branch-and-bound

本文介绍了一种用于非线性神经反馈系统可扩展验证的分支定界框架，通过结合组合求解器和传播求解器（如 rail 和 clipper 工具）来改进现有最优方法。

本文提出一种方法，用于确定地球上避开陆地或主要水体的最长直线路径，使用分支定界算法解决优化挑战。

This paper introduces an inprocessing framework for neural network verification driven by lookahead lemmas, improving the performance of verifiers Marabou and α-β-CROWN by proving up to 34% more instances unsatisfiable.

This paper proposes efficient search methods to locate verdict boundaries in Branch and Bound (BaB) neural network verification, leveraging path monotonicity to skip irrelevant subproblems and improve verification efficiency.