Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent
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References listed on IDEAS
- Arnak S. Dalalyan, 2017.
"Theoretical guarantees for approximate sampling from smooth and log-concave densities,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
- Arnak S. Dalalyan, 2014. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Working Papers 2014-45, Center for Research in Economics and Statistics.
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Cited by:
- Florian Maire & Nial Friel & Pierre ALQUIER, 2017. "Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets," Working Papers 2017-40, Center for Research in Economics and Statistics.
- Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
- Chau, Huy N. & Rásonyi, Miklós, 2022. "Stochastic Gradient Hamiltonian Monte Carlo for non-convex learning," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 341-368.
- Dalalyan, Arnak S. & Karagulyan, Avetik, 2019.
"User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient,"
Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
- Arnak Dalalyan & Avetik Karagulyan, 2017. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Working Papers 2017-20, Center for Research in Economics and Statistics.
- Ghaderi, Susan & Ahookhosh, Masoud & Arany, Adam & Skupin, Alexander & Patrinos, Panagiotis & Moreau, Yves, 2024. "Smoothing unadjusted Langevin algorithms for nonsmooth composite potential functions," Applied Mathematics and Computation, Elsevier, vol. 464(C).
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Keywords
Markov Chain Monte Carlo; Approximate sampling; Rates of convergence; Langevin algorithm; Gradient descent;All these keywords.
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