Global Convergence of a Memory Gradient Method for Unconstrained Optimization
Global Convergence of a Memory Gradient Method for Unconstrained Optimization
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DOI：
10.1007/s10589-006-8719-z
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发表时间：
2006-11
影响因子：
2.2
通讯作者：
Yasushi Narushima;H. Yabe
中科院分区：
文献类型：
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作者：
Yasushi Narushima;H. Yabe
记忆梯度法用于无约束优化，特别是大规模优化问题。记忆梯度方法最早是由Miele和坎特雷尔(1969)和Cragg和Levy(1969)提出的。本文提出了一种新的记忆梯度法，该方法在每次迭代时生成目标函数的下降搜索方向。我们证明了当Wolfe条件在线搜索策略的框架内满足时，我们的方法是全局收敛的。数值结果表明，如果选择合适的参数，该方法对给定的标准测试问题是有效的。
Memory gradient methods are used for unconstrained optimization, especially large scale problems. The first idea of memory gradient methods was proposed by Miele and Cantrell (1969) and Cragg and Levy (1969). In this paper, we present a new memory gradient method which generates a descent search direction for the objective function at every iteration. We show that our method converges globally to the solution if the Wolfe conditions are satisfied within the framework of the line search strategy. Our numerical results show that the proposed method is efficient for given standard test problems if we choose a good parameter included in the method.