8384
2021
eng
conferenceobject
0
--
--
--
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe variant that uses the open-loop step size strategy 𝛾𝑡 = 2/(𝑡 + 2), obtaining a O (1/𝑡) convergence rate for this class of functions in terms of primal gap and Frank-Wolfe gap, where 𝑡 is the iteration count. This avoids the use of second-order information or the need to estimate local smoothness parameters of previous work. We also show improved convergence rates for various common cases, e.g., when the feasible region under consideration is uniformly convex or polyhedral.
Thirty-fifth Conference on Neural Information Processing Systems, NeurIPS 2021
publish
yes
Alejandro Carderera
Alejandro Carderera
Sebastian Pokutta
Besançon Mathieu
MODAL-SynLab
Pokutta, Sebastian
AI in Society, Science, and Technology
Math+AA3-7