9 citations · 20 across the 22 of their papers we have counts for
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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…
Performance Estimation for Smooth and Strongly Convex Sets
Alan Luner, Benjamin Grimmer
We extend recent computer-assisted design and analysis techniques for first-order optimization over structured functions--known as performance estimation--to apply to structured se…
A Strengthened Conjecture on the Minimax Optimal Constant Stepsize for Gradient Descent
Benjamin Grimmer, Kevin Shu, Alex L. Wang
Drori and Teboulle [4] conjectured that the minimax optimal constant stepsize for N steps of gradient descent is given by the stepsize that balances performance on Huber and quadra…
Accelerated Objective Gap and Gradient Norm Convergence for Gradient Descent via Long Steps
Benjamin Grimmer, Kevin Shu, Alex L. Wang
This work considers gradient descent for L-smooth convex optimization with stepsizes larger than the classic regime where descent can be ensured. The stepsize schedules considered…
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…