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A quasi-Newton acceleration for high-dimensional optimization algorithms.
Statistics and Computing ( IF 2.2 ) Pub Date : 2009-12-12 , DOI: 10.1007/s11222-009-9166-3
Hua Zhou 1 , David Alexander , Kenneth Lange
Affiliation  

In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception. This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test problems.

中文翻译:

用于高维优化算法的拟牛顿加速度。

在许多统计问题中,通过 EM 或 MM 算法进行的最大似然估计的收敛速度极其缓慢。这种趋势限制了这些算法在数据挖掘、基因组学和成像方面的现代高维问题的应用。不幸的是,大多数现有的加速技术不适合涉及大量参数的复杂模型。Varadhan 和 Roland 最近提出的平方迭代方法 (SQUAREM) 是一个值得注意的例外。本文提出了一种新的拟牛顿加速方案,该方案在每次迭代和整体存储中只需要适度的计算增量,并且在几个有代表性的测试问题上与 SQUAREM 的性能相匹敌或超越。
更新日期:2009-12-12
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