22 citations · 28 across the 8 of their papers we have counts for
4 papers · 2 filters
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…