A Field Guide to Forward-Backward Splitting with a FASTA Implementation

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Authors Tom Goldstein, Christoph Studer, Richard G. Baraniuk
Journal/Conference Name ArXiv
Paper Category
Paper Abstract Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond. For high-dimensional minimization problems involving large datasets or many unknowns, the forward-backward splitting method provides a simple, practical solver. Despite its apparently simplicity, the performance of the forward-backward splitting is highly sensitive to implementation details.
Date of publication 2014
Code Programming Language R
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