From 9476d637b0d3a26089fbf37ee77dd7491f645243 Mon Sep 17 00:00:00 2001
From: rathaROG <siv.ratha89@gmail.com>
Date: Sun, 25 Aug 2024 14:45:42 +0200
Subject: [PATCH] Update README.md

---
 README.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index dade2a4..6d0c143 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@
 
 `lapx` basically is Tomas Kazmar's [`gatagat/lap`](https://github.com/gatagat/lap) with support for all Windows/Linux/macOS and Python 3.7-3.12. 
 
+
 <details><summary>About <code>lap</code></summary><br>
 
 Tomas Kazmar's [`lap`](https://github.com/gatagat/lap) is a [linear assignment problem](https://en.wikipedia.org/wiki/Assignment_problem) solver using Jonker-Volgenant algorithm for dense LAPJV ¹ or sparse LAPMOD ² matrices. Both algorithms are implemented from scratch based solely on the papers ¹˒² and the public domain Pascal implementation provided by A. Volgenant ³. The LAPMOD implementation seems to be faster than the LAPJV implementation for matrices with a side of more than ~5000 and with less than 50% finite coefficients.
@@ -33,11 +34,12 @@ pip install lapx
 |:---:|:---:|:---:|:---:|
 | Python 3.7 | AMD64 | x86_64/aarch64 | x86_64 |
 | Python 3.8 | AMD64 | x86_64/aarch64 | x86_64/arm64 |
-| Python 3.9-3.12 ¹ | AMD64/ARM64 ² | x86_64/aarch64 | x86_64/arm64 |
+| Python 3.9-3.13 ¹ | AMD64/ARM64 ² | x86_64/aarch64 | x86_64/arm64 |
 
-<sup>¹ lapx v0.5.9.post1 supports numpy v2.0 for Python 3.9-3.12. 🆕 </sup><br>
+<sup>¹ v0.5.10 supports numpy v2.x for Python 3.9-3.13. 🆕 </sup><br>
 <sup>² Windows ARM64 is experimental.</sup><br>
 
+
 <details><summary>Other options</summary>
 
 ### Install from GitHub repo (Require C++ compiler):
@@ -69,6 +71,7 @@ import numpy as np
 print(lap.lapjv(np.random.rand(4, 5), extend_cost=True))
 ```
 
+
 <details><summary>More details</summary>
   
 ### `cost, x, y = lap.lapjv(C)`