This article demonstrates the effectiveness of employing a deep learning
model in an optimization pipeline. Specifically, in a generic exact algorithm
for a NP problem, multiple heuristic criteria are usually used to guide the
search of the optimum within the set of all feasible solutions. In this
context, neural networks can be leveraged to rapidly acquire valuable
information, enabling the identification of a more expedient path in this vast
space. So, after the explanation of the tackled traveling salesman problem, the
implemented branch and bound for its classical resolution is described. This
algorithm is then compared with its hybrid version termed "graph convolutional
branch and bound" that integrates the previous branch and bound with a graph
convolutional neural network. The empirical results obtained highlight the
efficacy of this approach, leading to conclusive findings and suggesting
potential directions for future research.

使用深度学习模型在优化流程中的有效性进行了论证，通过将图卷积分支限界与经典的分支限界算法相结合，实现了更快速的解决旅行商问题的路径的识别。

图卷积分支与界

Graph Convolutional Branch and Bound

This paper presents TexRO, a novel method for generating delicate textures of
a known 3D mesh by optimizing its UV texture. The key contributions are
two-fold. We propose an optimal viewpoint selection strategy, that finds the
most miniature set of viewpoints covering all the faces of a mesh. Our
viewpoint selection strategy guarantees the completeness of a generated result.
We propose a recursive optimization pipeline that optimizes a UV texture at
increasing resolutions, with an adaptive denoising method that re-uses existing
textures for new texture generation. Through extensive experimentation, we
demonstrate the superior performance of TexRO in terms of texture quality,
detail preservation, visual consistency, and, notably runtime speed,
outperforming other current methods. The broad applicability of TexRO is
further confirmed through its successful use on diverse 3D models.

该论文介绍了一种名为 TexRO 的新方法，通过优化 UV 纹理来生成已知 3D 网格的精细纹理。TexRO 的关键贡献有两方面：提出了一种最优视点选择策略，通过选择最小的视点集来覆盖所有网格的面；提出了递归优化流程和自适应降噪方法，以增加分辨率优化 UV 纹理，并利用现有纹理生成新的纹理。通过广泛的实验证明了 TexRO 在纹理质量、细节保留、视觉一致性和运行速度等方面的卓越性能，超过了其他当前方法。TexRO 在不同的 3D 模型上的成功应用进一步证实了其广泛适用性。