欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为非强迫的最小化问题的解决方案的多方向性的平行 quasi-Newtonalgorithms 的一个类的集中性质在这篇论文被学习。Ateach 重复这些算法产生几不同的伪 -- 牛顿方向，然后使用线搜索同时沿着每个方向决定步长度。下一个重申被在功能减小的意义选择最低的点在这些小道点之中获得。不同的伪 -- 从 Broyden 家庭更新公式的牛顿被用来产生麻袋布矩阵近似的一个主要序列。基于 BFGS 和更新公式的修改 BFGS，全球、超级的线性集中结果被证明。iterate 顺序什么时候收敛到客观功能的本地最小，被观察所有 thequasi 牛顿方向 asymptotically 在方向和长度接近牛顿方向，并且因此超级线性集中的结果列在后面。
英文摘要：
Convergence properties of a class of multi-directional parallel quasi-Newton algorithms for the solution of unconstrained minimization problems are studied in this paper. At each iteration these algorithms generate several different quasi-Newton directions, and then apply line searches to determine step lengths along each direction, simultaneously. The next iterate is obtained among these trail points by choosing the lowest point in the sense of function reductions. Different quasi-Newton updating formulas from the Broyden family are used to generate a main sequence of Hessian matrix approximations. Based on the BFGS and the modified BFGS updating formulas, the global and superlinear convergence results are proved. It is observed that all the quasi-Newton directions asymptotically approach the Newton direction in both direction and length when the iterate sequence converges to a local minimum of the objective function, and hence the result of superlinear convergence follows.
同期刊论文项目
同项目期刊论文
期刊信息
位置：