Update the citation info for solve_adaptive_save_at (#814)

* Update the citation info for solve_adaptive_save_at

* Fix linter-complaints in the tests

Author: pnkraemer

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
   python: python3
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
       - id: check-merge-conflict
@@ -13,15 +13,15 @@ repos:
       - id: yamlfix
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.9.9
+    rev: v0.12.10
     hooks:
       # Run the linter.
       - id: ruff
         args: [--fix]
       # Run the formatter.
       - id: ruff-format
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.17.1
     hooks:
       - id: mypy
         args: [--ignore-missing-imports, --check-untyped-defs]

README.md

@@ -85,13 +85,17 @@ A PDF is available [at this link](https://tobias-lib.ub.uni-tuebingen.de/xmlui/h
 
 If you use the solve-and-save-at functionality, please cite
 ```bibtex
-@article{krämer2024adaptive,
-    title={Adaptive Probabilistic {ODE} Solvers Without Adaptive Memory Requirements},
-    author={Kr{\"a}mer, Nicholas},
-    year={2024},
-    eprint={2410.10530},
-    archivePrefix={arXiv},
-    url={https://arxiv.org/abs/2410.10530},
+@InProceedings{kramer2024adaptive,
+  title     = {Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements},
+  author    = {Kr\"{a}mer, Nicholas},
+  booktitle = {Proceedings of the First International Conference on Probabilistic Numerics},
+  pages     = {12--24},
+  year      = {2025},
+  editor    = {Kanagawa, Motonobu and Cockayne, Jon and Gessner, Alexandra and Hennig, Philipp},
+  volume    = {271},
+  series    = {Proceedings of Machine Learning Research},
+  publisher = {PMLR},
+  url       = {https://proceedings.mlr.press/v271/kramer25a.html}
 }
 ```
 This article introduced the algorithm we use.

probdiffeq/ivpsolve.py

@@ -128,26 +128,29 @@ def solve_adaptive_save_at(
 ) -> IVPSolution:
     r"""Solve an initial value problem and return the solution at a pre-determined grid.
 
-    This algorithm implements the method by Krämer (2024).
-    Please consider citing it if you use it for your research.
-    A PDF is available [here](https://arxiv.org/abs/2410.10530)
-    and Krämer's (2024) experiments are
-    [here](https://github.com/pnkraemer/code-adaptive-prob-ode-solvers).
-
+    This algorithm implements the method by Krämer (2024). Please consider citing it
+    if you use it for your research. A PDF is available
+    [here](https://arxiv.org/abs/2410.10530) and Krämer's (2024) experiments are
+    available [here](https://github.com/pnkraemer/code-adaptive-prob-ode-solvers).
 
     ??? note "BibTex for Krämer (2024)"
         ```bibtex
-        @article{krämer2024adaptive,
-            title={Adaptive Probabilistic {ODE} Solvers Without
-            Adaptive Memory Requirements},
-            author={Kr{\"a}mer, Nicholas},
-            year={2024},
-            eprint={2410.10530},
-            archivePrefix={arXiv},
-            url={https://arxiv.org/abs/2410.10530},
+        @InProceedings{kramer2024adaptive,
+            title     = {Adaptive Probabilistic ODE Solvers Without Adaptive Memory
+                        Requirements},
+            author    = {Kr\"{a}mer, Nicholas},
+            booktitle = {Proceedings of the First International Conference on
+                        Probabilistic Numerics},
+            pages     = {12--24},
+            year      = {2025},
+            editor    = {Kanagawa, Motonobu and Cockayne, Jon and Gessner, Alexandra
+                        and Hennig, Philipp},
+            volume    = {271},
+            series    = {Proceedings of Machine Learning Research},
+            publisher = {PMLR},
+            url       = {https://proceedings.mlr.press/v271/kramer25a.html}
         }
         ```
-
     """
     if not adaptive_solver.solver.is_suitable_for_save_at:
         msg = (

tests/test_ivpsolvers/test_controllers.py

@@ -2,9 +2,13 @@
 
 from probdiffeq import ivpsolvers
 from probdiffeq.backend import numpy as np
+from probdiffeq.backend import testing
 
 
-def test_equivalence_pi_vs_i(dt=0.1428, error_power=3.142, num_applies=4):
+@testing.parametrize("dt", [0.1428])
+@testing.parametrize("error_power", [3.142])
+@testing.parametrize("num_applies", [4])
+def test_equivalence_pi_vs_i(dt, error_power, num_applies):
     ctrl_pi = ivpsolvers.control_proportional_integral(
         power_integral_unscaled=1.0, power_proportional_unscaled=0.0
     )

tests/test_ivpsolvers/test_cubature_equivalence.py

@@ -2,6 +2,7 @@
 
 from probdiffeq import ivpsolvers
 from probdiffeq.backend import numpy as np
+from probdiffeq.backend import testing
 
 
 def test_third_order_spherical_vs_unscented_transform_scalar_input():
@@ -16,7 +17,8 @@ def test_third_order_spherical_vs_unscented_transform_scalar_input():
         assert np.allclose(x[2:], y[1:])
 
 
-def test_third_order_spherical_vs_unscented_transform(n=4):
+@testing.parametrize("n", [4])
+def test_third_order_spherical_vs_unscented_transform(n):
     """Assert that UT with r=0 equals the third-order spherical rule."""
     tos = ivpsolvers.cubature_third_order_spherical(input_shape=(n,))
     ut = ivpsolvers.cubature_unscented_transform(input_shape=(n,), r=0.0)

tests/test_taylor/test_affine_recursion.py

@@ -6,7 +6,8 @@
 
 
 @testing.parametrize("num", [1, 2, 4])
-def test_affine_recursion(num, num_derivatives_max=5):
+@testing.parametrize("num_derivatives_max", [5])
+def test_affine_recursion(num, num_derivatives_max):
     """The approximation should coincide with the reference."""
     f, init, solution = _affine_problem(num_derivatives_max)