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math.OC2025
Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness
Rajiv Sambharya, Jinho Bok, Nikolai Matni +1
We develop a machine-learning framework to learn hyperparameter sequences for accelerated first-order methods (e.g., the step size and momentum sequences in accelerated gradient de…
math.OC2025
Optimized methods for composite optimization: a reduction perspective
Jinho Bok, Jason M. Altschuler
Recent advances in convex optimization have leveraged computer-assisted proofs to develop optimized first-order methods that improve over classical algorithms. However, each optimi…
math.OC2025
Accelerating Proximal Gradient Descent via Silver Stepsizes
Jinho Bok, Jason M. Altschuler
Surprisingly, recent work has shown that gradient descent can be accelerated without using momentum -- just by judiciously choosing stepsizes. An open question raised by several pa…