Incorporate observations from EPP lecture

.pre-commit-config.yaml

@@ -45,7 +45,7 @@ repos:
         types: [python]
 
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.2.3
+    rev: v3.3.1
     hooks:
     -   id: pyupgrade
         args: [--py38-plus]
@@ -58,7 +58,7 @@ repos:
         exclude: docs/source/how_to_guides/optimization/how_to_specify_constraints.md
 
 -   repo: https://github.com/psf/black
-    rev: "22.10.0"
+    rev: "22.12.0"
     hooks:
       - id: black
         language_version: python3.9
@@ -87,6 +87,6 @@ repos:
         additional_dependencies: [isort==5.6.4]
 
 -   repo: https://github.com/mgedmin/check-manifest
-    rev: "0.48"
+    rev: "0.49"
     hooks:
     -   id: check-manifest

docs/source/algorithms.md

@@ -3686,7 +3686,7 @@ using an NLOPT algorithm. To install nlopt run `conda install nlopt`.
 estimagic supports the following [SimOpt](https://github.com/simopt-admin/simopt)
 algorithms. Please add the [appropriate
 citations](https://github.com/simopt-admin/simopt) in addition to estimagic when using a
-SimOpt algorithm. To install simopt run `pip install simoptlib`.
+SimOpt algorithm. To install simopt run `pip install simoptlib==1.0.1`.
 
 ```{eval-rst}
 .. dropdown:: simopt_adam

docs/source/getting_started/first_optimization_with_estimagic.ipynb

@@ -687,7 +687,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('base')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -701,7 +701,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.13"
   },
   "vscode": {
    "interpreter": {

docs/source/how_to_guides/optimization/index.md

@@ -15,6 +15,7 @@ how_to_handle_errors_during_optimization.ipynb
 how_to_scale_optimization_problems
 how_to_do_multistart_optimizations.ipynb
 how_to_benchmark_optimization_algorithms.ipynb
+how_to_visualize_histories.ipynb
 how_to_visualize_an_optimization_problem.ipynb
 how_to_pick_an_optimizer.ipynb
 ```

setup.cfg

@@ -50,12 +50,13 @@ install_requires =
     numpy>=1.17
     pandas>=1.0
     bokeh>=1.3
-    scipy
+    scipy>=1.7
     fuzzywuzzy
     sqlalchemy>=1.3
     plotly
     dill
     pybaum
+    numba
 
 
 [options.packages.find]

src/estimagic/optimization/scipy_optimizers.py

@@ -143,6 +143,8 @@ def scipy_slsqp(
 def scipy_neldermead(
     criterion,
     x,
+    lower_bounds,
+    upper_bounds,
     *,
     stopping_max_iterations=STOPPING_MAX_ITERATIONS,
     stopping_max_criterion_evaluations=STOPPING_MAX_CRITERION_EVALUATIONS,
@@ -168,6 +170,7 @@ def scipy_neldermead(
     res = scipy.optimize.minimize(
         fun=criterion,
         x0=x,
+        bounds=_get_scipy_bounds(lower_bounds, upper_bounds),
         method="Nelder-Mead",
         options=options,
     )