3 citations · 3 across the 3 of their papers we have counts for
6 papers
Accelerated Markov Chain Monte Carlo Algorithms on Discrete States
Bohan Zhou, Shu Liu, Xinzhe Zuo +1
We propose a class of discrete state sampling algorithms based on Nesterov's accelerated gradient method, which extends the classical Metropolis-Hastings (MH) algorithm. The evolut…
Variational conditional normalizing flows for computing second-order mean field control problems
Jiaxi Zhao, Mo Zhou, Xinzhe Zuo +1
Mean field control (MFC) problems have vast applications in artificial intelligence, engineering, and economics, while solving MFC problems accurately and efficiently in high-dimen…
Gradient-adjusted underdamped Langevin dynamics for sampling
Xinzhe Zuo, Stanley Osher, Wuchen Li
Sampling from a target distribution is a fundamental problem. Traditional Markov chain Monte Carlo (MCMC) algorithms, such as the unadjusted Langevin algorithm (ULA), derived from…
Numerical Analysis on Neural Network Projected Schemes for Approximating One Dimensional Wasserstein Gradient Flows
Xinzhe Zuo, Jiaxi Zhao, Shu Liu +2
We provide a numerical analysis and computation of neural network projected schemes for approximating one dimensional Wasserstein gradient flows. We approximate the Lagrangian mapp…
Fisher information dissipation for time inhomogeneous stochastic differential equations
Qi Feng, Xinzhe Zuo, Wuchen Li
We provide a Lyapunov convergence analysis for time-inhomogeneous variable coefficient stochastic differential equations (SDEs). Three typical examples include overdamped, irrevers…
Numerical analysis of a first-order computational algorithm for reaction-diffusion equations via the primal-dual hybrid gradient method
Shu Liu, Xinzhe Zuo, Stanley Osher +1
In arXiv:2305.03945 [math.NA], a first-order optimization algorithm has been introduced to solve time-implicit schemes of reaction-diffusion equations. In this research, we conduct…