Alignment with scipy (#504)

.github/workflows/main.yml

@@ -120,9 +120,9 @@ jobs:
     strategy:
       fail-fast: false
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: create build environment
-        uses: mamba-org/provision-with-micromamba@main
+        uses: mamba-org/setup-micromamba@v1
         with:
           environment-file: ./.envs/testenv-linux.yml
           environment-name: optimagic

.pre-commit-config.yaml

@@ -50,8 +50,6 @@ repos:
       - id: trailing-whitespace
         exclude: docs/
       - id: check-ast
-      - id: check-docstring-first
-        exclude: src/optimagic/optimization/algo_options.py
   - repo: https://github.com/adrienverge/yamllint.git
     rev: v1.35.1
     hooks:

CHANGES.md

@@ -18,10 +18,43 @@ the major changes are:
 - Rename the package from `estimagic` to `optimagic` (while keeping the `estimagic`
   namespace for the estimation capabilities).
 
-### Breaking changes
 
 - {gh}`500` removes the dashboard, the support for simopt optimizers and the
   `derivative_plot` ({ghuser}`janosg`)
+- {gh}`504` aligns `maximize` and `minimize` more closely with scipy. All related
+  deprecations and breaking changes are listed below. As a result, scipy code that uses
+  minimize with the arguments `x0`, `fun`, `jac` and `method` will run without changes
+  in optimagic. Similarly, to `OptimizeResult` gets some aliases so it behaves more
+  like SciPy's.
+
+### Breaking changes
+
+
+### Deprecations
+
+- The `criterion` argument of `maximize` and `minimize` is renamed to `fun` (as in
+  SciPy).
+- The `derivative` argument of `maximize` and `minimize` is renamed to `jac` (as
+  in SciPy)
+- The `criterion_and_derivative` argument of `maximize` and `minimize` is renamed
+  to `fun_and_jac` to align it with the other names.
+- The `criterion_kwargs` argument of `maximize` and `minimize` is renamed to
+  `fun_kwargs` to align it with the other names.
+- The `derivative_kwargs` argument of `maximize` and `minimize` is renamed to
+  `jac_kwargs` to align it with the other names.
+- The `criterion_and_derivative_kwargs` argument of `maximize` and `minimize` is
+  renamed to `fun_and_jac_kwargs` to align it with the other names.
+- Algorithm specific convergence and stopping criteria are renamed to align them more
+  with NlOpt and SciPy names.
+    - `convergence_relative_criterion_tolerance` -> `convergence_ftol_rel`
+    - `convergence_absolute_criterion_tolerance` -> `convergence_ftol_abs`
+    - `convergence_relative_params_tolerance` -> `convergence_xtol_rel`
+    - `convergence_absolute_params_tolerance` -> `convergence_xtol_abs`
+    - `convergence_relative_gradient_tolerance` -> `convergence_gtol_rel`
+    - `convergence_absolute_gradient_tolerance` -> `convergence_gtol_abs`
+    - `convergence_scaled_gradient_tolerance` -> `convergence_gtol_scaled`
+    - `stopping_max_criterion_evaluations` -> `stopping_maxfun`
+    - `stopping_max_iterations` -> `stopping_maxiter`
 
 
 ## 0.4.7

README.md

@@ -12,39 +12,21 @@
 [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![image](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CODE_OF_CONDUCT.md)
 [![image](https://pepy.tech/badge/estimagic/month)](https://pepy.tech/project/estimagic)
+[![image](https://img.shields.io/badge/NumFOCUS-affiliated%20project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org/sponsored-projects/affiliated-projects)
+[![image](https://img.shields.io/twitter/follow/aiidateam.svg?style=social&label=Follow)](https://x.com/optimagic)
 
 ## Introduction
 
-optimagic is a Python package for nonlinear optimization with or without constraints. It
-is particularly suited to solve difficult nonlinear estimation problems. On top, it
-provides functionality to perform statistical inference on estimated parameters.
+*optimagic* is a Python package for numerical optimization. It is a unified interface to
+optimizers from SciPy, NlOpt and many other Python packages.
 
-### Optimization
+*optimagic*'s `minimize` function works just like SciPy's, so you don't have to adjust
+your code. You simply get more optimizers for free. On top you get powerful diagnostic
+tools, parallel numerical derivatives and more.
 
-- optimagic wraps algorithms from *scipy.optimize*, *nlopt*, *pygmo* and more.
-- optimagic implements constraints efficiently via reparametrization, so you can solve
-  constrained problems with any optimizer that supports bounds.
-- The parameters of an optimization problem can be arbitrary pytrees
-- The complete history of parameters and function evaluations can be saved in a database
-  for maximum reproducibility.
-- Painless and efficient multistart optimization.
-- The progress of the optimization can be displayed in `criterion_plot` and
-  `params_plot` while the optimization is still running.
-
-### Estimation and Inference
-
-- You can estimate a model using method of simulated moments (MSM), calculate standard
-  errors and do sensitivity analysis with just one function call.
-- Asymptotic standard errors for maximum likelihood estimation.
-- estimagic also provides bootstrap confidence intervals and standard errors. Of course
-  the bootstrap procedures are parallelized.
-
-### Numerical differentiation
-
-- optimagic can calculate precise numerical derivatives using
-  [Richardson extrapolations](https://en.wikipedia.org/wiki/Richardson_extrapolation).
-- Function evaluations needed for numerical derivatives can be done in parallel with
-  pre-implemented or user provided batch evaluators.
+*optimagic* was formerly called *estimagic*, because it also provides functionality to
+perform statistical inference on estimated parameters. *estimagic* is now a subpackage
+of *optimagic*.
 
 ## Installation
 
@@ -101,3 +83,14 @@ If you use optimagic for your research, please do not forget to cite it.
   Url    = {https://github.com/OpenSourceEconomics/optimagic}
 }
 ```
+
+## Acknowledgements
+
+We thank all institutions that have funded or supported optimagic (formerly estimagic)
+
+<img src="docs/source/_static/images/aai-institute-logo.svg" width="185">
+<img src="docs/source/_static/images/numfocus_logo.png" width="200">
+<img src="docs/source/_static/images/tra_logo.png" width="240">
+
+<img src="docs/source/_static/images/hoover_logo.png" width="192">
+<img src="docs/source/_static/images/transferlab-logo.svg" width="400">