Mixed Integer

Mixed-integer programming (MIP) focuses on optimization problems involving both continuous and discrete variables, aiming to find optimal solutions within constraints. Current research emphasizes improving the efficiency and scalability of MIP solvers, particularly for large-scale problems, through techniques like integrating machine learning models (e.g., neural networks, transformers) to accelerate solution finding and improve cut selection strategies within branch-and-bound algorithms. These advancements have significant implications for various fields, including robotics, logistics, and machine learning itself, by enabling the solution of previously intractable optimization problems and enhancing the performance of data-driven methods.

Papers

February 4, 2024

Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
Hongyu Cheng, Sammy Khalife, Barbara Fiedorowicz, Amitabh Basu
Neural Network Financial Application Data Driven Sample Complexity Mixed Integer Algorithm Selection Branch and Cut

January 31, 2024

Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming
Haotian Ling, Zhihai Wang, Jie Wang
LeArning Abstract Mixed Integer Reinforcement Learning Problem Mixed Integer Linear Dynamic Decision Cutting Plane MILP Solver

January 19, 2024

Learning Backdoors for Mixed Integer Programs with Contrastive Learning
Junyang Cai, Taoan Huang, Bistra Dilkina
Contrastive Learning Backdoor Attack Monte Carlo Tree Search Mixed Integer Backdoor Learning

January 17, 2024

Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
Niki Triantafyllou, Maria M. Papathanasiou
Deep Learning Deep Learning Model Mixed Integer Output Space Facility Location Problem

December 20, 2023

Task Planning for Multiple Item Insertion using ADMM
Gavin Zheng
Task Planning Mixed Integer Alternating Direction Method of Multiplier Nonlinear Programming Test Point Insertion

December 13, 2023

PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs
Mark Turner, Antonia Chmiela, Thorsten Koch, Michael Winkler
Machine Learning Machine Learning Model Mixed Integer Constrained Learning Scikit Learn

December 2, 2023

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
Graph Neural Network Mixed Integer Molecular Design Global Optimality

November 26, 2023

Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning
Jingtao Qin, Nanpeng Yu
Graph Neural Network Mixed Integer Graph Neural Network Model Unit Commitment

November 22, 2023

BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
Vassilis Digalakis, Christos Ziakas
Unsupervised Learning Mixed Integer Easy to Use Library Backbone Fine Tuning Interpretable Learning

November 7, 2023

Practical Performance Guarantees for Pipelined DNN Inference
Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu
Deep Neural Network Mixed Integer Performance Guarantee Pipeline Parallelism Optimality Gap Inference Pipeline

November 3, 2023

Global Optimization: A Machine Learning Approach
Dimitris Bertsimas, Georgios Margaritis
Robust Optimization Machine Learning Approach Mixed Integer Global Optimization Constraint Learning Global Optimization Problem

October 30, 2023

Solving a Class of Cut-Generating Linear Programs via Machine Learning
Atefeh Rajabalizadeh, Danial Davarnia
Machine Learning Mixed Integer True Class Branch and Bound Graph Cut Branch and Cut

October 16, 2023

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Chendi Qian, Didier Chételat, Christopher Morris
Graph Neural Network Real Power Mixed Integer Linear Programming Message Passing Graph Neural Network

October 12, 2023

Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming
Cora A. Dimmig, Kevin C. Wolfe, Bradley Woosley, Marin Kobilarov, Joseph Moore
Multi Robot Dynamic Graph Mixed Integer Uncertainty Aware Robot Team Multi Robot Team Heterogeneous Robot Team

October 10, 2023

Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
Ilias Mitrai, Prodromos Daoutidis
Artificial Intelligence Human Generated Graph Classification Mixed Integer Decomposition Method

October 9, 2023

Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
Ricardo Knauer, Erik Rodner
Machine Learning Feature Selection Mixed Integer Logistic Regression Prognostic Model Best Subset

October 5, 2023

Taming Binarized Neural Networks and Mixed-Integer Programs
Johannes Aspman, Georgios Korpas, Jakub Marecek
Neural Network Back Propagation Mixed Integer Automatic Differentiation Implicit Differentiation

October 4, 2023

A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Zijie Geng, Xijun Li, Jie Wang, Xiao Li, Yongdong Zhang, Feng Wu
Mixed Integer MILP Solver

September 1, 2023

Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches
Bruno Machado Pacheco, Laio Oriel Seman, Eduardo Camponogara
Constructive Approach Weakly Supervised Mixed Integer Oil Well Heuristic Learning Fixed Point Iteration

August 21, 2023

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients
Mehak Arora, Hassan Mortagy, Nathan Dwarshius, Swati Gupta, Andre L. Holder, Rishikesan Kamaleswaran
Machine Learning Machine Learning Model Mixed Integer Patient Data Standard Sepsis Hospitalization Risk Metric Prediction

Mixed Integer

Papers

Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut

Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming

Learning Backdoors for Mixed Integer Programs with Contrastive Learning

Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality

Task Planning for Multiple Item Insertion using ADMM

PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs

Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design

Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning

BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning

Practical Performance Guarantees for Pipelined DNN Inference

Global Optimization: A Machine Learning Approach

Solving a Class of Cut-Generating Linear Programs via Machine Learning

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems

Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming

Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose

Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective

Taming Binarized Neural Networks and Mixed-Integer Programs

A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability

Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients