7 citations · 28 across the 31 of their papers we have counts for
9 papers · 1 filter
Distributed Primal-dual Optimization for Heterogeneous Multi-agent Systems
Yichuan Li, Petros Voulgaris, Nikolaos M. Freris
Heterogeneous networks comprise agents with varying capabilities in terms of computation, storage, and communication. In such settings, it is crucial to factor in the operating cha…
Communication Efficient Curvature Aided Primal-dual Algorithms for Decentralized Optimization
Yichuan Li, Petros G. Voulgaris, Dusan M. Stipanovic +1
This paper presents a family of algorithms for decentralized convex composite problems. We consider the setting of a network of agents that cooperatively minimize a global objectiv…
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…
Accelerated Almost-Sure Convergence Rates for Nonconvex Stochastic Gradient Descent using Stochastic Learning Rates
Theodoros Mamalis, Dusan Stipanovic, Petros Voulgaris
Large-scale optimization problems require algorithms both effective and efficient. One such popular and proven algorithm is Stochastic Gradient Descent which uses first-order gradi…
Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings
Theodoros Mamalis, Dusan Stipanovic, Petros Voulgaris
In this work, multiplicative stochasticity is applied to the learning rate of stochastic optimization algorithms, giving rise to stochastic learning-rate schemes. In-expectation th…
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…
A Youla Operator State-Space Framework for Stably Realizable Distributed Control
Mohammad Naghnaeian, Petros G. Voulgaris, Nicola Elia
This paper deals with the problem of distributed control synthesis. We seek to find structured controllers that are stably realizable over the underlying network. We address the pr…
Attack-resilient Estimation for Linear Discrete-time Stochastic Systems with Input and State Constraints
Wenbin Wan, Hunmin Kim, Naira Hovakimyan +1
In this paper, an attack-resilient estimation algorithm is presented for linear discrete-time stochastic systems with state and input constraints. It is shown that the state estima…