9 citations · 20 across the 22 of their papers we have counts for
4 papers · 1 filter
Accelerated Gradient Descent via Long Steps
Benjamin Grimmer, Kevin Shu, Alex L. Wang
Recently Grimmer [1] showed for smooth convex optimization by utilizing longer steps periodically, gradient descent's textbook convergence guarantees can be improved by c…
Provably Faster Gradient Descent via Long Steps
Benjamin Grimmer
This work establishes new convergence guarantees for gradient descent in smooth convex optimization via a computer-assisted analysis technique. Our theory allows nonconstant stepsi…
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization
Benjamin Grimmer, Danlin Li
We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of…
Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets
Ning Liu, Benjamin Grimmer
We consider feasibility and constrained optimization problems defined over smooth and/or strongly convex sets. These notions mirror their popular function counterparts but are much…