Work

NPSA: Nonparametric Simulated Annealing for Global Optimization

Year: 2023

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2301.12656

Authors: Chen, Rong, Schumitzky, Alan, Kryshchenko, Alona, Otalvaro, Julian D., Yamada, Walter M., Neely, Michael N.

Institutions: University of Southern California, Children's Hospital Los Angeles, Arizona State University, California State University, Channel Islands

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Arizona State University

OpenAlex ID: W4318718984

Cites:

Cited by: 1

Related to: 10