R、Python、Julia 和 C++ 中 SLOPE 的高效求解器
Efficient Solvers for SLOPE in R, Python, Julia, and C++
AI总结：
提出一套在 R、Python、Julia 和 C++ 中高效求解 Sorted L-One Penalized Estimation (SLOPE) 问题的软件包，采用混合坐标下降算法，支持多种损失函数和数据结构，性能优于现有实现。
摘要：
我们提供了一套在 R、Python、Julia 和 C++ 中高效求解 Sorted L-One Penalized Estimation (SLOPE) 问题的软件包。这些软件包采用了一种高效的混合坐标下降算法，能够拟合广义线性模型（GLM），并支持多种损失函数，包括高斯、二项、泊松和多项逻辑回归。我们的实现旨在快速、内存高效且灵活。这些软件包支持多种数据结构（稠密、稀疏和内存外矩阵），并设计用于高效拟合完整的 SLOPE 路径以及处理 SLOPE 模型的交叉验证，包括松弛 SLOPE。我们展示了如何使用这些软件包的示例，以及在真实和模拟数据上展示其性能的基准测试，结果表明我们的软件包在速度上优于现有的 SLOPE 实现。
英文摘要：
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 fits generalized linear models (GLMs) and supports a variety of loss functions, including Gaussian, binomial, Poisson, and multinomial logistic regression. Our implementation is designed to be fast, memory-efficient, and flexible. The packages support a variety of data structures (dense, sparse, and out-of-memory matrices) and are designed to efficiently fit the full SLOPE path as well as handle cross-validation of SLOPE models, including the relaxed SLOPE. We present examples of how to use the packages and benchmarks that demonstrate the performance of the packages on both real and simulated data and show that our packages outperform existing implementations of SLOPE in terms of speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
