Lecture

Optimization for Machine Learning: Frank-Wolfe Algorithm

Description

This lecture covers the Frank-Wolfe algorithm, a projection-free optimization method reducing non-linear to linear optimization. It was invented in 1956 by Marguerite Frank and Philip Wolfe. The algorithm is sparse and always feasible, making it suitable for various applications. The lecture explains the algorithm's properties, stepsize variants, examples like Lasso Regression, and its convergence in O(1/ɛ) steps. It also discusses the duality gap and affine invariance. The proof of convergence in O(1/ɛ) steps is detailed, emphasizing the algorithm's scalability and efficiency.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.