add ReadMe and clean up

README.md

@@ -1,23 +1,30 @@
 
 ![cbx](https://github.com/PdIPS/CBXpy/assets/44805883/65e7f1b2-e858-4b8d-af37-8eeaea15214c)
 
-# What is CBXPy?
+A python package for consensus-based particle dynamics, focusing on **optimization** and **sampling**. 
 
-```CBXPy``` is a python package for consensus-based particle dynamics, focusing on **optimization** and **sampling**. Minimizing a function using CBXPy can be done as follows
+# How to use CBXPy?
+
+Minimizing a function using CBXPy can be done as follows
 
 ```python
-   from cbx.dynamics import CBO
+   from cbx.dynamics import CBO       # import the CBO class
 
-   f = lambda x: x[0]**2 + x[1]**2
-   dyn = CBO(f, d=2)
-   x = dyn.optimize()
+   f = lambda x: x[0]**2 + x[1]**2     # define the function to minimize
+   x = CBO(f, d=2).optimize()          # run the optimization
 ```
 
-A Documentation is available at [https://pdips.github.io/CBXpy](https://pdips.github.io/CBXpy)
+A documentation together with more examples and usage instructions is available at [https://pdips.github.io/CBXpy](https://pdips.github.io/CBXpy).
 
 
 # Installation
 
+Currently ```CBXPy``` can only be installed from PyPI with pip
+
+```bash
+   pip install cbx
+```
+
 
 
 # What is CBX?
@@ -31,7 +38,7 @@ $$
 the scheme was introduced as CBO (Consensus Based Optimization). Given an ensemble of points $x = (x_1, \ldots, x_N)$, the update reads
 
 $$
-x_i \gets x_i - \lambda\ dt\ (x_i - c(x)) + \sigma\ \sqrt{dt} |x_i - c(x)| \xi_i
+x_i \gets x_i - \lambda\ dt\ (x_i - c(x)) + \sigma\ \sqrt{dt} |x_i - c(x)|\ \xi_i
 $$
 
 where $\xi_i$ are i.i.d. standard normal random variables. The core element is the consensus point
@@ -45,6 +52,22 @@ $$
 with a parameter $\alpha>0$. The scheme can be extended to sampling problems  known as CBS, clustering problems and opinion dynamics, which motivates the acronym 
 **CBX**, indicating the flexibility of the scheme.
 
+## Functionality
+
+Among others CBXPy currently implments
+
+* CBO (Consensus Based Optimization) [[1]](#CBO),
+* CBS (Consensus Based Sampling) [[2]](#CBS),
+* CBO with memory [[3]](#CBOMemory),
+* Batching schemes [[4]](#Batching).
+
+
+## References
+
+<a name="CBO">[1]</a> A consensus-based model for global optimization and its mean-field limit, Pinnau, R., Totzeck, C., Tse, O. and Martin, S., Mathematical Models and Methods in Applied Sciences 2017
+
+<a name="CBS">[2]</a> Consensus-based sampling, Carrillo, J.A., Hoffmann, F., Stuart, A.M., and Vaes, U, Studies in Applied Mathematics 2022
 
+<a name="CBOMemory">[3]</a> Leveraging Memory Effects and Gradient Information in Consensus-Based Optimization: On Global Convergence in Mean-Field Law, Riedl, K., 2022
 
-# Usage examples
+<a name="Batching">[4]</a> A consensus-based global optimization method for high dimensional machine learning problems, Carrillo, J.A., Jin, S., Li, L. and Zhu, Y., ESAIM: Control, Optimisation and Calculus of Variations 2021

pyproject.toml

@@ -1,8 +1,12 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
 [project]
 name="cbx"
-version="0.1.0"
+version="0.1.1"
 authors = [
-    {name = "Tim Roith", email = "tim.roith@fau.de"},
+    {name = "Tim Roith", email = "tim.roith@desy.de"},
 ]
 description="CBXpy"
 dependencies = [  
@@ -11,23 +15,23 @@ dependencies = [
     'scikit-learn', 
     'matplotlib'
     ]
-readme = "README.rst"
+readme = "README.md"
 requires-python = ">3.5.2"
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent"
     ]
 
-[tool.setuptools.packages]
-find = {}  # Scan the project directory with the default parameters
-
 [project.optional-dependencies] # Optional dependencies
 test = [
     'pytest',
     'torch'
     ]
 
+[tool.setuptools]
+packages = ['cbx', 'cbx.dynamics', 'cbx.utils']
+
 
 [tool.ruff]
 # Enable flake8-bugbear (`B`) rules.