Universal Majorization-Minimization Algorithms

Matthew Streeter

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arXiv · 2308.00190

Universal Majorization-Minimization Algorithms

Matthew Streeter

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Abstract

Majorization-minimization (MM) is a family of optimization methods that iteratively reduce a loss by minimizing a locally-tight upper bound, called a majorizer. Traditionally, majorizers were derived by hand, and MM was only applicable to a small number of well-studied problems. We present optimizers that instead derive majorizers automatically, using a recent generalization of Taylor mode automatic differentiation. These universal MM optimizers can be applied to arbitrary problems and converge from any starting point, with no hyperparameter tuning.

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Matthew Streeter. 2023-07-31. Universal Majorization-Minimization Algorithms. https://arxiv.org/abs/2308.00190

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