线性混合系统伪造验证与下降法
Linear Hybrid System Falsification With Descent
AI总结：
针对仿射动力学混合自动机的伪造验证问题，提出一种基于序列二次规划的局部搜索方法，并与高层随机优化结合，实验表明该方法优于纯随机优化。
摘要：
本文研究了仿射动力学混合自动机伪造验证中的局部搜索问题。即，给定一个位置序列和最大仿真时间，我们返回最接近不安全集的轨迹。为了解决这个问题，我们将其表述为一个可微优化问题，并使用序列二次规划求解。开发这种局部搜索方法的目的是将其与高层随机优化算法相结合，以伪造具有复杂离散动力学和高维连续空间的混合系统。实验结果表明，局部搜索过程确实改进了纯随机优化算法的结果。
英文摘要：
In this paper, we address the problem of local search for the falsification of hybrid automata with affine dynamics. Namely, if we are given a sequence of locations and a maximum simulation time, we return the trajectory that comes the closest to the unsafe set. In order to solve this problem, we formulate it as a differentiable optimization problem which we solve using Sequential Quadratic Programming. The purpose of developing such a local search method is to combine it with high level stochastic optimization algorithms in order to falsify hybrid systems with complex discrete dynamics and high dimensional continuous spaces. Experimental results indicate that indeed the local search procedure improves upon the results of pure stochastic optimization algorithms.
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。
