Decentralized online optimization with global objectives and local communication
By Angelia Nedić, Soomin Lee, Maxim Raginsky. 2015
51 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Sebastian U. Stich
- Martin Jaggi
- Anastasia Koloskova
- Shahin Shahrampour
- Peilin Zhao
- Wenwu Zhu
- Ali Jadbabaie
- Steven C. H. Hoi
In Management Science and Operations Research
- Sebastian U. Stich
- Martin Jaggi
- T. Zhang
- Peilin Zhao
- Shahin Shahrampour
- Wenwu Zhu
- Ali Jadbabaie
- Steven C. H. Hoi
In Optimization & Training
- Sebastian U. Stich
- Martin Jaggi
- Peter Richtárik
- Anastasia Koloskova
- Peilin Zhao
- T. Zhang
- Simon Lacoste-Julien
- Jakub Konecný
Citations in the corpus
- Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, methodology): “The most popular algorithms are based on (sub)gradient descent (Nedi´c & Ozdaglar, 2009; Johansson et al., 2010), alternating direction method of multipliers (ADMM) (Wei & Ozdaglar, 2012; Iutzeler et al., 2013) or dual averaging (Duchi et al., 2012; Nedi´c et al., 2015).”
- Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend (2019, neutral): “Additionally, many decentralized online optimization methods are proposed, for example, decentralized online multi-task learning [41], decentralized online ADMM [35], decentralized online gradient descent [2], decentralized continuous-time online saddle-point method [23], decentralized online Nester”
- Asynchronous Decentralized Online Learning (2021, unknown): “We index the prediction rounds of learner i as τ = 1 , . . . , N i .”
- Projection-free Distributed Online Learning in Networks (2017, background): “…descent (D-OGD) (Ram et al., 2010) (Yan et al., 2013) and distributed online dual averaging (D-ODA) (Duchi et al., 2012) (Hosseini et al., 2013) (Lee et al., 2015), which have been successfully applied in handling large-scale streaming data in decentralized computational architectures (e.g.,…”
- Distributed Online Optimization in Dynamic Environments Using Mirror Descent (2016, background): “The agents can exchange information with one another, and their relationship is encoded via an undirected graph G = (V, E), where V = [n] denotes the set of nodes (agents), and E is the set of edges (links between agents).”