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math.OC2024
Automatic Differentiation of Optimization Algorithms with Time-Varying Updates
Sheheryar Mehmood, Peter Ochs
Numerous Optimization Algorithms have a time-varying update rule thanks to, for instance, a changing step size, momentum parameter or, Hessian approximation. In this paper, we appl…
cs.LG2024
A Generalization Result for Convergence in Learning-to-Optimize
Michael Sucker, Peter Ochs
Learning-to-optimize leverages machine learning to accelerate optimization algorithms. While empirical results show tremendous improvements compared to classical optimization algor…
cs.LG2024
A Probabilistic Framework for Learnable Optimization Algorithms
Peter Ochs, Michael Sucker
We propose a statistical-learning framework for optimization algorithms. The framework is based on probability distributions over optimization trajectories induced by a distributio…