Soy：一种用于分段仿射系统的高效MILP求解器
Soy: An Efficient MILP Solver for Piecewise-Affine Systems
AI总结：
针对PWA系统MICP控制问题，本文利用one-hot约束结构提出融合逻辑、算术推理与随机局部搜索的专用求解器Soy，在基准上比两款先进MICP求解器求解更多且更快。
摘要：
分段仿射（PWA）系统被广泛用于机器人问题的建模与控制，包括对接触动力学进行建模。一种常见方法是将PWA系统的控制问题编码为混合整数凸规划（MICP），并可由通用的现成MICP求解器求解。为缓解求解这些MICP问题时的可扩展性挑战，现有工作集中于设计高效且强的问题表述，而较少投入精力利用其特定结构来开发专用求解器。后者正是本文工作的主题。我们关注高效处理one-hot约束，这类约束在编码PWA动力学时尤为重要。我们已将相关技术实现为一个名为Soy的工具，它将逻辑推理、算术推理和随机局部搜索有机结合。在一组PWA控制基准上，与两款最先进的MICP求解器相比，Soy求解了更多问题，且速度更快。
英文摘要：
Piecewise-affine (PWA) systems are widely used for modeling and control of robotics problems including modeling contact dynamics. A common approach is to encode the control problem of the PWA system as a Mixed-Integer Convex Program (MICP), which can be solved by general-purpose off-the-shelf MICP solvers. To mitigate the scalability challenge of solving these MICP problems, existing work focuses on devising efficient and strong formulations of the problems, while less effort has been spent on exploiting their specific structure to develop specialized solvers. The latter is the theme of our work. We focus on efficiently handling one-hot constraints, which are particularly relevant when encoding PWA dynamics. We have implemented our techniques in a tool, Soy, which organically integrates logical reasoning, arithmetic reasoning, and stochastic local search. For a set of PWA control benchmarks, Soy solves more problems, faster, than two state-of-the-art MICP solvers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
