Accelerating Fixed-Point Algorithms in Statistics and Data Science: A State-of-Art Review
نویسندگان
چکیده
Fixed-point algorithms are popular in statistics and data science due to their simplicity, guaranteed convergence, applicability high-dimensional problems. Well-known examples include the expectation-maximization (EM) algorithm, majorization-minimization (MM), gradient-based like gradient descent (GD) proximal descent. A characteristic weakness of these is slow convergence. We discuss several state-of-art techniques for accelerating demonstrate evaluate terms efficiency robustness six distinct applications. Among acceleration schemes, SQUAREM shows robust with a mean 18-fold speedup. DAAREM restarted-Nesterov schemes also consistently impressive accelerations. Thus, it possible accelerate original fixed-point algorithm by using one SQUAREM, DAAREM, or schemes. describe implementation details software packages facilitate application strategies selecting particular scheme given problem.
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ژورنال
عنوان ژورنال: Journal of data science
سال: 2022
ISSN: ['1680-743X', '1683-8602']
DOI: https://doi.org/10.6339/22-jds1051