Fixed Point Strategies in Data Science
نویسندگان
چکیده
The goal of this paper is to promote the use fixed point strategies in data science by showing that they provide a simplifying and unifying framework model, analyze, solve great variety problems. They are seen constitute natural environment explain behavior advanced convex optimization methods as well recent nonlinear which formulated terms paradigms go beyond minimization concepts involve constructs such Nash equilibria or monotone inclusions. We review pertinent tools theory describe main state-of-the-art algorithms for provably convergent construction. also incorporate additional ingredients stochasticity, block-implementations, non-Euclidean metrics, further enhancements. Applications signal image processing, machine learning, statistics, neural networks, inverse problems discussed.
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2021
ISSN: ['1053-587X', '1941-0476']
DOI: https://doi.org/10.1109/tsp.2021.3069677