论文标题
最佳气流中的数据驱动的热启动方法，用于凸出放松
A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow
论文作者
论文摘要
在这封信中，我们提出了一种由人工神经网络授权的数据驱动的热门方法，以提高最佳气流中凸松弛的效率。案例研究表明，这种方法大大减少了凸 - 孔孔程序算法的迭代次数，并且仍然可以保证溶液的最优性和可行性。
In this letter, we propose a data-driven warm start approach, empowered by artificial neural networks, to boost the efficiency of convex relaxations in optimal gas flow. Case studies show that this approach significantly decreases the number of iterations for the convex-concave procedure algorithm, and optimality and feasibility of the solution can still be guaranteed.
