arXiv Statistics ML October 2, 2026 By Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak, Mathurin Massias, Jonas Wallin

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

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The paper introduces a set of software packages in R, Python, Julia, and C++ that solve the Sorted L-One Penalized Estimation (SLOPE) problem efficiently. The packages employ a hybrid coordinate descent algorithm capable of fitting generalized linear models with various loss functions such as Gaussian, binomial, Poisson, and multinomial logistic regression. They support dense, sparse, and out‑of‑memory data structures, can compute the full SLOPE path, perform cross‑validation (including relaxed SLOPE), and are shown to outperform existing SLOPE implementations in speed on both real and simulated data.

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