removed sidebars where unnecessary, cleaned tutorials landing page and fixed links

Author: floriankozikowski

doc/conf.py

@@ -173,6 +173,19 @@
     ],
 }
 
+# add sidebars if necessary (e.g. if more tutorials are being added)
+html_sidebars = {
+    "index": [],
+    "getting_started": [],
+    "tutorials": [],
+    "tutorials/*": [],
+    "auto_examples/*": [],
+    "api": [],  # not applied for subpages as it is useful here
+    "contribute": [],
+    "changes": [],
+    "changes/*": []
+}
+
 # Enable asciimath parsing in MathJax and configure the HTML renderer to output
 # the default asciimath delimiters. Asciimath will not be correctly rendered in
 # other output formats, but can likely be fixed using py-asciimath[1] to convert

doc/tutorials/add_datafit.rst

@@ -1,10 +1,8 @@
-:orphan:
-
 .. _how_to_add_custom_datafit:
 .. meta::
    :description: Tutorial on creating and implementing a custom datafit in skglm. Step-by-step guide includes deriving gradients, Hessians, and an example with Poisson datafit.
 
-How to add a custom datafit
+How to Add a Custom Datafit
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Motivated by generalized linear models but not limited to it, ``skglm`` solves problems of the form

doc/tutorials/alpha_max.rst

@@ -4,7 +4,7 @@
    :description: Tutorial explaining the critical regularization strength (alpha_max) in skglm. Learn conditions for zero solutions in L1-regularized optimization problems.
 
 ==========================================================
-Critical regularization strength above which solution is 0
+Critical Regularization Strength above which Solution is 0
 ==========================================================
 
 This tutorial shows that for :math:`\lambda \geq \lambda_{\text{max}} = || \nabla f(0) ||_{\infty}`, the solution to

doc/tutorials/intercept.rst

@@ -3,10 +3,10 @@
 .. meta::
    :description: In-depth guide on intercept handling in skglm solvers. Covers mathematical derivations, gradient updates, Lipschitz constants, and examples for quadratic, logistic, and Huber datafits.
 
-Computation of the intercept
+Computation of the Intercept
 ============================
 
 .. currentmodule:: skglm
 
-.. include:: intercept.md
+.. include:: intercept2.md
     :parser: myst_parser.sphinx_

doc/tutorials/intercept.md renamed to doc/tutorials/intercept2.md

@@ -6,38 +6,52 @@
 =========
 Tutorials
 =========
-----------------
 
+.. grid:: 1 1 2 2
+   :gutter: 2
 
-:ref:`How to add a custom datafit <how_to_add_custom_datafit>`
---------------------------------------------------------------
+   .. grid-item-card:: How to Add a Custom Datafit
+      :link: add_datafit.html
+      :text-align: left
 
-Learn to add a custom datafit through a hands-on examples: Implementing a Poisson datafit.
+      Learn to add a custom datafit through a hands-on examples: Implementing a Poisson datafit.
 
+   .. grid-item-card:: How to Add a Custom Penalty
+      :link: add_penalty.html
+      :text-align: left
 
-:ref:`How to add a custom penalty <how_to_add_custom_penalty>`
---------------------------------------------------------------
+      Learn to add a custom penalty by implementing the :math:`\ell_1` penalty.
 
-Learn to add a custom penalty by implementing the :math:`\ell_1` penalty.
+   .. grid-item-card:: Computation of the Intercept
+      :link: intercept.html
+      :text-align: left
 
+      Explore how ``skglm`` fits an unpenalized intercept.
 
-:ref:`Computation of the intercept <maths_unpenalized_intercept>`
------------------------------------------------------------------
+   .. grid-item-card:: Mathematics behind Cox Datafit
+      :link: cox_datafit.html
+      :text-align: left
 
-Explore how ``skglm`` fits an unpenalized intercept.
+      Understand the mathematical foundation of Cox datafit and its applications in survival analysis.
 
+   .. grid-item-card:: Details on the Group Lasso
+      :link: prox_nn_group_lasso.html
+      :text-align: left
 
-:ref:`Mathematics behind Cox datafit <maths_cox_datafit>`
----------------------------------------------------------
+      Mathematical details about the group Lasso, in particular with nonnegativity constraints.
 
-Get details about Cox datafit equations.
+   .. grid-item-card:: Understanding `alpha_max`
+      :link: alpha_max.html
+      :text-align: left
 
-:ref:`Details on the group Lasso <prox_nn_group_lasso>`
--------------------------------------------------------
+      Learn how to choose the regularization strength in :math:`\ell_1`-regularization?
 
-Mathematical details about the group Lasso, in particular with nonnegativity constraints.
+.. toctree::
+   :hidden:
 
-:ref:`Critical regularization strength above which solution is 0 <alpha_max>`
------------------------------------------------------------------------------
-
-How to choose the regularization strength in L1-regularization?
+   add_datafit
+   add_penalty
+   intercept
+   cox_datafit
+   prox_nn_group_lasso
+   alpha_max