基于数据驱动稀疏学习的化学反应网络模型约简方法
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
摘要：
在本文中，我们提出了一种基于优化的稀疏学习方法来识别化学反应网络中最有影响力的反应集。然后使用这组简化的反应来构建简化的化学反应机制，这与化学相互作用网络建模有关。首先将影响反应的识别问题转化为一个混合整数二次规划问题，然后利用松弛法降低了算法的计算复杂性.稀疏编码方法的定性和定量验证表明，该模型捕捉到重要的网络结构特性与适度的计算负荷。
英文摘要：
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational complexity of our approach. Qualitative and quantitative validation of the sparse encoding approach demonstrates that the model captures important network structural properties with moderate computational load.
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