2 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
A Communication Efficient Quasi-Newton Method for Large-scale Distributed Multi-agent Optimization
Yichuan Li, Petros G. Voulgaris, Nikolaos M. Freris
We propose a communication efficient quasi-Newton method for large-scale multi-agent convex composite optimization. We assume the setting of a network of agents that cooperatively…
BFGS-ADMM for Large-Scale Distributed Optimization
Yichuan Li, Yonghai Gong, Nikolaos M. Freris +2
We consider a class of distributed optimization problem where the objective function consists of a sum of strongly convex and smooth functions and a (possibly nonsmooth) convex reg…
DN-ADMM: Distributed Newton ADMM for Multi-agent Optimization
Yichuan Li, Nikolaos M. Freris, Petros Voulgaris +1
In a multi-agent network, we consider the problem of minimizing an objective function that is expressed as the sum of private convex and smooth functions, and a (possibly) non-diff…
Consensus over evolutionary graphs
Michalis Smyrnakis, Nikolaos M. Freris, Hamidou Tembine
We establish average consensus on graphs with dynamic topologies prescribed by evolutionary games among strategic agents. Each agent possesses a private reward function and dynamic…
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
Hoi-To Wai, Nikolaos M. Freris, Angelia Nedic +1
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems. SUCAG constitutes…
Accelerated reconstruction of a compressively sampled data stream
Pantelis Sopasakis, Nikolaos Freris, Panagiotis Patrinos
The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for perf…