scikit-learn · ogrisel · Aug 22, 2025 · Aug 5, 2025 · Aug 5, 2025 · Aug 7, 2025
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -233,24 +233,23 @@ Cross-Validation.
 Lasso
 =====
 
-The :class:`Lasso` is a linear model that estimates sparse coefficients.
+The :class:`Lasso` is a linear model that estimates sparse coefficients, i.e., it is
+able to set coefficients exactly to zero.
 It is useful in some contexts due to its tendency to prefer solutions
 with fewer non-zero coefficients, effectively reducing the number of
 features upon which the given solution is dependent. For this reason,
 Lasso and its variants are fundamental to the field of compressed sensing.
-Under certain conditions, it can recover the exact set of non-zero
-coefficients (see
+Under certain conditions, it can recover the exact set of non-zero coefficients (see
 :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`).
 
 Mathematically, it consists of a linear model with an added regularization term.
 The objective function to minimize is:
 
-.. math::  \min_{w} { \frac{1}{2n_{\text{samples}}} ||X w - y||_2 ^ 2 + \alpha ||w||_1}
+.. math::  \min_{w} P(w) = {\frac{1}{2n_{\text{samples}}} ||X w - y||_2 ^ 2 + \alpha ||w||_1}
 
-The lasso estimate thus solves the minimization of the
-least-squares penalty with :math:`\alpha ||w||_1` added, where
-:math:`\alpha` is a constant and :math:`||w||_1` is the :math:`\ell_1`-norm of
-the coefficient vector.
+The lasso estimate thus solves the least-squares with added penalty
+:math:`\alpha ||w||_1`, where :math:`\alpha` is a constant and :math:`||w||_1` is the
+:math:`\ell_1`-norm of the coefficient vector.
 
 The implementation in the class :class:`Lasso` uses coordinate descent as
 the algorithm to fit the coefficients. See :ref:`least_angle_regression`
@@ -281,18 +280,86 @@ computes the coefficients along the full path of possible values.
 
 .. dropdown:: References
 
-  The following two references explain the iterations
-  used in the coordinate descent solver of scikit-learn, as well as
-  the duality gap computation used for convergence control.
+  The following references explain the origin of the Lasso as well as properties
+  of the Lasso problem and the duality gap computation used for convergence control.
 
-  * "Regularization Path For Generalized linear Models by Coordinate Descent",
-    Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper
-    <https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf>`__).
+  * :doi:`Robert Tibshirani. (1996) Regression Shrinkage and Selection Via the Lasso.
+    J. R. Stat. Soc. Ser. B Stat. Methodol., 58(1):267-288
+    <10.1111/j.2517-6161.1996.tb02080.x>`
   * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares,"
     S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky,
     in IEEE Journal of Selected Topics in Signal Processing, 2007
     (`Paper <https://web.stanford.edu/~boyd/papers/pdf/l1_ls.pdf>`__)
 
+.. _coordinate_descent:
+
+Coordinate Descent with Gap Safe Screening Rules
+------------------------------------------------
+
+Coordinate descent (CD) is a strategy so solve a minimization problem that considers a
+single feature :math:`j` at a time. This way, the optimization problem is reduced to a
+1-dimensional problem which is easier to solve:
+
+.. math::  \min_{w_j} {\frac{1}{2n_{\text{samples}}} ||x_j w_j + X_{-j}w_{-j} - y||_2 ^ 2 + \alpha |w_j|}
+
+with index :math:`-j` meaning all features but :math:`j`. The solution is
+
+.. math:: w_j = \frac{S(x_j^T (y - X_{-j}w_{-j}), \alpha)}{||x_j||_2^2}
+
+with the soft-thresholding function
+:math:`S(z, \alpha) = \operatorname{sign}(z) \max(0, |z|-\alpha)`.
+Note that the soft-thresholding function is exactly zero whenever
+:math:`\alpha \geq |z|`.
+The CD solver then loops over the features either in a cycle, picking one feature after
+the other in the order given by `X` (`selection="cyclic"`), or by randomly picking
+features (`selection="random"`).
+It stops if the duality gap is smaller than the provided tolerance `tol`.
+
+.. dropdown:: Mathematical details
+
+  The duality gap :math:`G(w, v)` is an upper bound of the difference between the
+  current primal objective function of the Lasso, :math:`P(w)`, and its minimum
+  :math:`P(w^\star)`, i.e. :math:`G(w, v) \leq P(w) - P(w^\star)`. It is given by
+  :math:`G(w, v) = P(w) - D(v)` with dual objective function
+
+  .. math:: D(v) = \frac{1}{2n_{\text{samples}}}(y^Tv - ||v||_2^2)
+
+  subject to :math:`v \in ||X^Tv||_{\infty} \leq n_{\text{samples}}\alpha`.
+  With (scaled) dual variable :math:`v = c r`, current residual :math:`r = y - Xw` and
+  dual scaling
+
+  .. math::
+    c = \begin{cases}
+      1, & ||X^Tr||_{\infty} \leq n_{\text{samples}}\alpha, \\
+      \frac{n_{\text{samples}}\alpha}{||X^Tr||_{\infty}}, & \text{otherwise}
+    \end{cases}
+
+  the stopping criterion is
+
+  .. math:: \text{tol} \frac{||y||_2^2}{n_{\text{samples}}} < G(w, cr)\,.
+
+A clever method to speedup the coordinate descent algorithm is to screen features such
+that at optimum :math:`w_j = 0`. Gap safe screening rules are such a
+tool. Anywhere during the optimization algorithm, they can tell which feature we can
+safely exclude, i.e., set to zero with certainty.
+
+.. dropdown:: References
+
+  The first reference explains the coordinate descent solver used in scikit-learn, the
+  others treat gap safe screening rules.
+
+  * :doi:`Friedman, Hastie & Tibshirani. (2010).
+    Regularization Path For Generalized linear Models by Coordinate Descent.
+    J Stat Softw 33(1), 1-22 <10.18637/jss.v033.i01>`
+  * :arxiv:`O. Fercoq, A. Gramfort, J. Salmon. (2015).
+    Mind the duality gap: safer rules for the Lasso.
+    Proceedings of Machine Learning Research 37:333-342, 2015.
+    <1505.03410>`
+  * :arxiv:`E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon. (2017).
+    Gap Safe Screening Rules for Sparsity Enforcing Penalties.
+    Journal of Machine Learning Research 18(128):1-33, 2017.
+    <1611.05780>`
+
 Setting regularization parameter
 --------------------------------
 

diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.efficiency.rst
@@ -0,0 +1,11 @@
+- :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`,
+  :class:`linear_model.Lasso`, :class:`linear_model.LassoCV` as well as
+  :func:`linear_model.lasso_path` and :func:`linear_model.enet_path` now implement
+  gap safe screening rules in the coordinate descent solver for dense `X` and
+  `precompute=False` or `"auto"` with `n_samples < n_features`.
+  The speedup of fitting time is particularly pronounced (10-times is possible) when
+  computing regularization paths like the \*CV-variants of the above estimators do.
+  There is now an additional check of the stopping criterion before entering the main
+  loop of descent steps. As the stopping criterion requires the computation of the dual
+  gap, the screening happens whenever the dual gap is computed.
+  By :user:`Christian Lorentzen <lorentzenchr>`.