共查询到18条相似文献,搜索用时 203 毫秒
1.
姜爱萍 《数学物理学报(A辑)》2011,31(1):103-116
该文提出一种QP-free可行域方法用来解满足光滑不等式约束的最优化问题.此方法把QP-free方法和3-1线性互补函数相结合一个等价于原约束问题的一阶KKT条件的方程组,并在此基础上给出解这个方程组的迭代算法. 这个方法的每一步迭代都可以看作是对求KKT条件解的牛顿或拟牛顿迭代的扰动,且在该方法中每一步的迭代均具有可行性. 该方法是可实行的且具有全局性, 且不需要严格互补条件、聚点的孤立性和积极约束函数梯度的线性独立等假设. 在与文献[2]中相同的适当条件下,此方法还具有超线性收敛性. 数值检验结果表示,该文提出的QP-free可行域方法是切实有效的方法. 相似文献
2.
3.
4.
5.
6.
7.
8.
提出一种新的序列线性方程组(SSLE)算法解非线性不等式约束优化问题.在算法的每步迭代,子问题只需解四个简化的有相同的系数矩阵的线性方程组.证明算法是可行的,并且不需假定聚点的孤立性、严格互补条件和积极约束函数的梯度的线性独立性得到算法的全局收敛性.在一定条件下,证明算法的超线性收敛率. 相似文献
9.
10.
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
11.
PIECEWISE LINEAR NCP FUNCTION FOR QP FREE FEASIBLE METHOD 总被引:3,自引:0,他引:3
Pu Dingguo Zhou Yan 《高校应用数学学报(英文版)》2006,21(3):289-301
In this paper,a QP-free feasible method with piecewise NCP functions is proposed for nonlinear inequality constrained optimization problems.The new NCP functions are piece- wise linear-rational,regular pseudo-smooth and have nice properties.This method is based on the solutions of linear systems of equation reformulation of KKT optimality conditions,by using the piecewise NCP functions.This method is implementable and globally convergent without assuming the strict complementarity condition,the isolatedness of accumulation points.Fur- thermore,the gradients of active constraints are not requested to be linearly independent.The submatrix which may be obtained by quasi-Newton methods,is not requested to be uniformly positive definite.Preliminary numerical results indicate that this new QP-free method is quite promising. 相似文献
12.
In this paper, a QP-free feasible method with piecewise NCP functions is proposed for nonlinear inequality constrained optimization problems. The new NCP functions are piecewise linear-rational, regular pseudo-smooth and have nice properties. This method is based on the solutions of linear systems of equation reformulation of KKT optimality conditions, by using the piecewise NCP functions. This method is implementable and globally convergent without assuming the strict complementarity condition, the isolatedness of accumulation points. Furthermore, the gradients of active constraints are not requested to be linearly independent. The submatrix which may be obtained by quasi-Newton methods, is not requested to be uniformly positive definite. Preliminary numerical results indicate that this new QP-free method is quite promising. 相似文献
13.
Zhou Yan Pu Dingguo 《高校应用数学学报(英文版)》2007,22(4):425-433
In this paper,a smoothing QP-free infeasible method is proposed for nonlinear inequality constrained optimization problems.This iterative method is based on the solution of nonlinear equations which is obtained by the multipliers and the smoothing Fisher-Burmeister function for the KKT first-order optimality conditions.Comparing with other QP-free methods, this method does not request the strict feasibility of iteration.In particular,this method is implementable and globally convergent without assuming the strict complementarity condition and the isolatedness of accumulation points.Furthermore,the gradients of active constraints are not requested to be linearly independent.Preliminary numerical results indicate that this smoothing QP-free infeasible method is quite promising. 相似文献
14.
Chang-yin Zhou Guo-ping He Yong-li Wang 《计算数学(英文版)》2006,24(5):591-608
In this paper, we propose a feasible QP-free method for solving nonlinear inequality constrained optimization problems. A new working set is proposed to estimate the active set. Specially, to determine the working set, the new method makes use of the multiplier information from the previous iteration, eliminating the need to compute a multiplier function. At each iteration, two or three reduced symmetric systems of linear equations with a common coefficient matrix involving only constraints in the working set are solved, and when the iterate is sufficiently close to a KKT point, only two of them are involved. Moreover, the new algorithm is proved to be globally convergent to a KKT point under mild conditions. Without assuming the strict complementarity, the convergence rate is superlinear under a condition weaker than the strong second-order sufficiency condition. Numerical experiments illustrate the efficiency of the algorithm. 相似文献
15.
Numerical test results are presented for solving smooth nonlinear programming problems with a large number of constraints, but a moderate number of variables. The active set method proceeds from a given bound for the maximum number of expected active constraints at an optimal solution, which must be less than the total number of constraints. A quadratic programming subproblem is generated with a reduced number of linear constraints from the so-called working set, which is internally changed from one iterate to the next. Only for active constraints, i.e., a certain subset of the working set, new gradient values must be computed. The line search is adapted to avoid too many active constraints which do not fit into the working set. The active set strategy is an extension of an algorithm described earlier by the author together with a rigorous convergence proof. Numerical results for some simple academic test problems show that nonlinear programs with up to 200,000,000 nonlinear constraints are efficiently solved on a standard PC. 相似文献
16.
1.IntroductionTheproblemconsideredinthispaperiswhereX={xER"laTx5hi,jEI={l,.'.,m}},ajeR"(jEI)areallcolumn*ThisresearchissupportedbytheNationalNaturalSciencesFoundationofChinaandNaturalSciencesFoundationofHunanProvince.vectors,hiERI(j6I)areallscalars,andf:R"-- Risacontinuouslydifferentiablefunction.Weonlyconsiderinequalityconstraintsheresinceanyequalitycanbeexpressedastwoinequalities.Withoutassumingregularityofthelinearconstraints,thereisnotanydifficultyinextendingtheresultstothegenera… 相似文献
17.
18.
Combining the norm-relaxed sequential quadratic programming (SQP) method and the idea of method of quasi-strongly sub-feasible directions (MQSSFD) with active set identification technique, a new SQP algorithm for solving nonlinear inequality constrained optimization is proposed. Unlike the previous work, at each iteration of the proposed algorithm, the norm-relaxed quadratic programming (QP) subproblem only consists of the constraints corresponding to an active identification set. Moreover, the high-order correction direction (used to avoid the Maratos effect) is yielded by solving a system of linear equations (SLE) which also includes only the constraints and their gradients corresponding to the active identification set, therefore, the scale and the computation cost of the high-order correction directions are further decreased. The arc search in our algorithm can effectively combine the initialization processes with the optimization processes, and the iteration points can get into the feasible set after a finite number of iterations. Furthermore, the arc search conditions are weaker than the previous work, and the computation cost is further reduced. The global convergence is proved under the Mangasarian–Fromovitz constraint qualification (MFCQ). If the strong second-order sufficient conditions are satisfied, then the active constraints are exactly identified by the identification set. Without the strict complementarity, superlinear convergence can be obtained. Finally, some elementary numerical results are reported. 相似文献