Parallel Stochastic Computing

Slides

• Gabriel Peyré (CNRS and ENS): Batch, stochastic and mirror descents.
• Emilie Chouzenoux, Paris-Est: Stochastic optimization.
• Fabian Pedregosa, ETH and UC Berkeley: Parallel Computing.

Resources

• The proximity operator repository: a comprehensive website about first order methods, with code in Python and Matlab.
• ProxASAGA, implements some of discussed asynchronous methods.
• Ligthning has implementations of many stochastic methods (SGD, SAGA, SDCA).
• Scikit-Learn: machine learning in Python, contains implementations of many basic proximal splitting methods.
• The numerical Tours of Signal Processing (in Matlab, Python and Julia) contains a section dedicated to convex optimization, with applications.
• See also the related Mathematical Coffee session on Convex Optimization.