The Solution Path of SLOPE

@InProceedings{pmlr-v238-dupuis24a,
  title = 	 {The Solution Path of SLOPE},
  author =       {Dupuis, Xavier and Tardivel, Patrick},
  booktitle = 	 {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {775--783},
  year = 	 {2024},
  editor = 	 {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
  volume = 	 {238},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {02--04 May},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v238/dupuis24a/dupuis24a.pdf},
  url = 	 {https://proceedings.mlr.press/v238/dupuis24a.html},
  abstract = 	 {The SLOPE estimator has the particularity of having null components (sparsity) and components that are equal in absolute value (clustering). The number of clusters depends on the regularization parameter of the estimator. This parameter can be chosen as a trade-off between interpretability (with a small number of clusters) and accuracy (with a small mean squared error or a small prediction error). Finding such a compromise requires to compute the solution path, that is the function mapping the regularization parameter to the estimator. We provide in this article an algorithm to compute the solution path of SLOPE and show how it can be used to adjust the regularization parameter.}
}