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

• URL: http://arxiv.org/abs/2310.02807v3
• Date: Mon, 11 Mar 2024 10:51:14 GMT
• Title: A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
• Authors: Zijie Geng, Xijun Li, Jie Wang, Xiao Li, Yongdong Zhang, Feng Wu
• Abstract summary: We propose G2MILP, the first deep generative framework for MILP instances. G2MILP represents MILP instances as bipartite graphs, and applies a masked variational autoencoder to iteratively corrupt and replace parts of the original graphs to generate new ones. We design a suite of benchmarks to evaluate the quality of the generated MILP instances.
• Score: 66.37474135424637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-integer linear programs (MILPs). Despite the achievements, the limited availability of real-world instances often leads to sub-optimal decisions and biased solver assessments, which motivates a suite of synthetic MILP instance generation techniques. However, existing methods either rely heavily on expert-designed formulations or struggle to capture the rich features of real-world instances. To tackle this problem, we propose G2MILP, the first deep generative framework for MILP instances. Specifically, G2MILP represents MILP instances as bipartite graphs, and applies a masked variational autoencoder to iteratively corrupt and replace parts of the original graphs to generate new ones. The appealing feature of G2MILP is that it can learn to generate novel and realistic MILP instances without prior expert-designed formulations, while preserving the structures and computational hardness of real-world datasets, simultaneously. Thus the generated instances can facilitate downstream tasks for enhancing MILP solvers under limited data availability. We design a suite of benchmarks to evaluate the quality of the generated MILP instances. Experiments demonstrate that our method can produce instances that closely resemble real-world datasets in terms of both structures and computational hardness. The deliverables are released at https://miralab-ustc.github.io/L2O-G2MILP.

Related papers

• MILP-StuDio: MILP Instance Generation via Block Structure Decomposition [55.79888361191114]
  Mixed-integer linear programming (MILP) is one of the most popular mathematical formulations with numerous applications. We propose a novel MILP generation framework, called Block Structure Decomposition (MILP-StuDio), to generate high-quality instances by preserving the block structures.
  arXiv  Detail & Related papers  (2024-10-30T08:33:27Z)
• DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee [47.11455377400096]
  Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. 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.
  arXiv  Detail & Related papers  (2023-10-20T03:45:29Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers [13.790116387956703]
  Mixed-integer programming (MIP) technology offers a generic way of formulating and solving optimization problems. MIP-GNN is a general framework for enhancing such solvers with data-driven insights. We integrate MIP-GNN into a state-of-the-art MIP solver, applying it to tasks such as node selection and warm-starting.
  arXiv  Detail & Related papers  (2022-05-27T19:34:14Z)
• Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs [0.0]
  Current solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems. Recent works have shown that machine learning (ML) can be integrated with an MIP solver to inject domain knowledge and efficiently close the optimality gap. This paper proposes an online solver that uses the notion of entropy to efficiently build a model with minimal training data and tuning.
  arXiv  Detail & Related papers  (2022-02-07T18:52:56Z)
• Learning Mixed-Integer Linear Programs from Contextual Examples [37.56298025474654]
  Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research. In this paper, we study the problem of acquiring MILPs from contextual examples. We also contribute MISSLE, an algorithm for learning MILPs from contextual examples.
  arXiv  Detail & Related papers  (2021-07-15T05:45:52Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.