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New research questions efficiency of kinetic Langevin dynamics sampling

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details findings regarding the efficiency of standard Strang splittings used in kinetic Langevin dynamics. The study proves that these methods, commonly employed in Markov chain Monte Carlo algorithms, do not achieve the theoretical acceleration for sampling targets with a condition number $\kappa$. The research establishes lower bounds for the mixing time of OBABO and BAOAB schemes, demonstrating that ballistic cold-start mixing fails uniformly over a class of smooth strongly convex functions. Complementary upper bounds suggest that for fixed-parameter OBABO tunings, the optimal condition-number dependence for total variation mixing is linear, up to logarithmic factors. AI

RANK_REASON Academic paper published on arXiv detailing theoretical findings in computational mathematics. [lever_c_demoted from research: ic=1 ai=0.1]

Read on arXiv stat.ML →

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New research questions efficiency of kinetic Langevin dynamics sampling

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Academic paper published on arXiv detailing theoretical findings in computational mathematics. [lever_c_demoted from research: ic=1 ai=0.1]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Nawaf Bou-Rabee ·

    Provable Non-Acceleration of Standard Strang Splittings of Kinetic Langevin Dynamics

    arXiv:2608.25279v1 Announce Type: cross Abstract: The OBABO and BAOAB schemes and the other standard Strang splittings of kinetic (underdamped) Langevin dynamics are widely used Markov chain Monte Carlo algorithms. Under a suitable friction scaling, the underlying diffusion relax…

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