An adaptive Hessian approximated stochastic gradient MCMC method

DOI10.1016/J.JCP.2021.110150OpenAlexW3090537715MaRDI QIDQ2128489

Wei Deng, Yating Wang, Guang Lin

Publication date: 22 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.01384

zbMATH Keywords

stochastic approximationdeep learninglimited memory BFGSadaptive Bayesian methodHessian approximated stochastic gradient MCMChighly correlated density

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Markov processes (60Jxx) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx)

Related Items (1)

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

Uses Software

• L-BFGS
• SGD-QN

Cites Work

• Unnamed Item
• A Stochastic Quasi-Newton Method for Large-Scale Optimization
• On the limited memory BFGS method for large scale optimization
• Langevin diffusions and the Metropolis-adjusted Langevin algorithm
• Global Convergence of Online Limited Memory BFGS
• (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• A mixed multiscale finite element method for elliptic problems with oscillating coefficients
• Mixed Generalized Multiscale Finite Element Methods and Applications
• Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
• A Stochastic Approximation Method

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