

Slow Convergence of Stochastic Optimization Algorithms Without Derivatives Is Avoidable
De Anne Auger


Le transport optimal en pratique : géométrie, algorithmes et applications
De Jean Feydy
Apparaît dans la collection : 2019 - T1 - WS3 - Imaging and machine learning
In the last decades, semidefinite programs have emerged as a a powerful way to solve difficult combinatorial optimization problems in polynomial time. Unfortunately, they are difficult to numerically solve in high dimensions. This talk will discuss a classical heuristic used to speed up the solving, namely the Burer-Monteiro formulation. We will review the main correctness guarantees that have been established for this heuristic, and study their optimality.