80 citations · 138 across the 20 of their papers we have counts for
3 papers · 1 filter
Conformal Symplectic and Relativistic Optimization
Guilherme França, Jeremias Sulam, Daniel P. Robinson +1
Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both correspondi…
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI
Evan Schwab, Benjamin D. Haeffele, René Vidal +1
Sparse dictionary learning is a popular method for representing signals as linear combinations of a few elements from a dictionary that is learned from the data. In the classical s…
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…