Olivier Fercoq

Accelerated, Parallel, and Proximal Coordinate Descent

We propose a new randomized coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only. Our method (APPROX) is simultaneously Accelerated, Parallel, and PROXimal; this is the first time such a method is proposed. In the special case when the number of processors is equal to the number of coordinates, the method converges at the rate

Original article: Synchronisation and control of proliferation in cycling cell population models with age structure

2\bar{\omega}\bar{L} R^2/(k+1)^2

Ergodic Control and Polyhedral Approaches to PageRank Optimization

, where

Fast distributed coordinate descent for non-strongly convex losses

k

Optimisation of Cancer Drug Treatments Using Cell Population Dynamics

is the iteration counter,

Parallel Coordinate Descent for the Adaboost Problem

\bar{\omega}

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

is a data-weighted average degree of separability of the loss function,

Optimization in High Dimensions via Accelerated, Parallel, and Proximal Coordinate Descent

\bar{L}

Proliferation in Cell Population Models with Age Structure

is the average of Lipschitz constants associated with the coordinates and individual functions in the sum, and

A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

R