Optimization

Optimization for Data Science Course @ Télécom ParisTech by Alexandre Gramfort & Stéphane Gaïffas

This course covers a general review of theory and practise of gradient-based algorithms to solve empirical risk minimization problems (mainly linear regression, logistic regression and support vector machines). For all methods, it covers also the proximal approach, dealing with regularization.

First order algorithms

Study & implementation of ISTA and FISTA algotithms: the first is a vanilla gradient descent algorithm, the second is its accelerated version

Coordinate descent

The gradient may be update coordinate after coordinate, making the convergence faster using smart updates

Conjugate gradient descent

An iterative method to solve linear problems with positive definite matrices. Specific result for quadratic case: it converges in at most n iterations

Quasi-Newton methods

These methods leverage the Taylor expansion around the optimal to approach the hessian for approaching Newton Method (which does not scale, as such)

Stochastic Gradient Descent

Instead of updating over the whole dataset, update only on one data point, whosen sequentially and randomly, or on a mini-batch. The SGD performs very well for the first iteration, but then have a bad behavior. Indeed, the estimator is unbiased, but has a large variance.

SGD with variance reduction

Monte Carlo methods are useful to reduce the variance induced by the SGD algorithm. We review (theoretically and in practise) several such algorithms: SAG, SAGA, SVRG

Introduction to Non Convex Optimization

Review of several non convex regularization Intro to conditional gradient algorithm

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