20 results for “Understanding of distributed systems, optimization algorithms, and robotics”
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This paper provides a systematic survey of ROS 2 middleware and identifies architectural limits through three dimensions: Space, Time, and State.
Proposed an asynchronous block coordinate descent algorithm for distributed trajectory estimation in robotics, reducing communications by up to 96.9% and achieving exponential convergence.
Cheng-Han Huang, Yongliang Sun, Chaoyan Huang, Ismail Alkhouri +1 more
The paper establishes conditions for QUBO formulations of combinatorial optimization problems that guarantee valid binary and feasible local minimizers using gradient-based methods.
The paper proposes a novel framework combining evolutionary algorithms and Secure Multi-Party Computation (MPC) to enable privacy-preserving distributed optimization that meets strict time deadlines.
This paper analyzes the Metabolic Multi-Agent Optimizer (MMAO) framework at a high level, establishing properties and identifying behavioral regimes.
The paper analyzes a new class of asynchronous adaptive first-order optimization methods and proves their stochastic convergence rate is O(1/sqrt{t}) for non-convex functions.
Rudolf Krecht, Tamas Budai, Erno Horvath, Akos Kovacs +2 more
This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.
This paper introduces the Metabolic Multi-Agent Optimizer (MMAO), a self-calibrating optimization framework with endogenous resource allocation for continuous and discrete problems.
The paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework that enables stable, scalable consensus control for large swarms of quadcopters using only local neighbo…
Pekka Malo, Lauri Viitasaari, Patrik Nummi, Antti Suominen +2 more
The paper introduces an operator calculus for population-based optimization methods, establishing a modular Lyapunov principle for their convergence analysis.
This paper trains sparse sensor policies for Rayleigh-Bénard convection control using multi-agent reinforce learning and grouped regularization.
The paper presents an algorithm for updating a Directed Minimum Spanning Tree using the weighted matroid intersection algorithm and a dynamic auxiliary graph.
The paper extends results for interval scheduling to the more general throughput problem in the real-time model with constant competitive ratios for specific weight functions and advance notice.