gradient-based proposals 1hamiltonian monte carlo 1high-dimensional sampling 1mcmc scaling 1metropolis-hastings 1
From the 1 of 3 linked papers with an AI index.
3 papers
stat.CO2026
Optimal scaling of MCMC algorithms: the Hamiltonian approach
P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis
The paper develops a general Hamiltonian‑based framework to analyze how Metropolis‑Hastings MCMC algorithms should be scaled as the dimensionality of the target distribution grows,…
math.OC2026
Accelerated optimization algorithms and ordinary differential equations: the convex non Euclidean case
Paul Dobson, Jesus MarÃa Sanz-Serna, Konstantinos C. Zygalakis
We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising con…
math.NA2024
Stroboscopic averaging methods to study autoresonance and other problems with slowly varying forcing frequencies
M. P. Calvo, J. M. Sanz-Serna, Beibei Zhu
Autoresonance is a phenomenon of physical interest that may take place when a nonlinear oscillator is forced at a frequency that varies slowly. The stroboscopic averaging method (S…