NPSA: Nonparametric Simulated Annealing for Global Optimization

Autor:	Chen, Rong, Schumitzky, Alan, Kryshchenko, Alona, Otalvaro, Julian D., Yamada, Walter M., Neely, Michael N.
Rok vydání:	2023
Předmět:	Statistics - Methodology Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: 35 pages, 11 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.12656 Zobrazit plný text záznamu View this record from Arxiv