DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Haoyu Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin

TL;DR
DIG-MILP is a deep generative framework that creates realistic and feasible MILP instances from limited data, aiding solver tuning and machine learning model training.
Contribution
It introduces a VAE-based deep generative model that guarantees feasibility and boundedness of generated MILP instances using duality principles.
Findings
Generated instances closely match original data in solver solution times
DIG-MILP enhances machine learning model performance on MILP tasks
Supports data sharing without exposing original problem data
Abstract
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. The development of effective algorithms, the tuning of solvers, and the training of machine learning models for MILP resolution all hinge on access to extensive, diverse, and representative data. Yet compared to the abundant naturally occurring data in image and text realms, MILP is markedly data deficient, underscoring the vital role of synthetic MILP generation. We present DIG-MILP, a deep generative framework based on variational auto-encoder (VAE), adept at extracting deep-level structural features from highly limited MILP data and producing instances that closely mirror the target data. Notably, by leveraging the MILP duality, DIG-MILP guarantees a correct and complete generation space as well as ensures the boundedness and feasibility of the generated…
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Taxonomy
TopicsPlant biochemistry and biosynthesis · Process Optimization and Integration · Constraint Satisfaction and Optimization
