44 citations · 158 across the 15 of their papers we have counts for
5 papers · 1 filter
Distributed Optimization, Averaging via ADMM, and Network Topology
Guilherme França, José Bento
There has been an increasing necessity for scalable optimization methods, especially due to the explosion in the size of datasets and model complexity in modern machine learning ap…
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization
Guilherme França, Daniel P. Robinson, René Vidal
Optimization is at the heart of machine learning, statistics and many applied scientific disciplines. It also has a long history in physics, ranging from the minimal action princip…
A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM
Guilherme França, Daniel P. Robinson, René Vidal
Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth…
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme França, Daniel P. Robinson, René Vidal
Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and acceler…
An Explicit Convergence Rate for Nesterov's Method from SDP
Sam Safavi, Bikash Joshi, Guilherme França +1
The framework of Integral Quadratic Constraints (IQC) introduced by Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization al…