(datasketches-website) branch master updated: update site_head.html with newer MathJax

[le...@apache.org]

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leerho pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/datasketches-website.git


The following commit(s) were added to refs/heads/master by this push:
     new 53176703 update site_head.html with newer MathJax
53176703 is described below

commit 531767033952e7abbbda265d54d7c8c6339fbe0f
Author: Lee Rhodes <le...@gmail.com>
AuthorDate: Sun Jan 25 12:28:31 2026 -0800

    update site_head.html with newer MathJax
---
 _includes/site_head.html      | 16 ++++++++++++++--
 docs/Density/DensitySketch.md |  8 ++++----
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/_includes/site_head.html b/_includes/site_head.html
index 0f597d38..969f4bf8 100644
--- a/_includes/site_head.html
+++ b/_includes/site_head.html
@@ -21,8 +21,20 @@
 <link rel="stylesheet" href="/css/syntax.css">
 <link rel="stylesheet" href="/css/docs.css">
 
-<script type="text/x-mathjax-config">
-  MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]},showMathMenu:false,showMathMenuMSIE:false,showProcessingMessages:false});
+<script>
+  window.MathJax = {
+    tex: {
+      inlineMath: [['$', '$'], ['\\(', '\\)']],
+      displayMath: [['$$', '$$'], ['\\[', '\\]']]
+    },
+    options: {
+      enableMenu: false // Optional: hides the right-click context menu
+    }
+  };
+</script>
+
+<script id="MathJax-script" async 
+  src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js">
 </script>
 
 <!-- original source: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMX_HTML-full -->
diff --git a/docs/Density/DensitySketch.md b/docs/Density/DensitySketch.md
index 8c547438..a5fc7c17 100644
--- a/docs/Density/DensitySketch.md
+++ b/docs/Density/DensitySketch.md
@@ -33,7 +33,7 @@ layout: doc_page
 **Quick summary:** This sketch builds a coreset from the given set of input points as multi-dimensional vectors. Provides density estimate at a given point.
 
 <a id="paper"></a>
-### Our implementation is based on the following paper:
+### Our implementation was based on the following paper:
 
 * Zohar Karnin, Edo Liberty "Discrepancy, Coresets, and Sketches in Machine Learning"
 https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
@@ -44,12 +44,12 @@ https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
 #### Key Highlights:
 
 * **New Complexity Measure:** The authors define "class discrepancy" as a way to characterize the coreset complexity of different function families, similar to how Rademacher complexity is used for generalization.
-* **Improved Coreset Sizes:** They prove the existence of &epsilon;-approximation coresets of size *O(&radic;d/&epsilon;)* for several common machine learning problems, including:
+* **Improved Coreset Sizes:** They prove the existence of &epsilon;-approximation coresets of size $O(\sqrt{d}/\epsilon)$ for several common machine learning problems, including:
     * Logistic regression
     * Sigmoid activation loss
     * Matrix covariance
     * Kernel density estimation
-* **Gaussian Kernel Resolution:** The paper resolves a long-standing open problem by matching the lower bound for the coreset complexity of Gaussian kernel density estimation at *O(&radic;d/&epsilon;)*.
+* **Gaussian Kernel Resolution:** The paper resolves a long-standing open problem by matching the lower bound for the coreset complexity of Gaussian kernel density estimation at $O(\sqrt{d}/\epsilon)$.
 * **Streaming Algorithms:** It introduces an exponential improvement to the "merge-and-reduce" trick, leading to better streaming sketches for any problem with low discrepancy.
 * **Deterministic Algorithm:** The authors provide a simple, deterministic algorithm for finding low-discrepancy sequences and coresets for any positive semi-definite kernel.
 
@@ -58,7 +58,7 @@ https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
 The findings allow for significantly faster optimization in large-scale machine learning. By reducing a massive dataset into a much smaller coreset, researchers can perform complex calculations (like training a logistic regression model) with a fraction of the computational cost while maintaining a high level of accuracy.
 
 <a id="inspiration"></a>
-### Our implementations was inspired by the following code, example, and tests by Edo Liberty:
+### Our implementation was inspired by the following code, example, and tests by Edo Liberty:
 
 * **Code:** https://github.com/edoliberty/streaming-quantiles/blob/f688c8161a25582457b0a09deb4630a81406293b/gde.py
 * **Example** https://github.com/edoliberty/streaming-quantiles/blob/f688c8161a25582457b0a09deb4630a81406293b/gde_example_usage.ipynb


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