共查询到10条相似文献,搜索用时 62 毫秒
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A rank-one algorithm is presented for unconstrained function minimization. The algorithm is a modified version of Davidon's variance algorithm and incorporates a limited line search. It is shown that the algorithm is a descent algorithm; for quadratic forms, it exhibits finite convergence, in certain cases. Numerical studies indicate that it is considerably superior to both the Davidon-Fletcher-Powell algorithm and the conjugate-gradient algorithm. 相似文献
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针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率. 相似文献
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A descent algorithm for nonsmooth convex optimization 总被引:1,自引:0,他引:1
Masao Fukushima 《Mathematical Programming》1984,30(2):163-175
This paper presents a new descent algorithm for minimizing a convex function which is not necessarily differentiable. The
algorithm can be implemented and may be considered a modification of the ε-subgradient algorithm and Lemarechal's descent
algorithm. Also our algorithm is seen to be closely related to the proximal point algorithm applied to convex minimization
problems. A convergence theorem for the algorithm is established under the assumption that the objective function is bounded
from below. Limited computational experience with the algorithm is also reported. 相似文献
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A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION 总被引:1,自引:0,他引:1
Peng Yehui Liu Zhenhai 《高校应用数学学报(英文版)》2005,20(4):491-498
In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don't bear. 相似文献
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In this paper, we present a long-step primal path-following algorithm and prove its global convergence under usual assumptions. It is seen that the short-step algorithm is a special case of the long-step algorithm for a specific selection of the parameters and the initial solution. Our theoretical result indicates that the long-step algorithm is more flexible. Numerical results indicate that the long-step algorithm converges faster than the short-step algorithm. 相似文献
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提出了一种凸组合共轭梯度算法,并将其算法应用到ARIMA模型参数估计中.新算法由改进的谱共轭梯度算法与共轭梯度算法作凸组合构造而成,具有下述特性:1)具备共轭性条件;2)自动满足充分下降性.证明了在标准Wolfe线搜索下新算法具备完全收敛性,最后数值实验表明通过调节凸组合参数,新算法更加快速有效,通过具体实例证实了模型的显著拟合效果. 相似文献
9.
P. P. B. Eggermont 《Applied Mathematics and Optimization》1999,39(1):75-91
We study a modification of the EMS algorithm in which each step of the EMS algorithm is preceded by a nonlinear smoothing
step of the form , where S is the smoothing operator of the EMS algorithm. In the context of positive integral equations (à la positron emission tomography)
the resulting algorithm is related to a convex minimization problem which always admits a unique smooth solution, in contrast
to the unmodified maximum likelihood setup. The new algorithm has slightly stronger monotonicity properties than the original
EM algorithm. This suggests that the modified EMS algorithm is actually an EM algorithm for the modified problem. The existence
of a smooth solution to the modified maximum likelihood problem and the monotonicity together imply the strong convergence
of the new algorithm. We also present some simulation results for the integral equation of stereology, which suggests that
the new algorithm behaves roughly like the EMS algorithm.
Accepted 1 April 1997 相似文献
10.
一种改进的蚁群算法及其在TSP中的应用 总被引:2,自引:0,他引:2
蚁群算法是一种求解复杂组合优化问题的新的拟生态算法,也是一种基于种群的启发式仿生进化算法,属于随机搜索算法的一种,并用于较好地解决TSP问题.然而此算法也有它自己的缺陷,如易于陷入局部优化、搜索时间长等.通过对基本蚁群算法的介绍及相关因素的分析,提出了一种改进的蚁群算法,用于解决TSPLAB问题的10个问题,并与参考文献中的F-W、NCSOM、ASOM算法进行比较,计算机仿真结果表明了改进算法的有效性.如利用改进的蚁群算法解决lin105问题,其最优解为14382.995933(已知最优解为14379),相对误差是0.0209%,计算出的最小值几乎接近于已知最优解. 相似文献