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此为A new class of supermemory gradient methods数学文献的摘要
Abstract
In this paper we propose a new class of supermemory gradient methods for unconstrained optimization problems. Trust
region approach is used in the new algorithms to guarantee the global convergence. In each iteration, the new algorithms
generate a suitable trust region radius automatically and obtain the next iterate by solving a simple subproblem. These
algorithms converge stably and averagely due to using more iterative information at each iteration, and can be reduced
to quasi-Newton methods when the iterate is close to the optimal solution. Numerical results show that this new class
of supermemory gradient methods is effective in practical computation.
2006 Elsevier Inc. All rights reserved.
Keywords: Unconstrained optimization; Supermemory gradient method; Global convergence
如有全文翻译，可否提供一下，万分感谢！！
Abstract
In this paper we propose a new class of supermemory gradient methods for unconstrained optimization problems. Trust
region approach is used in the new algorithms to guarantee the global convergence. In each iteration, the new algorithms
generate a suitable trust region radius automatically and obtain the next iterate by solving a simple subproblem. These
algorithms converge stably and averagely due to using more iterative information at each iteration, and can be reduced
to quasi-Newton methods when the iterate is close to the optimal solution. Numerical results show that this new class
of supermemory gradient methods is effective in practical computation.
2006 Elsevier Inc. All rights reserved.
Keywords: Unconstrained optimization; Supermemory gradient method; Global convergence
如有全文翻译，可否提供一下，万分感谢！！
摘要
在这纸中，我们为不受拘束的最佳化问题计画一个 快速记忆倾斜度方法的新班级。信赖
区域方法被用于新的运算法则保证全球的集中。在每个重复中，新的运算法则
自动地产生一个适当的信托区域半径而且获得那然后藉由解决一个简单的次问题反复。这些
运算法则在每个重复由于使用坚固地而且平均地聚合比较反复的数据，而且能被减少
对似乎是牛顿的方法当那反复接近最佳的解决。数字的结果表示这个新的班级
快速记忆倾斜度方法在实际的计算中是有效的。