Safe Metropolis-Hastings algorithm and its application to swarm control

DOI10.1016/j.sysconle.2017.10.006zbMath1380.93297OpenAlexW2771099899MaRDI QIDQ1697161

Behçet Açıkmeşe, Mahmoud El Chamie

Publication date: 15 February 2018

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sysconle.2017.10.006

zbMATH Keywords

convex optimizationMarkov chainsMetropolis-Hastingssafety constraintsswarm control

Mathematics Subject Classification ID

Stochastic programming (90C15) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Decentralized systems (93A14) Synthesis problems (93B50)

Cites Work

• What do we know about the Metropolis algorithm?
• A geometric interpretation of the Metropolis-Hastings algorithm.
• Non-negative matrices and Markov chains.
• Fast linear iterations for distributed averaging
• Markov Chain Approach to Probabilistic Guidance for Swarms of Autonomous Agents
• Implicit dual control based on particle filtering and forward dynamic programming
• Convex Necessary and Sufficient Conditions for Density Safety Constraints in Markov Chain Synthesis
• Distributed Weight Selection in Consensus Protocols by Schatten Norm Minimization
• Fastest Mixing Markov Chain on Graphs with Symmetries
• The Markov chain Monte Carlo revolution
• Fastest Mixing Markov Chain on a Graph
• Controlled Markov chains with safety upper bound
• Stability of Model-Based Networked Control Systems With Time-Varying Transmission Times
• The Monte Carlo Method
• Monte Carlo strategies in scientific computing
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