arXiv (All)
open access
Efficient Solvers for SLOPE in R, Python, Julia, and C++
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, i
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription#609728
- Assessment of Lasso and Ridge models for soil swelling potential prediction.#771
- Accelerating Branch MPC with Two-Level Parallel Direct Solves on GPUs#1078048
- symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver#620134
- Stable and robust differentiation programs are predominantly irreversible#619180
Record · ID 1156227
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive