This study presents a constructive methodology for designing accelerated
convex optimisation algorithms in continuous-time domain. The two key enablers
are the classical concept of passivity in control theory and the time-dependent
change of variables that maps the output of the internal dynamic system to the
optimisation variables. The Lyapunov function associated with the optimisation
dynamics is obtained as a natural consequence of specifying the internal
dynamics that drives the state evolution as a passive linear time-invariant
system. The passivity-based methodology provides a general framework that has
the flexibility to generate convex optimisation algorithms with the guarantee
of different convergence rate bounds on the objective function value. The same
principle applies to the design of online parameter update algorithms for
adaptive control by re-defining the output of internal dynamics to allow for
the feedback interconnection with tracking error dynamics.

本研究提出了一种在连续时间域中设计加速凸优化算法的建构性方法，该方法利用控制理论中的经典概念 —— 无源性和时间依赖变量的改变，通过将内部动态系统的输出映射到优化变量，得到与优化动态相关的李亚普诺夫函数，提供了一个具有灵活性的通用框架，可以生成带有不同收敛速度界限的凸优化算法。

基于被动性的加速凸优化方法

A Passivity-Based Method for Accelerated Convex Optimisation

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes
with partially observed states. Belief filtering in DBNs is the task of
inferring the belief state (i.e. the probability distribution over process
states) based on incomplete and uncertain observations. In this article, we
explore the idea of accelerating the filtering task by automatically exploiting
causality in the process. We consider a specific type of causal relation,
called passivity, which pertains to how state variables cause changes in other
variables. We present the Passivity-based Selective Belief Filtering (PSBF)
method, which maintains a factored belief representation and exploits passivity
to perform selective updates over the belief factors. PSBF is evaluated in both
synthetic processes and a simulated multi-robot warehouse, where it
outperformed alternative filtering methods by exploiting passivity.

该研究探索了利用因果关系加速贝叶斯网络中的信念过滤任务的想法，并提出了一种基于被动性的选择性信念过滤方法（PSBF），该方法在维护因果关系的情况下，在虚拟进程和模拟多机器人仓库中均优于传统滤波方法。

利用因果关系在动态贝叶斯网络中进行选择性信念过滤（扩展摘要）

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian  Networks (Extended Abstract)

This paper investigates an energy conservation and dissipation -- passivity
-- aspect of dynamic models in evolutionary game theory. We define a notion of
passivity using the state-space representation of the models, and we devise
systematic methods to examine passivity and to identify properties of passive
dynamic models. Based on the methods, we describe how passivity is connected to
stability in population games and illustrate stability of passive dynamic
models using numerical simulations.

本文研究了演化博弈理论中动态模型的一个能量守恒和耗散 —— 被动性方面，提出了使用状态空间表示的被动性概念，并设计了系统性方法来检验被动性并识别被动动态模型的属性，基于这些方法，阐述了被动性与人口游戏的稳定性的联系，并使用数值模拟说明了被动动态模型的稳定性。