In recent years, there has been notable interest in investigating
combinatorial optimization (CO) problems by neural-based framework. An emerging
strategy to tackle these challenging problems involves the adoption of graph
neural networks (GNNs) as an alternative to traditional algorithms, a subject
that has attracted considerable attention. Despite the growing popularity of
GNNs and traditional algorithm solvers in the realm of CO, there is limited
research on their integrated use and the correlation between them within an
end-to-end framework. The primary focus of our work is to formulate a more
efficient and precise framework for CO by employing decision-focused learning
on graphs. Additionally, we introduce a decision-focused framework that
utilizes GNNs to address CO problems with auxiliary support. To realize an
end-to-end approach, we have designed two cascaded modules: (a) an unsupervised
trained graph predictive model, and (b) a solver for quadratic binary
unconstrained optimization. Empirical evaluations are conducted on various
classical tasks, including maximum cut, maximum independent set, and minimum
vertex cover. The experimental results on classical CO problems (i.e. MaxCut,
MIS, and MVC) demonstrate the superiority of our method over both the
standalone GNN approach and classical methods.

我们的研究工作的重点是通过决策导向的图学习，在组合优化问题中采用神经网络框架，提出了一个更高效和精确的框架。此外，我们引入了一个决策导向的框架，利用图神经网络解决具有辅助支持的组合优化问题。实验结果表明，我们的方法在经典组合优化问题上优于独立的图神经网络方法和传统方法。

面向决策的图神经网络在组合优化中的应用

Decision-focused Graph Neural Networks for Combinatorial Optimization

In this paper, we present the texture reformer, a fast and universal
neural-based framework for interactive texture transfer with user-specified
guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2)
the simplicity of guidance maps, and 3) the execution efficiency. To address
these challenges, our key idea is to use a novel feed-forward multi-view and
multi-stage synthesis procedure consisting of I) a global view structure
alignment stage, II) a local view texture refinement stage, and III) a holistic
effect enhancement stage to synthesize high-quality results with coherent
structures and fine texture details in a coarse-to-fine fashion. In addition,
we also introduce a novel learning-free view-specific texture reformation
(VSTR) operation with a new semantic map guidance strategy to achieve more
accurate semantic-guided and structure-preserved texture transfer. The
experimental results on a variety of application scenarios demonstrate the
effectiveness and superiority of our framework. And compared with the
state-of-the-art interactive texture transfer algorithms, it not only achieves
higher quality results but, more remarkably, also is 2-5 orders of magnitude
faster. Code is available at this https URL

通过使用全新的前向多视图和多阶段合成过程以及语义地图引导策略，提出了一种快速而通用的交互式纹理转移的神经网络框架，从而在粗到细的方式下合成高质量的结果，同时保留了结构和纹理的细节和连贯性，并且相较于交互式纹理转移算法，不仅能够获得更高质量的结果，而且速度也提高了 2-5 个数量级。