Finding the optimal integration coefficient for a palindromic multi-stage splitting integrator in HMC applications to Bayesian inference
Description
We present the tables of integration coefficients for the 2- and 3-stage adaptive splitting integrators derived for Hamiltonian Monte Carlo (HMC) using the Adaptive Integration Approach s-AIA introduced in - Nagar, L., Fernández-Pendás, M., Sanz-Serna, J. M., Akhmatskaya, E. (2023). Adaptive multi-stage integration schemes for Hamiltonian Monte Carlo. arXiv:2307.02096. doi:10.48550/arXiv.2307.02096 . The tables provide the maps that assign the optimal (in terms of the best conservation of energy for harmonic forces) integration coefficient for a k-stage palindromic splitting integrator to a nondimensional simulation step size in the stability interval (0, 2 k). The repository includes the two tables for 2- and 3-stage s-AIA, a Python script that provides the optimal integration coefficient for a user-chosen dimensional step size, two .txt files containing the values of the optimal integration coefficients for 2- and 3-stage s-AIA used by the Python script, and a readme.pdf file describing the s-AIA methodology and the usage guidelines for the tables.
Files
Steps to reproduce
Open readme.pdf and follow the instructions in "Using the tables" section.
Institutions
Categories
Funding
Ministerio de Ciencia, Innovación y Universidades
CEX2021-001142-S
Ministerio de Ciencia, Innovación y Universidades
PID2019-104927GB-C22
Ministerio de Ciencia, Innovación y Universidades
PID2019-104927GB-C21
Ministerio de Ciencia, Innovación y Universidades
MCIN/AEI/10.13039/501100011033
Ministerio de Ciencia, Innovación y Universidades
ERDF (“A way of making Europe”)
Basque Government
BERC 2022-2025 Program
Basque Government
Convenio IKUR 21-HPC-IA
Basque Government
ELKARTEK KK-2022/00006
Basque Government
ELKARTEK KK-2021/00022
Basque Government
ELKARTEK KK-2021/00064
CaixaBank
LCF/BQ/DI20/11780022