mixed-integer-optimization

Represent trained machine learning models as Pyomo optimization formulations

An end-to-end framework for mixed-integer optimization with data-driven learned constraints.

Julia interface to SCIP solver

Python API for AMPL

An extensible MINLP solver

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs

AMPL Model Colaboratory

A Julia/JuMP Package for Maximizing Algebraic Connectivity of Undirected Weighted Graphs

Special Structure Detection for Pyomo

R API for AMPL

Mixed-Integer MPC library. Was successfully demonstrated on a free-floating platform with binary thrusters (under timing constraints) and a reaction wheel.

ALChemE is a software to assist users in calculations related to the chemical engineering field and process design. Currently, the software can assist users with the analysis of heat exchange networks (HENs) and water recycle networks (WReNs).

This repository contains the code examples of the book "Einführung in Optimierungsmodelle" ("Introduction to Optimization Models") (Sudermann-Merx, 2023)

Quantum control algorithms for multiple examples based on the package Qutip

This project addresses the real-world portfolio optimization problem, going beyond classical mean-variance models. Actual portfolio construction involves discrete investment decisions, transaction costs, and monitoring constraints, making the problem a Mixed-Integer Optimization (MIO) challenge that is computationally intractable at scale

Advanced Enclosure Algorithm for Multi-Objective Optimization Problems

Reference implementation and reproduction scripts for the paper Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control.

Implementation of the Hybrid Patch Decomposition Algorithm - a solver for multi-objective mixed-integer convex optimization problems

Code and results for quantum optimal control based on switching time optimization

Likely Counterfactual Explanations - a MIO-based method for CE generation, using Sum-Product 网络s to ensure plausibility