CHANGELOG.md

@@ -3,16 +3,7 @@
 All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
-and this project adheres to
-[Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [0.4.2] - 2024-12-10
-
-## Changed
-
-- H5File: Use float64 precision instead of float32
-- LearningSolver: optimize now returns (model, stats) instead of just stats
-- Update dependency: Gurobi 11
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [0.4.0] - 2024-02-06
 
@@ -24,41 +15,31 @@ and this project adheres to
 ### Changed
 
 - LearningSolver.solve no longer generates HDF5 files; use a collector instead.
-- Add `_gurobipy` suffix to all `build_model` functions; implement some `_pyomo`
-  and `_jump` functions.
+- Add `_gurobipy` suffix to all `build_model` functions; implement some `_pyomo` and `_jump` functions.
 
 ## [0.3.0] - 2023-06-08
 
-This is a complete rewrite of the original prototype package, with an entirely
-new API, focused on performance, scalability and flexibility.
+This is a complete rewrite of the original prototype package, with an entirely new API, focused on performance, scalability and flexibility.
 
 ### Added
 
-- Add support for Python/Gurobipy and Julia/JuMP, in addition to the existing
-  Python/Pyomo interface.
-- Add six new random instance generators (bin packing, capacitated p-median, set
-  cover, set packing, unit commitment, vertex cover), in addition to the three
-  existing generators (multiknapsack, stable set, tsp).
-- Collect some additional raw training data (e.g. basis status, reduced costs,
-  etc)
-- Add new primal solution ML strategies (memorizing, independent vars and joint
-  vars)
-- Add new primal solution actions (set warm start, fix variables, enforce
-  proximity)
+- Add support for Python/Gurobipy and Julia/JuMP, in addition to the existing Python/Pyomo interface.
+- Add six new random instance generators (bin packing, capacitated p-median, set cover, set packing, unit commitment, vertex cover), in addition to the three existing generators (multiknapsack, stable set, tsp).
+- Collect some additional raw training data (e.g. basis status, reduced costs, etc)
+- Add new primal solution ML strategies (memorizing, independent vars and joint vars)
+- Add new primal solution actions (set warm start, fix variables, enforce proximity)
 - Add runnable tutorials and user guides to the documentation.
 
 ### Changed
 
-- To support large-scale problems and datasets, switch from an in-memory
-  architecture to a file-based architecture, using HDF5 files.
-- To accelerate development cycle, split training data collection from feature
-  extraction.
+- To support large-scale problems and datasets, switch from an in-memory architecture to a file-based architecture, using HDF5 files.
+- To accelerate development cycle, split training data collection from feature extraction.
 
 ### Removed
 
 - Temporarily remove ML strategies for lazy constraints
-- Remove benchmarks from documentation. These will be published in a separate
-  paper.
+- Remove benchmarks from documentation. These will be published in a separate paper.
+
 
 ## [0.1.0] - 2020-11-23
 

docs/tutorials/getting-started-gurobipy.ipynb

@@ -45,10 +45,16 @@
     "- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
     "- Julia version, compatible with the JuMP modeling language.\n",
     "\n",
-    "In this tutorial, we will demonstrate how to use and install the Python/Gurobipy version of the package. The first step is to install Python 3.9+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
+    "In this tutorial, we will demonstrate how to use and install the Python/Gurobipy version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
     "\n",
     "```\n",
-    "$ pip install MIPLearn~=0.4\n",
+    "$ pip install MIPLearn==0.4\n",
+    "```\n",
+    "\n",
+    "In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
+    "\n",
+    "```\n",
+    "$ pip install 'gurobipy>=10,<10.1'\n",
     "```"
    ]
   },

docs/tutorials/getting-started-pyomo.ipynb

@@ -45,10 +45,16 @@
     "- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
     "- Julia version, compatible with the JuMP modeling language.\n",
     "\n",
-    "In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.9+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
+    "In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
     "\n",
     "```\n",
-    "$ pip install MIPLearn~=0.4\n",
+    "$ pip install MIPLearn==0.4\n",
+    "```\n",
+    "\n",
+    "In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
+    "\n",
+    "```\n",
+    "$ pip install 'gurobipy>=10,<10.1'\n",
     "```"
    ]
   },

setup.py

@@ -6,7 +6,7 @@ from setuptools import setup, find_namespace_packages
 
 setup(
     name="miplearn",
-    version="0.4.2",
+    version="0.4.0",
     author="Alinson S. Xavier",
     author_email="axavier@anl.gov",
     description="Extensible Framework for Learning-Enhanced Mixed-Integer Optimization",
@@ -15,7 +15,7 @@ setup(
     python_requires=">=3.9",
     install_requires=[
         "Jinja2<3.1",
-        "gurobipy>=11,<12",
+        "gurobipy>=10,<11",
         "h5py>=3,<4",
         "networkx>=2,<3",
         "numpy>=1,<2",
@@ -36,13 +36,8 @@ setup(
             "pyflakes==2.5.0",
             "pytest>=7,<8",
             "sphinx-book-theme==0.1.0",
-            "sphinxcontrib-applehelp==1.0.4",
-            "sphinxcontrib-devhelp==1.0.2",
-            "sphinxcontrib-htmlhelp==2.0.1",
-            "sphinxcontrib-serializinghtml==1.1.5",
-            "sphinxcontrib-qthelp==1.0.3",
             "sphinx-multitoc-numbering>=0.1,<0.2",
-            "twine>=6,<7",
+            "twine>=4,<5",
         ]
     },
 )