional trust region method. We also propose an approximate algorithm for the trust region subproblem. Preliminary numerical experiments are reported. Acknowledgements.The authors are indebted to our supervisor, Professor Y.-X. Yuan, for his excellent guidance and Jorge J. Moré for his subroutine. And we would like to thank the referees for their valuable suggestions and comments.