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 .     

New sequential quadratic programming algorithm with consistent subproblems

One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done. The method proposed by De O. Pantoja J.F. A. and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form. A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and Local superlinear convergence. Project partly supported by the National Natural Science Foundation of China.     

Sequential Quadratic Programming Method for Volatility Estimation in Option Pricing

Our goal is to identify the volatility function in Dupire’s equation from given option prices. Following an optimal control approach in a Lagrangian framework, a globalized sequential quadratic programming (SQP) algorithm combined with a primal-dual active set strategy is proposed. Existence of local optimal solutions and of Lagrange multipliers is shown. Furthermore, a sufficient second-order optimality condition is proved. Finally, some numerical results are presented underlining the good properties of the numerical scheme.     

一个修正的SQP方法-滤子方法

序列二次规划方法（SQP）是解决非线性规划问题最有效的算法之一，但是当QP子问题不可行时算法可能会失败．而且线搜索中的罚参数的选择通常比较困难．在文献[1]中，SQP方法得到了修正，使得QP子问题可行．在本文中，我们利用滤子技术避免了罚函数的使用同时提出了带线搜索的滤子方法，最终保证了SQP方法总是可行的，而且得到了方法的全局收敛性．     

一类修改的带NCP函数信赖域滤子算法

本文针对非线性规划给出了一种修改的带NCP函数的信赖域滤子SQP算法,主要的修改之处是用NCP函数替代了滤子中约束违反度函数,而且进一步证明了这种修改的算法同样具有全局收敛性.     

A New Superlinearly Convergent SQP Algorithm for Nonlinear Minimax Problems

In this paper, the nonlinear minimax problems are discussed. By means of the Sequential Quadratic Programming （SQP）, a new descent algorithm for solving the problems is presented. At each iteration of the proposed algorithm, a main search direction is obtained by solving a Quadratic Programming （QP） which always has a solution. In order to avoid the Maratos effect, a correction direction is obtained by updating the main direction with a simple explicit formula. Under mild conditions without the strict complementarity, the global and superlinear convergence of the algorithm can be obtained. Finally, some numerical experiments are reported.     

On the superlinear local convergence of a filter-SQP method

Transition to superlinear local convergence is shown for a modified version of the trust-region filter-SQP method for nonlinear programming introduced by Fletcher, Leyffer, and Toint [8]. Hereby, the original trust-region SQP-steps can be used without an additional second order correction. The main modification consists in using the Lagrangian function value instead of the objective function value in the filter together with an appropriate infeasibility measure. Moreover, it is shown that the modified trust-region filter-SQP method has the same global convergence properties as the original algorithm in [8].Mathematics Subject Classification (2000): 90C55, 65K05, 90C30     

An improved SQP algorithm for inequality constrained optimization

In this paper, the feasible type SQP method is improved. A new algorithm is proposed to solve nonlinear inequality constrained problem, in which a new modified method is presented to decrease the computational complexity. It is required to solve only one QP subproblem with only a subset of the constraints estimated as active per single iteration. Moreover, a direction is generated to avoid the Maratos effect by solving a system of linear equations. The theoretical analysis shows that the algorithm has global and superlinear convergence under some suitable conditions. In the end, numerical experiments are given to show that the method in this paper is effective.This work is supported by the National Natural Science Foundation (No. 10261001) and Guangxi Science Foundation (No. 0236001 and 0249003) of China. Acknowledgement.We would like to thank one anonymous referee for his valuable comments and suggestions, which greatly improved the quality of this paper.     

Experiments with successive quadratic programming algorithms

There are many variants of successive quadratic programming (SQP) algorithms. Important issues include: the choice of either line search or trust region strategies; the QP formulation to be used; and how the QP is to be solved. Here, we consider the QP's proposed by Fletcher and Powell and discuss a specialized reduced-gradient procedure for solving them. A computer implementation is described, and the various options are compared on some well-known test problems. Factors influencing robustness and speed are identified.     

不等式约束优化的非单调可行信赖域-SQP算法

本文讨论不等式约束优化问题,给出一个信赖域方法与SQP方法相结合的新的可行算法,算法中采用了压缩技术,使得QP子问题产生的搜索方向尽可能为可行方向,并且采用了高阶校正的方法来克服算法产生的Maratos效应现象.在适当的条件下,证明了算法的全局收敛性和超线性收敛性.数值结果表明算法是有效的.