共查询到20条相似文献,搜索用时 89 毫秒
1.
《数学的实践与认识》2013,(24)
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. 相似文献
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非线性约束条件下的SQP可行方法 总被引:9,自引:0,他引:9
本文对非线性规划问题给出了一个具有一步超线性收敛速度的可行方法。由于此算法每步迭代均在可行域内进行,并且每步迭代只需计算一个二次子规划和一个逆矩阵,因而算法具有较好的实用价值。本文还在较弱的条件下证明了算法的全局收敛和一步超线性收敛性。 相似文献
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对约束优化问题给出了一类光滑罚算法.它是基于一类光滑逼近精确罚函数 l_p(p\in(0,1]) 的光滑函数 L_p 而提出的.在非常弱的条件下, 建立了算法的一个摄动定理, 导出了算法的全局收敛性.特别地, 在广义Mangasarian-Fromovitz约束规范假设下, 证明了当 p=1 时, 算法经过有限步迭代后, 所有迭代点都是原问题的可行解; p\in(0,1) 时,算法经过有限迭代后, 所有迭代点都是原问题可行解集的内点. 相似文献
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本文研究了P(K)-阵线性互补问题宽邻域高阶内点算法.利用线性规划的原始-对偶仿射尺度算法来确定迭代方向,得到了算法的收敛性及迭代复杂性,其算法是有效可行的. 相似文献
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对水平线性互补问题提出了一种广义中心路径跟踪算法.任意的原始-对偶可行内点均可作为算法的初始点.每步迭代选择“仿射步”与“中心步”的凸组合为新的迭代方向,采用使对偶间隙尽可能减小的最大步长.算法的迭代复杂性为O(√nL). 相似文献
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在Hilbert空间中,为了研究分裂可行问题迭代算法的强收敛性,提出了一种新的CQ算法.首先利用CQ算法构造了一个改进的Halpern迭代序列; 然后通过把分裂可行问题转化为算子不动点, 在较弱的条件下, 证明了该序列强收敛到分裂可行问题的一个解. 推广了Wang和Xu的有关结果. 相似文献
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提出了一种解非线性规划问题的修改的非单调线搜索算法,并给出了它的全局收敛性证明.不需要用罚函数作为价值函数,也不用滤子和可行性恢复阶段.该算法是基于多目标优化的思想一个迭代点被接受当且仅当目标函数值或是约束违反度函数值有充分的下降.数值结果与LANCELOT作了比较,表明该算法是可靠的. 相似文献
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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. 相似文献
13.
Nonmonotone line search for minimax problems 总被引:7,自引:0,他引:7
It was recently shown that, in the solution of smooth constrained optimization problems by sequential quadratic programming (SQP), the Maratos effect can be prevented by means of a certain nonmonotone (more precisely, three-step or four-step monotone) line search. Using a well-known transformation, this scheme can be readily extended to the case of minimax problems. It turns out however that, due to the structure of these problems, one can use a simpler scheme. Such a scheme is proposed and analyzed in this paper. Numerical experiments indicate a significant advantage of the proposed line search over the Armijo search.This research was supported in part by NSF Engineering Research Centers Program No. NSFD-CDR-88-03012, by NSF Grant No. DMC-88-15996, and by a grant from the Westinghouse Corporation. 相似文献
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提出了—个求解非线性互补约束均衡问题的滤子SQP算法.借助Fischer-Burmeister函数把均衡约束转化为—个非光滑方程组,然后利用逐步逼近和分裂思想,给出—个与原问题近似的一般的约束优化.引入滤子思想,避免了罚函数法在选择罚因子上的困难.在适当的条件下证明了算法的全局收敛性,部分的数值结果表明算法是有效的. 相似文献
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An SQP-filter method for inequality constrained optimization and its global convergence 总被引:1,自引:0,他引:1
Xiangling Wang Zhibin ZhuShuangyong Zuo Qingqun Huang 《Applied mathematics and computation》2011,217(24):10224-10230
In this paper, we combine the filter technique with a modified sequential quadratic programming (SQP) method. The optimization solution is obtained by reducing step length, which is obtained by an exact linear search. Furthermore, this method can start with an infeasible initial point. The method uses a filter to promote global convergence. 相似文献
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倪勤 《高等学校计算数学学报(英文版)》2002,11(1):1-12
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. 相似文献
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Juliang Zhang+ 《计算数学(英文版)》2003,(2)
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. 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
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In this paper, the augmented Lagrangian SQP method is considered for the numerical solution of optimization problems with equality constraints. The problem is formulated in a Hilbert space setting. Since the augmented Lagrangian SQP method is a type of Newton method for the nonlinear system of necessary optimality conditions, it is conceivable that q-quadratic convergence can be shown to hold locally in the pair (x, ). Our interest lies in the convergence of the variable x alone. We improve convergence estimates for the Newton multiplier update which does not satisfy the same convergence properties in x as for example the least-square multiplier update. We discuss these updates in the context of parameter identification problems. Furthermore, we extend the convergence results to inexact augmented Lagrangian methods. Numerical results for a control problem are also presented. 相似文献
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C. Ling L. Q. Qi G. L. Zhou S. Y. Wu 《Journal of Optimization Theory and Applications》2006,129(1):147-164
The semi-infinite programming (SIP) problem is a program with infinitely many constraints. It can be reformulated as a nonsmooth
nonlinear programming problem with finite constraints by using an integral function. Due to the nondifferentiability of the
integral function, gradient-based algorithms cannot be used to solve this nonsmooth nonlinear programming problem. To overcome
this difficulty, we present a robust smoothing sequential quadratic programming (SQP) algorithm for solving the nonsmooth
nonlinear programming problem. At each iteration of the algorthm, we need to solve only a quadratic program that is always
feasible and solvable. The global convergence of the algorithm is established under mild conditions. Numerical results are
given.
Communicated by F. Giannessi
His work was supported by the Hong Kong Research Grant Council
His work was supported by the Australian Research Council. 相似文献