We propose an end-to-end approach for answer set programming (ASP) and linear
algebraically compute stable models satisfying given constraints. The idea is
to implement Lin-Zhao's theorem \cite{Lin04} together with constraints directly
in vector spaces as numerical minimization of a cost function constructed from
a matricized normal logic program, loop formulas in Lin-Zhao's theorem and
constraints, thereby no use of symbolic ASP or SAT solvers involved in our
approach. We also propose precomputation that shrinks the program size and
heuristics for loop formulas to reduce computational difficulty. We empirically
test our approach with programming examples including the 3-coloring and
Hamiltonian cycle problems. As our approach is purely numerical and only
contains vector/matrix operations, acceleration by parallel technologies such
as many-cores and GPUs is expected.

我们提出了一种端到端的方法，用于通过线性代数计算满足给定约束条件的稳定模型，并针对循环公式使用预处理和启发式方法来减少计算难度。

朝全流程 ASP 计算迈进

Towards end-to-end ASP computation

We propose a framework that automatically transforms non-scalable GNNs into
precomputation-based GNNs which are efficient and scalable for large-scale
graphs. The advantages of our framework are two-fold; 1) it transforms various
non-scalable GNNs to scale well to large-scale graphs by separating local
feature aggregation from weight learning in their graph convolution, 2) it
efficiently executes precomputation on GPU for large-scale graphs by
decomposing their edges into small disjoint and balanced sets. Through
extensive experiments with large-scale graphs, we demonstrate that the
transformed GNNs run faster in training time than existing GNNs while achieving
competitive accuracy to the state-of-the-art GNNs. Consequently, our
transformation framework provides simple and efficient baselines for future
research on scalable GNNs.

提出一种框架，可自动将不可扩展的 GNNs 转化为基于预计算的 GNNs，从而实现大规模图形的高效可扩展性，通过对大规模图形的广泛实验，我们证明了转换后的 GNNs 在训练时间上比现有的 GNNs 更快，同时达到了与现有 GNNs 的竞争准确性，因此，我们的转换框架为未来研究可扩展的 GNNs 提供了简单和高效的基线。