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New SLOPE solvers released for R, Python, Julia, and C++

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new software packages for R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. These packages utilize a hybrid coordinate descent algorithm capable of fitting generalized linear models with various loss functions, including Gaussian, binomial, Poisson, and multinomial logistic regression. Benchmarks indicate that these new implementations outperform existing SLOPE solvers in terms of speed and memory efficiency, supporting sparse and out-of-memory matrices for flexible data handling. AI

RANK_REASON The cluster contains an academic paper detailing new software for statistical computation. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New SLOPE solvers released for R, Python, Julia, and C++

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The cluster contains an academic paper detailing new software for statistical computation. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak, Mathurin Massias, Jonas Wallin ·

    Efficient Solvers for SLOPE in R, Python, Julia, and C++

    arXiv:2511.02430v3 Announce Type: replace-cross Abstract: We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hybrid coordinate descent algorithm that fit…

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