no code implementations • 21 Jun 2023 • Xufeng Cai, Cheuk Yin Lin, Jelena Diakonikolas
Contrary to the empirical practice of sampling from the datasets without replacement and with (possible) reshuffling at each epoch, the theoretical counterpart of SGD usually relies on the assumption of sampling with replacement.
no code implementations • 28 Mar 2023 • Cheuk Yin Lin, Chaobing Song, Jelena Diakonikolas
Exploiting partial first-order information in a cyclic way is arguably the most natural strategy to obtain scalable first-order methods.
1 code implementation • 2 Nov 2021 • Chaobing Song, Cheuk Yin Lin, Stephen J. Wright, Jelena Diakonikolas
\textsc{clvr} yields improved complexity results for (GLP) that depend on the max row norm of the linear constraint matrix in (GLP) rather than the spectral norm.
no code implementations • 12 Feb 2021 • Alejandro Carderera, Jelena Diakonikolas, Cheuk Yin Lin, Sebastian Pokutta
Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but linear optimization over the constraint set remains computationally feasible.
