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math.OC2025
Glocal Smoothness: Line search and adaptive step sizes can help in theory too!
Curtis Fox, Aaron Mishkin, Sharan Vaswani +1
Iteration complexities for optimizing smooth functions with first-order algorithms are typically stated in terms of a global Lipschitz constant of the gradient, and near-optimal re…
math.OC2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
Aaron Mishkin, Mert Pilanci, Mark Schmidt
We prove new convergence rates for a generalized version of stochastic Nesterov acceleration under interpolation conditions. Unlike previous analyses, our approach accelerates any…
math.OC2023
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm
Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt +3
We consider minimizing a smooth function subject to a summation constraint over its variables. By exploiting a connection between the greedy 2-coordinate update for this problem an…