4 papers · 1 filter
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…
Composing Optimized Stepsize Schedules for Gradient Descent
Benjamin Grimmer, Kevin Shu, Alex L. Wang
Recent works by Altschuler and Parrilo and the authors have shown that it is possible to accelerate the convergence of gradient descent on smooth convex functions, even without mom…
On Averaging and Extrapolation for Gradient Descent
Alan Luner, Benjamin Grimmer
This work considers the effect of averaging, and more generally extrapolation, of the iterates of gradient descent in smooth convex optimization. After running the method, rather t…
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points
Zhichao Jia, Benjamin Grimmer
Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we sh…