无需线搜索的并行非线性共轭梯度法

A Parallel Nonlinear Conjugate Gradient Method with No-line-search

摘要: 给出了一类无需线搜索的无约束最优化并行算法--并行非线性共轭梯度法(NLS-PNCG),用一个固定的公式来计算搜索步长,较常用的共轭梯度法计算量小.并且证明了目的的全局收敛性,给出了数值试验,结果显示NLS-PNCG优于线搜索的非线性共轭梯度法(NCG).

Abstract: A no-line-search parallel nonlinear conjugate gradient method(NLS-PNCG) for unconstrained optimization is proposed. In this method the step length is evaluated by a fixed formula. It is shown that the NLS-PNCG requires less computation and its performance is superior to those with line search on Deep Comp 6800.