2013
NeurIPS
NeurIPS 2013
On Decomposing the Proximal Map
Abstract
The proximal map is the key step in gradient-type algorithms, which have become prevalent in large-scale high-dimensional problems. For simple functions this proximal map is available in closed-form while for more complicated functions it can become highly nontrivial. Motivated by the need of combining regularizers to simultaneously induce different types of structures, this paper initiates a systematic investigation of when the proximal map of a sum of functions decomposes into the composition of the proximal maps of the individual summands. We not only unify a few known results scattered in the literature but also discover several new decompositions obtained almost effortlessly from our theory.
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- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Regularization
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Hot Topic Early Bird
- optimization algorithm
Authors
Topics
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Optimization > Continuous Optimization
Mathematics & Optimization > Optimization > Convex Optimization
Machine Learning > Learning Types > Regularization
Deep Learning > Optimization & Theory > Regularization