NPSA：全局优化的非参数模拟退火算法
NPSA: Nonparametric Simulated Annealing for Global Optimization
摘要：
在本文中，我们描述了NPSA，第一个并行的非参数全局极大似然优化算法使用模拟退火（SA）。与非参数自适应网格搜索方法NPAG不同，NPAG不能保证找到全局最优解，并且可能受到维数灾难的影响，NPSA是一个全局优化器，并且没有这些网格相关的问题。我们说明NPSA的一些例子，包括伏立康唑的药代动力学（PK）模型，并表明NPSA可以作为升级到目前的网格搜索为基础的非参数方法。
英文摘要：
In this paper we describe NPSA, the first parallel nonparametric global maximum likelihood optimization algorithm using simulated annealing (SA). Unlike the nonparametric adaptive grid search method NPAG, which is not guaranteed to find a global optimum solution, and may suffer from the curse of dimensionality, NPSA is a global optimizer and it is free from these grid related issues. We illustrate NPSA by a number of examples including a pharmacokinetics (PK) model for Voriconazole and show that NPSA may be taken as an upgrade to the current grid search based nonparametric methods.
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