共查询到17条相似文献,搜索用时 140 毫秒
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提出了求解非线性不等式约束优化问题的一个可行序列线性方程组算法. 在每次迭代中, 可行下降方向通过求解两个线性方程组产生, 系数矩阵具有较好的稀疏性. 在较为温和的条件下, 算法具有全局收敛性和强收敛性, 数值试验表明算法是有效的. 相似文献
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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. 相似文献
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本基于离散技术,给出了任意初始点下的半无限规划的一个序列线性方程组算法和算法的全局收敛性的证明。并在一定的假设下,证明了算法的一步超线性收敛性。 相似文献
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给出了解病态线性方程组的一种新的Jacobi迭代算法,并证明了算法的收敛性;通过具体算例说明了算法的实用性和有效性. 相似文献
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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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提出了一种新的求解无约束优化问题的ODE型方法,其特点是:它在每次迭代时仅求解一个线性方程组系统来获得试探步;若该试探步不被接受,算法就沿着该试探步的方向求得下一个迭代点,其中步长通过固定公式计算得到.这样既避免了传统的ODE型算法中为获得可接受的试探步而重复求解线性方程组系统,又不必执行线搜索,从而减少了计算量.在适当的条件下,还证明了新算法的整体收敛性和局部超线性收敛性.数值试验结果表明:提出的算法是有效的. 相似文献
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针对约束块可分的最优化问题,引入序列线性方程组方法和有效集策略,提出了一个求解约束块可分优化问题的QP-free型并行变量分配(PVD)算法.算法中用三个系数具有对称结构的线性方程组来代替PVD算法中的二次规划问题以求解线搜索方向,避免了约束不相容,减小了计算量.并且算法不要求约束是凸的.最后证明了QP-free型PVD算法的全局收敛性. 相似文献
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Based on a new efficient identification technique of active constraints introduced in this paper, a new sequential systems of linear equations (SSLE) algorithm generating feasible iterates is proposed for solving nonlinear optimization problems with inequality constraints. In this paper, we introduce a new technique for constructing the system of linear equations, which recurs to a perturbation for the gradients of the constraint functions. At each iteration of the new algorithm, a feasible descent direction is obtained by solving only one system of linear equations without doing convex combination. To ensure the global convergence and avoid the Maratos effect, the algorithm needs to solve two additional reduced systems of linear equations with the same coefficient matrix after finite iterations. The proposed algorithm is proved to be globally and superlinearly convergent under some mild conditions. What distinguishes this algorithm from the previous feasible SSLE algorithms is that an improving direction is obtained easily and the computation cost of generating a new iterate is reduced. Finally, a preliminary implementation has been tested. 相似文献
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In this paper, an improved feasible QP-free method is proposed to solve nonlinear inequality constrained optimization problems. Here, a new modified method is presented to obtain the revised feasible descent direction. In view of the computational cost, the most attractive feature of the new algorithm is that only one system of linear equations is required to obtain the revised feasible descent direction. Thereby, per single iteration, it is only necessary to solve three systems of linear equations with the same coefficient matrix. In particular, without the positive definiteness assumption on the Hessian estimate, the proposed algorithm is still global convergence. Under some suitable conditions, the superlinear convergence rate is obtained. 相似文献
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基于一个有效约束识别技术, 给出了具有不等式约束的非线性最优化问题的一个可行SSLE算法. 为获得搜索方向算法的每步迭代只需解两个或三个具有相同系数矩阵的线性方程组. 在一定的条件下, 算法全局收敛到问题的一个KKT点. 没有严格互补条件, 在比强二阶充分条件弱的条件下算法具有超线性收敛速度. 相似文献
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《Optimization》2012,61(1):101-131
In this article, non-linear minimax problems with general constraints are discussed. By means of solving one quadratic programming an improved direction is yielded and a second-order correction direction can also be at hand via one system of linear equations. So a new algorithm for solving the discussed problems is presented. In connection with a special merit function, the generalized monotone line search is used to yield the step size at each iteration. Under mild conditions, we can ensure global and superlinear convergence. Finally, some numerical experiments are operated to test our algorithm, and the results demonstrate that it is promising. 相似文献
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倪勤 《高等学校计算数学学报(英文版)》1997,(1)
In this paper we report a sparse truncated Newton algorithm for handling large-scale simple bound nonlinear constrained minimixation problem. The truncated Newton method is used to update the variables with indices outside of the active set, while the projected gradient method is used to update the active variables. At each iterative level, the search direction consists of three parts, one of which is a subspace truncated Newton direction, the other two are subspace gradient and modified gradient directions. The subspace truncated Newton direction is obtained by solving a sparse system of linear equations. The global convergence and quadratic convergence rate of the algorithm are proved and some numerical tests are given. 相似文献
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王建宏 《数学的实践与认识》2011,41(12)
系统和控制理论中许多重要的问题,都可转化为具有线性目标函数、线性矩阵不等式约束的LMI优化问题,从而使其在数值上易于求解.本文给出一种求解LMI优化问题的原对偶中心路径算法,该算法利用牛顿方法求解中心路径方程得到牛顿系统,并将该牛顿系统对称化以避免得到非对称化的搜索方向.文章详细分析了算法的计算复杂性. 相似文献
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Qing-jie Hu Yu Chen Nei-ping Chen Xue-quan Li 《Journal of Mathematical Analysis and Applications》2009,360(1):211-222
In this paper, a modified nonmonotone line search SQP algorithm for nonlinear minimax problems is presented. During each iteration of the proposed algorithm, a main search direction is obtained by solving a reduced quadratic program (QP). In order to avoid the Maratos effect, a correction direction is generated by solving the reduced system of linear equations. Under mild conditions, the global and superlinear convergence can be achieved. Finally, some preliminary numerical results are reported. 相似文献