共查询到16条相似文献,搜索用时 62 毫秒
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非线性不等式约束最优化一个超线性与二次收敛的强次可行方法 总被引:1,自引:0,他引:1
本文讨论非线性不等式约束最优化问题,借助于序列线性方程组技术和强次可行方法思想,建立了问题的一个初始点任意的快速收敛新算法.在每次迭代中,算法只需解一个结构简单的线性方程组.算法的初始迭代点不仅可以是任意的,而且不使用罚函数和罚参数,在迭代过程中,迭代点列的可行性单调不减.在相对弱的假设下,算法具有较好的收敛性和收敛速度,即具有整体与强收敛性,超线性与二次收敛性.文中最后给出一些数值试验结果. 相似文献
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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 总被引:2,自引:0,他引:2
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. 相似文献
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不等式约束优化一个新的SQP算法 总被引:5,自引:0,他引:5
本文提出了一个处理不等式约束优化问题的新的SQP算法.和传统的SQP算法相比,该算法每步只需求解一个仅含等式约束的子二次规划,从而减少了算法的计算工作量.在适当的条件下,证明算法是全局收敛的且具有超线性收敛速度.数值实验表明算法是有效的. 相似文献
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非线性约束最优化一族超线性收敛的可行方法 总被引:5,自引:0,他引:5
本文建立求解非线性不等式约束最优化一族含参数的可行方法.算法每次迭代仅需解一个规模较小的二次规划.在一定的假设条件下,证明了算法族的全局收敛性和超线性收敛性. 相似文献
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本文针对非线性不等式约束优化问题,提出了-个可行内点型算法.在每次迭代中,基于积极约束集策略,该算法只需求解三个线性方程组,因而其计算工作量较小.在-般的条件下,证明了算法具有全局收敛及超线性收敛性. 相似文献
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A Globally and Superlinearly Convergent SQP Algorithm for Nonlinear Constrained Optimization 总被引:2,自引:0,他引:2
Based on a continuously differentiable exact penalty function and a regularization technique for dealing with the inconsistency of subproblems in the SQP method, we present a new SQP algorithm for nonlinear constrained optimization problems. The proposed algorithm incorporates automatic adjustment rules for the choice of the parameters and makes use of an approximate directional derivative of the merit function to avoid the need to evaluate second order derivatives of the problem functions. Under mild assumptions the algorithm is proved to be globally convergent, and in particular the superlinear convergence rate is established without assuming that the strict complementarity condition at the solution holds. Numerical results reported show that the proposed algorithm is promising. 相似文献
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Jin-Bao Jian Qing-Jie Hu Hai-Yan Zheng 《Numerical Functional Analysis & Optimization》2013,34(3-4):376-409
Combining the ideas of generalized projection and the strongly subfeasible sequential quadratic programming (SQP) method, we present a new strongly subfeasible SQP algorithm for nonlinearly inequality-constrained optimization problems. The algorithm, in which a new unified step-length search of Armijo type is introduced, starting from an arbitrary initial point, produces a feasible point after a finite number of iterations and from then on becomes a feasible descent SQP algorithm. At each iteration, only one quadratic program needs to be solved, and two correctional directions are obtained simply by explicit formulas that contain the same inverse matrix. Furthermore, the global and superlinear convergence results are proved under mild assumptions without strict complementarity conditions. Finally, some preliminary numerical results show that the proposed algorithm is stable and promising. 相似文献
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A SQP Method for Inequality Constrained Optimization 总被引:1,自引:0,他引:1
Ju-liang ZHANG Xiang-sun ZHANGInstitute of Applied Mathematics Academy of Mathematics System Sciences Chinese Academy of Sciences Beijing China 《应用数学学报(英文版)》2002,18(1):77-84
Abstract In this paper, a new SQP method for inequality constrained optimization is proposed and the globalconvergence is obtained under very mild conditions. 相似文献
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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. 相似文献
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Globally and Superlinearly Convergent QP-Free Algorithm for Nonlinear Constrained Optimization 总被引:2,自引:0,他引:2
A new, infeasible QP-free algorithm for nonlinear constrained optimization problems is proposed. The algorithm is based on a continuously differentiable exact penalty function and on active-set strategy. After a finite number of iterations, the algorithm requires only the solution of two linear systems at each iteration. We prove that the algorithm is globally convergent toward the KKT points and that, if the second-order sufficiency condition and the strict complementarity condition hold, then the rate of convergence is superlinear or even quadratic. Moreover, we incorporate two automatic adjustment rules for the choice of the penalty parameter and make use of an approximated direction as derivative of the merit function so that only first-order derivatives of the objective and constraint functions are used. 相似文献
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Jin-bao Jian Ran Quan Qing-jie Hu 《应用数学学报(英文版)》2007,23(3):395-410
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. 相似文献