.gitea/workflows/publish-python.yml

@@ -16,7 +16,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.12'
+        python-version: '3.14'
 
     - name: Install dependencies
       run: |

.gitea/workflows/test.yml

@@ -6,7 +6,7 @@ jobs:
     runs-on: nvidia-cuda
     strategy:
       matrix:
-        python-version: ["3.8", "3.10", "3.12"]
+        python-version: ["3.8", "3.10", "3.12", "3.14"]
 
     steps:
     - name: Checkout repository

CONTRIBUTING.md

@@ -81,7 +81,7 @@ You can replicate our CI environment exactly to minimize "it works on my machine
 * **CUDA Toolkit:** 12.5.1
 * **Base Docker Image:** `nvidia/cuda:12.5.1-devel-ubuntu24.04`
 * **Node.js Version:** 20.x
-* **Python Versions Tested:** 3.8, 3.10, 3.12
+* **Python Versions Tested:** 3.8, 3.10, 3.12, 3.14
 * **CI Docker Image:** You can pull the exact image used by our runners from Docker Hub:
 
 ```bash

pyproject.toml

@@ -28,6 +28,8 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Topic :: Scientific/Engineering :: Mathematics",
 ]
 

tests/test_polysolve.py

@@ -139,7 +139,7 @@ def test_get_real_roots_numpy(quadratic_func):
     Tests that the NumPy-based genetic algorithm approximates the roots correctly.
     """
     # Using more generations for higher accuracy in testing
-    ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
+    ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
     
     roots = quadratic_func.get_real_roots(ga_opts, use_cuda=False)
     
@@ -158,7 +158,7 @@ def test_get_real_roots_cuda(quadratic_func):
     It will be skipped automatically if CuPy is not available.
     """
     
-    ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
+    ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
     
     roots = quadratic_func.get_real_roots(ga_opts, use_cuda=True)
     
@@ -172,7 +172,7 @@ def test_get_roots_numpy(complex_func):
     Tests that the NumPy-based genetic algorithm approximates the roots correctly.
     """
     # Using more generations for higher accuracy in testing
-    ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
+    ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
     
     roots = complex_func.get_roots(ga_opts, use_cuda=False)
     
@@ -191,7 +191,7 @@ def test_get_roots_cuda(complex_func):
     It will be skipped automatically if CuPy is not available.
     """
     
-    ga_opts = GA_Options(num_of_generations=50, data_size=200000, selection_percentile=0.66, root_precision=3)
+    ga_opts = GA_Options(num_of_generations=100, data_size=500000, selection_percentile=0.66, root_precision=2)
     
     roots = complex_func.get_roots(ga_opts, use_cuda=True)