Designing games for distributed optimization

By Na Li, Jason R. Marden. IEEE Conference on Decision and Control and European Control Conference, 2011

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6f69f77d4653d18bf58c0aba82d14727ef23cb1d

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Citations in the corpus

Distributed Nash Equilibrium Seeking by a Consensus Based Approach (2016, background): “, [5] and [7]–[9]), this paper addresses Nash equilibrium seeking under local communication network, i.”
A Passivity-Based Approach to Nash Equilibrium Seeking Over Networks (2017, background): “In continuous-time, gradient-based algorithms are designed in [32] based on information from a set of local neighboring agents, for games with local utility functions (proved to be state-based potential games).”
Coordinate Dual Averaging for Decentralized Online Optimization With Nonseparable Global Objectives (2015, methodology): “Li and Marden [1], whereas the primal update rule (13b) is exactly what one has in Nesterov’s scheme [33].”
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization (2017, methodology): “A different type of a distributed algorithm for convex optimization has been proposed in [69], where each agent keeps an estimate for all agents’ decisions.”
Robust Power Management via Learning and Game Design (2021, methodology): “The above elements cannot be treated with the techniques of Candogan et al. (2010) and/or Li and Marden (2013), leading us to introduce a new set of tools from stochastic approximation, martingale limit theory and variational analysis.”
Decentralized online optimization with global objectives and local communication (2015, methodology): “In contrast with [1] and [2], which considers primal methods, our algorithms have two main distinctive features: (1) we propose a primal-dual update based on Nesterov’s algorithm [33], and (2) we consider an online convex optimizatio n problem and provide regret bounds in terms of the global resourc”
Distributed Algorithms for Aggregative Games on Graphs (2016, background): “First, increasingly game-theoretic approaches are being employed for developing distributed control protocols where the choice of schemes lies with the designer (cf. [25, 27]).”
Learning Transferable Knowledge Through Embedding Spaces (2019, background): “, adversarial games), game-theoretical notions are used to define a global optimal state for the agents [248].”
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017, background): “, adversarial games), game theoretical notions are used to define a global optimal state for the agents [18].”
Distributed Seeking of Nash Equilibria With Applications to Mobile Sensor Networks (2012, background): “to the optimal solution of some global optimization problem [2]–[10].”
Distributed Nash equilibrium seeking for aggregative games with coupled constraints (2016, background): “Email addresses: sliang@amss.ac.cn (Shu Liang), peng.yi@utoronto.ca (Peng Yi), yghong@iss.ac.cn (Yiguang Hong). optimization problems can be handled with a gametheoretic approach (Li & Marden 2013).”
Characterization and computation of local Nash equilibria in continuous games (2013, methodology): “Game theory is an established technique for modeling this interaction, and it has emerged as an engineering tool for analysis and synthesis of systems comprised of dynamically–coupled decision–making agents possessing competing interests [1], [2].”