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  3. Promoting Generalization For Exact Combinatorial Solvers Via Adversarial Instance Augmentation.
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  3. Promoting Generalization For Exact Combinatorial Solvers Via Adversarial Instance Augmentation.

Related Experiment Video

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Promoting Generalization for Exact Combinatorial Solvers via Adversarial Instance Augmentation.

Haoyang Liu, Jie Wang, Yufei Kuang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 1, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    AdaSolver enhances machine learning solvers for Mixed-Integer Linear Programming (MILP) by using adversarial instance augmentation to improve data diversity. This approach significantly boosts solver performance and sample efficiency on diverse MILP problems.

    Related Experiment Videos

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    Area of Science:

    • Artificial Intelligence
    • Operations Research
    • Computer Science

    Background:

    • Machine learning accelerates Mixed-Integer Linear Programming (MILP) solvers.
    • Learning-based solvers face performance degradation on unseen instances due to limited training data diversity.

    Purpose of the Study:

    • Propose Adversarial Instance Augmentation (AdaSolver) to enhance data diversity for MILP solvers.
    • Improve the generalization capabilities of learning-based branch-and-bound (B&B) solvers.

    Main Methods:

    • AdaSolver augments MILP instance structures using an augmentation policy on bipartite graphs.
    • Formulates the augmentation policy learning as a contextual bandit problem for adversarial training.
    • Enables gradient-based adversarial training for both the solver and augmentation policy.

    Main Results:

    • AdaSolver achieves approximately 35% improvement in solving time across various distributions.
    • Demonstrates significant sample efficiency gains, reducing solving time by 40% compared to GNN baselines with only 1% of training data.

    Conclusions:

    • AdaSolver is the first general framework to improve generalization for imitation-learning and reinforcement-learning-based B&B solvers.
    • The method effectively addresses the data diversity challenge in learning-based MILP solvers.