A fast algorithm for separated sparsity via perturbed lagrangians
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madry18a.pdf
Description
Published version
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541.02 KB
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Adobe PDF
Checksum (MD5)
c8bec58f1f51529d587ee5af339df32a
Author(s)
Madry, A
Mitrović, S
Schmidt, L
Date Issued
2018
Journal
International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Citation
Madry, A, Mitrović, S and Schmidt, L. 2018. "A fast algorithm for separated sparsity via perturbed lagrangians." International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 84.
Version
Final published version
Abstract
Copyright 2018 by the author(s). Sparsity-based methods are widely used in machine learning, statistics, and signal processing. There is now a rich class of structured sparsity approaches that expand the modeling power of the sparsity paradigm and incorporate constraints such as group sparsity, graph sparsity, or hierarchical sparsity. While these sparsity models offer improved sample complexity and better interpretability, the improvements come at a computational cost: it is often challenging to optimize over the (non-convex) constraint sets that capture various sparsity structures. In this paper, we make progress in this direction in the context of separated sparsity – a fundamental sparsity notion that captures exclusion constraints in linearly ordered data such as time series. While prior algorithms for computing a projection onto this constraint set required quadratic time, we provide a perturbed Lagrangian relaxation approach that computes provably exact projection in only nearly-linear time. Although the sparsity constraint is non-convex, our perturbed Lagrangian approach is still guaranteed to find a globally optimal solution. In experiments, our new algorithms offer a 10× speed-up already on moderately-size inputs.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://proceedings.mlr.press/v84/madry18a/madry18a.pdf
