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Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed \citep{shi2021understanding}.
Product of semigroups of operators
Trotter, H. F · 1959
Earlier work this paper cites.
Some methods of speeding up the convergence of iteration methods
Polyak, B. T · 1964
Earlier work this paper cites.
On the construction and comparison of difference schemes
Strang, G · 1968
Earlier work this paper cites.
A method for unconstrained convex minimization problem with the rate of convergence o (1/kˆ 2)
Nesterov, Y · 1983
Earlier work this paper cites.
Effective diffusion in the fokker-planck equation
Kozlov, S. M · 1989
Earlier work this paper cites.
Computable bounds for geometric convergence rates of markov chains
Meyn, S. P., Tweedie, R. L., et al · 1994
Earlier work this paper cites.
Exponential convergence of langevin distributions and their discrete approximations
Roberts, G. O., Tweedie, R. L., et al · 1996
Earlier work this paper cites.
The variational formulation of the fokker–planck equation
Jordan, R., Kinderlehrer, D., and Otto, F · 1998
Earlier work this paper cites.
A second-order gradient-like dissipative dynamical system with hessian-driven damping.: Application to optimization and mechanics
Alvarez, F., Attouch, H., Bolte, J., and Redont, P · 2002
Earlier work this paper cites.
Ergodicity for sdes and approximations: locally lipschitz vector fields and degenerate noise
Mattingly, J. C., Stuart, A. M., and Higham, D. J · 2002
Earlier work this paper cites.
Splitting methods
McLachlan, R. I. and Quispel, G. R. W · 2002
Earlier work this paper cites.
Spectral properties of hypoelliptic operators
Eckmann, J.-P. and Hairer, M · 2003
Earlier work this paper cites.
Accelerating diffusions
Hwang, C.-R., Hwang-Ma, S.-Y., Sheu, S.-J., et al · 2005
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C · 2008
Earlier work this paper cites.
Hypocoercivity for kinetic equations with linear relaxation terms
Dolbeault, J., Mouhot, C., and Schmeiser, C · 2009
Earlier work this paper cites.
Hypocoercivity
Villani, C · 2009
Earlier work this paper cites.
Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion
Lelievre, T., Nier, F., and Pavliotis, G. A · 2013
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Nesterov, Y · 2013
Earlier work this paper cites.
Stochastic processes and applications: diffusion processes, the Fokker-Planck and Langevin equations , volume 60
Pavliotis, G. A · 2014
Earlier work this paper cites.
A differential equation for modeling nesterov’s accelerated gradient method: Theory and insights
Su, W., Boyd, S., and Candes, E · 2014
Earlier work this paper cites.
On the convergence of stochastic gradient MCMC algorithms with high-order integrators
Chen, C., Ding, N., and Carin, L · 2015
Earlier work this paper cites.
Hypocoercivity for linear kinetic equations conserving mass
Dolbeault, J., Mouhot, C., and Schmeiser, C · 2015
Earlier work this paper cites.
Langevin dynamics neglecting detailed balance condition
Ohzeki, M. and Ichiki, A · 2015
Earlier work this paper cites.
Irreversible langevin samplers and variance reduction: a large deviations approach
Rey-Bellet, L. and Spiliopoulos, K · 2015
Cited alongside, same era.
Variance reduction using nonreversible langevin samplers
Duncan, A. B., Lelievre, T., and Pavliotis, G · 2016
Cited alongside, same era.
Sampling from strongly log-concave distributions with the unadjusted langevin algorithm
Durmus, A. and Moulines, E · 2016
Cited alongside, same era.
Global rates of convergence in log-concave density estimation
Kim, A. K., Samworth, R. J., et al · 2016
Cited alongside, same era.
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Liu, Q. and Wang, D · 2016
Cited alongside, same era.
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Log-concave sampling: Metropolis-hastings algorithms are fast
Dwivedi, R., Chen, Y., Wainwright, M. J., and Yu, B · 2019
Later among the works it cites.
Couplings and quantitative contraction rates for langevin dynamics
Eberle, A., Guillin, A., Zimmer, R., et al · 2019
Later among the works it cites.
Stochastic Runge-Kutta accelerates Langevin Monte Carlo and beyond
Li, X., Wu, D., Mackey, L., and Erdogdu, M. A · 2019
Later among the works it cites.
Understanding and accelerating particle-based variational inference
Liu, C., Zhuo, J., Cheng, P., Zhang, R., and Zhu, J · 2019
Later among the works it cites.
Sampling can be faster than optimization
Ma, Y.-A., Chen, Y., Jin, C., Flammarion, N., and Jordan, M. I · 2019
Later among the works it cites.
The randomized midpoint method for log-concave sampling
Shen, R. and Lee, Y. T · 2019
Later among the works it cites.
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Wibisono, A., Wilson, A. C., and Jordan, M. I · 2016
Cited alongside, same era.
Bakry-emery meet villani
Baudoin, F · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Cited alongside, same era.
Dissipativity theory for nesterov’s accelerated method
Hu, B. and Lessard, L · 2017
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Accelerated information gradient flow
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