$$
\def\xx{\boldsymbol x}
\def\yy{\boldsymbol y}
\def\ss{\boldsymbol s}
\def\dd{\boldsymbol d}
\DeclareMathOperator*{\argmin}{{arg\,min}}
\DeclareMathOperator*{\minimize}{{minimize}}
\DeclareMathOperator*{\diam}{{diam}}
$$
This blog post is the first in a series discussing different theoretical and practical aspects of the Frank-Wolfe algorithm.

**Outline:**

The Frank-Wolfe Algorithm

Example …

Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers class of functions that are convex, strongly convex and $L$-smooth.

**Setting**. $f$ is a function $\mathbb{R}^p \to …

My friend Rémi Leblond has recently uploaded to ArXiv our preprint on an asynchronous version of the SAGA optimization algorithm.

The main contribution is to develop a parallel (fully asynchronous, no locks) variant of the SAGA algorighm. This is a stochastic variance-reduced method for general optimization, specially adapted for problems …