MIPLearn 0.4.0

Added

• Add ML strategies for user cuts
• Add ML strategies for lazy constraints

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.

MIPLearn 0.3.0

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 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.

Removed

• Temporarily remove ML strategies for lazy constraints
• Remove benchmarks from documentation. These will be published in a separate paper.

v0.1

Initial public release