General Information

In this paper, we propose and analyze an SQP-type method for solving linearly constrained convex minimization problems where the objective functions are too complex to be evaluated exactly. Some basic results for global convergence and local superlinear convergence are obtained according to the properties of the approximation sequence. We illustrate the applicability of our approach by proposing a new method for solving two-stage stochastic programs with fixed recourse.     

COMPOSITE-STEP LIKE FILTER METHODS FOR EQUALITY CONSTRAINT PROBLEMS

In a composite-step approach, a step sk is computed as the sum of two components vk and hk. The normal component vk, which is called the vertical step, aims to improve the linearized feasibility, while the tangential component hk, which is also called horizontal step, concentrates on reducing a model of the merit functions. As a filter method, it reduces both the infeasibility and the objective function. This is the same property of these two methods. In this paper, one concerns the composite-step like filter approach. That is, a step is tangential component hk if the infeasibility is reduced. Or else, sk is a compositestep composed of normal component Vk and tangential component hk.     

用序列二次规划法设计ICF 靶区光路

 运用序列二次规划法设计了靶区光路。由ICF 柱面靶间接驱动实验对多路激光束排布的要求, 建立靶区数学模型, 用本文设计结果构筑光路排布线框实体。该三维线框以特征线代表60路激光驱动光束实体, 以特征线段的转折点代表240面反射镜的空间位置。     

空间望远镜分块式主镜共相位调整的优化算法研究

以应用主动控制系统进行分块镜共相位调整的大口径空间望远镜为研究对象,分析了分块式主镜致动器位移与相位误差之间的对应关系以及共相位调整的目标。研究采用一种求解非线性约束问题的序列二次规划法优化求解主镜致动器的位移控制指令,校正分块式主镜相位误差。数学仿真结果表明,应用序列二次规划法对分块式主镜进行共相位调整优化,控制精度得到了很大地提高。     

A feasible SQP method for nonlinear programming

In this paper, a new SQP algorithm is presented to solve the general nonlinear programs with mixed equality and inequality constraints. Quoted from P. Spellucci (see [9]), this method maybe be named sequential equality constrained quadratic programming (SECQP) algorithm. Per single iteration, based on an active set strategy ( see [9]), this SECQP algorithm requires only to solve equality constrained quadratic programming subproblems or system of linear equations. The theoretical analysis shows that global and superlinear convergence can be induced under some suitable conditions.     

Adaptive Algorithm for Constrained Least-Squares Problems

This paper is concerned with the implementation and testing of an algorithm for solving constrained least-squares problems. The algorithm is an adaptation to the least-squares case of sequential quadratic programming (SQP) trust-region methods for solving general constrained optimization problems. At each iteration, our local quadratic subproblem includes the use of the Gauss–Newton approximation but also encompasses a structured secant approximation along with tests of when to use this approximation. This method has been tested on a selection of standard problems. The results indicate that, for least-squares problems, the approach taken here is a viable alternative to standard general optimization methods such as the Byrd–Omojokun trust-region method and the Powell damped BFGS line search method.     

A NEW INEXACT SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM

This paper represents an inexact sequential quadratic programming (SQP) algorithm which can solve nonlinear programming (NLP) problems. An inexact solution of the quadratic programming subproblem is determined by a projection and contraction method such that only matrix-vector product is required. Some truncated criteria are chosen such that the algorithm is suitable to large scale NLP problem. The global convergence of the algorithm is proved.     

Sequential quadratic programming with a flexible step acceptance strategy

A new sequential quadratic programming (SQP) method for nonlinear inequality constrained optimization is proposed. The aim of this paper is to promote global convergence for SQP methods using a flexible step acceptance strategy which combines merit functions and filter techniques. Global convergence is proved under some reasonable assumptions and preliminary numerical results are reported.     

解非线性规划问题的不精确线性搜索SQP滤子方法

本文用序列二次规划方法(SQP)结合Wolfe-Powell不精确线性搜索准则求解非线性规划问题.Wolfe-Powell准则是一种能够使目标函数获得充分下降而运行时间较省的确定步长方法.不精确线性搜索滤子方法比较其它结合精确线性搜索和信赖域方法求解问题的滤子方法更灵活更易实现.如果目标函数的预测下降量为负,我们的工作将主要利用可行恢复项改善可行性.一般条件下,本文提出的算法较易实现,且具有全局收敛性.数值试验显示了算法的有效性.     

线性约束凸规划内点法及其修正算法

凸规划的内点算法是目前较热门的课题之一，参考资料[2]；[3]等均给出了较深入的研究．本文在参考前人的工作前提下，提出了带线性约束凸规划的内点算法结论（定理2．8）及相应算法．另外，木文定义了偏移因子，偏移因子对的概念，对下降方向作出了修正，并给出了相关算法．