From faa8bceb63f9884565181ad802ff5157294cfa6e Mon Sep 17 00:00:00 2001
From: Ryan Charette <100053398+ryan-charette@users.noreply.github.com>
Date: Wed, 15 Jul 2026 22:26:01 -0500
Subject: [PATCH] Fix tagged equation rendering in docs

---
 ressources/demo/PEPit_demo_algorithm_design.ipynb | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index bd81d9c0..1257bf69 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -11,13 +11,16 @@
     "For instance, consider the optimization problem\n",
     "$$\\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
     "where $f$ is assumed to belong to some function class $\\mathcal{F}$. Assuming the gradients of $f$ are readily available, one may consider the following family of first-order algorithms parametrized by weights $\\{h_{i,j}\\}$:\n",
-    "$$\\begin{aligned}\n",
+    "\\begin{equation*}\n",
+    "\\begin{aligned}\n",
     "y_1 &= y_0 - h_{1,0} \\nabla f(y_0),\\\\\n",
     "y_2 &= y_1 - h_{2,0} \\nabla f(y_0) - h_{2,1} \\nabla f(y_1),\\\\\n",
     "y_3 &= y_2 - h_{3,0} \\nabla f(y_0) - h_{3,1} \\nabla f(y_1) - h_{3,2} \\nabla f(y_2),\\\\\n",
     "&\\vdots\\\\\n",
     "y_N &= y_{N-1} - \\sum_{i=0}^{N-1} h_{N,i} \\nabla f(y_i),\n",
-    "\\end{aligned} \\tag{1}\\label{eq:ogm_start}$$\n",
+    "\\end{aligned}\n",
+    "\\tag{1}\\label{eq:ogm_start}\n",
+    "\\end{equation*}\n",
     "and aim to choose $\\{h_{i,j}\\}$ as a solution to the minimax problem:\n",
     "$$\\min_{\\{h_{i,j}\\}}\\,\\, \\max_{f\\in\\mathcal{F}_{0,L}} \\left\\{ \\frac{f(y_N)-f_\\star}{\\|y_0-x_\\star\\|^2_2} \\, : \\, y_N \\text{ obtained from } \\eqref{eq:ogm_start} \\text{ and } y_0 \\right\\}. \\tag{2}\\label{eq:pep_opt_h}$$\n",
     "One natural approach is to choose the step sizes by solving $\\eqref{eq:pep_opt_h}$. Of course, methods of the form $\\eqref{eq:ogm_start}$ are impractical in general, since they require keeping track of all previous gradients, and we will try to *factor* them into more convenient forms. This notebook illustrates the following approaches:\n",