Train timetable rescheduling (TTR) aims to promptly restore the original
operation of trains after unexpected disturbances or disruptions. Currently,
this work is still done manually by train dispatchers, which is challenging to
maintain performance under various problem instances. To mitigate this issue,
this study proposes a reinforcement learning-based approach to TTR, which makes
the following contributions compared to existing work. First, we design a
simple directed graph to represent the TTR problem, enabling the automatic
extraction of informative states through graph neural networks. Second, we
reformulate the construction process of TTR's solution, not only decoupling the
decision model from the problem size but also ensuring the generated scheme's
feasibility. Third, we design a learning curriculum for our model to handle the
scenarios with different levels of delay. Finally, a simple local search method
is proposed to assist the learned decision model, which can significantly
improve solution quality with little additional computation cost, further
enhancing the practical value of our method. Extensive experimental results
demonstrate the effectiveness of our method. The learned decision model can
achieve better performance for various problems with varying degrees of train
delay and different scales when compared to handcrafted rules and
state-of-the-art solvers.

火车时刻表重新调度的研究中，通过基于强化学习的方法，设计了一个简单的有向图来表示问题，并利用图神经网络自动提取信息状态；通过改变解决方案的构建过程，使得决策模型与问题规模解耦合，确保生成方案的可行性；为模型设计了一个学习课程来处理不同程度延误的情况；并且提出了一个简单的局部搜索方法，显著提高了解决方案的质量，实验结果证明了该方法在解决各种不同程度火车延误和规模问题时比手工规则和现有解决方法具有更好的性能。

基于图表示的可扩展列车时刻表重排的强化学习

Reinforcement Learning for Scalable Train Timetable Rescheduling with  Graph Representation

We consider the problem of phase retrieval, namely, recovery of a signal from
the magnitude of its Fourier transform, or of any other linear transform. Due
to the loss of the Fourier phase information, this problem is ill-posed.
Therefore, prior information on the signal is needed in order to enable its
recovery. In this work we consider the case in which the signal is known to be
sparse, i.e., it consists of a small number of nonzero elements in an
appropriate basis. We propose a fast local search method for recovering a
sparse signal from measurements of its Fourier transform (or other linear
transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse
Retrieval. Our algorithm does not require matrix lifting, unlike previous
approaches, and therefore is potentially suitable for large scale problems such
as images. Simulation results indicate that GESPAR is fast and more accurate
than existing techniques in a variety of settings.

本研究考虑通过傅里叶变换或其他线性变换结果的幅度来恢复信号的相位信息，从而实现信号恢复。通过使用稀疏信号的先验信息，我们提出了一种名为 GESPAR 的快速局部搜索方法来恢复稀疏信号。相比于以前的方法，我们的算法不需要矩阵提升，因此适用于大规模问题。通过模拟实验，我们证明 GESPAR 在各种情况下都比现有技术更快更准确。

GESPAR: 稀疏信号高效相位恢复

GESPAR: Efficient Phase Retrieval of Sparse Signals

Although many algorithms have been designed to construct Bayesian network
structures using different approaches and principles, they all employ only two
methods: those based on independence criteria, and those based on a scoring
function and a search procedure (although some methods combine the two). Within
the score+search paradigm, the dominant approach uses local search methods in
the space of directed acyclic graphs (DAGs), where the usual choices for
defining the elementary modifications (local changes) that can be applied are
arc addition, arc deletion, and arc reversal. In this paper, we propose a new
local search method that uses a different search space, and which takes account
of the concept of equivalence between network structures: restricted acyclic
partially directed graphs (RPDAGs). In this way, the number of different
configurations of the search space is reduced, thus improving efficiency.
Moreover, although the final result must necessarily be a local optimum given
the nature of the search method, the topology of the new search space, which
avoids making early decisions about the directions of the arcs, may help to
find better local optima than those obtained by searching in the DAG space.
Detailed results of the evaluation of the proposed search method on several
test problems, including the well-known Alarm Monitoring System, are also
presented.

本文针对贝叶斯网络基于评分函数加搜索过程的局部搜索方法进行了改进，提出了一种利用有限无向结构图进行搜索的新的局部搜索方法，避免了在 DAG 空间中做出早期决策，减少了搜索空间的配置数量，提高了效率，同时在多个测试问题上验证了这种方法的优越性。