Self-concordant analysis of Frank-Wolfe algorithm

1 code implementation • ICML 2020 • Mathias Staudigl, Pavel Dvurechenskii, Shimrit Shtern, Kamil Safin, Petr Ostroukhov

Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved.

Quantum State Tomography

Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods

no code implementations • NeurIPS 2016 • Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii

In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges.