Scholarlink

Langevin-based Monte Carlo Markov Chain methods provide a powerful framework for nonlinear state estimation. Using Langevin dynamics for efficient state transitions, these methods offer a robust alternative to traditional nonlinear filtering techniques. However, standard approaches suffer from high computational costs, the curse of dimensionality, and sensitivity to local maxima. To address these challenges, we extend sequential Metropolis Adjusted Langevin Algorithm (MALA) techniques to parallel chains on Lie Groups, leading to the Lie Group parallel Metropolis-Adjusted Langevin Algorithm (LG-pMALA) filter.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sequential Parallel Metropolis-Adjusted Langevin Algorithm on Matrix Lie Groups

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Enzo Lopez,
  • Karim Dahia,
  • Nicolas Merlinge,
  • Benedicte Winter-Bonnet,
  • Alain Maschiella,
  • Christian Musso

摘要

Langevin-based Monte Carlo Markov Chain methods provide a powerful framework for nonlinear state estimation. Using Langevin dynamics for efficient state transitions, these methods offer a robust alternative to traditional nonlinear filtering techniques. However, standard approaches suffer from high computational costs, the curse of dimensionality, and sensitivity to local maxima. To address these challenges, we extend sequential Metropolis Adjusted Langevin Algorithm (MALA) techniques to parallel chains on Lie Groups, leading to the Lie Group parallel Metropolis-Adjusted Langevin Algorithm (LG-pMALA) filter.